Characterizing the Agent Grid
Manola and Craig Thompson
and Consulting, Inc.
The agent grid is a proposed construct that
is intended to support the rapid and dynamic configuration and creation
of new functionality from existing software components and systems.
Grids in general and the agent grid in particular are at an early stage
of development so this paper focuses on characterizing grids by examining
their purposes and properties. The paper describes work on existing
grid architectures, some of the properties that an agent grid might provide,
then explores various architectural interpretations of the agent grid.
The paper concludes that there is more work needed to begin to consolidate
the agent grid concept, and to allow agents to better interoperate with
other technologies in forming such grids.
The term grid is increasingly appearing in
computer literature, generally referring to some form of system framework
into which hardware, software, or information resources can be plugged,
and which permits easy configuration and creation of new functionality
from existing resources. The "killer applications" for these grid concepts
include computational challenge problems (e.g., codebreaking) requiring
supercomputing capabilities, universal availability of customized computing
services (e.g., access to one's individual desktop and application suite
anywhere in the world), and global integration of information, computing,
and other resources for various purposes. Several DoD and industry
programs use some form of grid concept. However, such computer-related
"grids" are a relatively new architectural idea, and not very well understood.
Sometimes the term grid is used loosely in describing systems connecting
some collection of distributed resources, while in other cases it is clear
that some more advanced set of capabilities is involved. The grid
concept has begun to be applied to computer systems involving agents,
with agents playing both the roles of enablers and customers of grid capabilities.
However, in this newer context there is even more that is not understood
about the characteristics that such grids might have. The purpose
of this paper (which derives from [Man99c, Tho98b]) is to better characterize
the architectural concept of an agent grid, and describe some issues associated
with defining such grids. The paper attempts to identify some general
characteristics which seem to apply to all sorts of grids, in order to
provide a "big picture" in terms of which agent grid concepts can be better
understood, and then focuses on issues specifically associated with agent
In Section 2, we give examples of several computer-related
grid concepts, in order to provide some examples as a basis for understanding
the grid concept. In Section 3, we describe, as an example of an
agent grid, the grid currently being developed within DARPA's Control of
Agent-Based Systems (CoABS) program. In Section 4, we identify some
general grid characteristics, based on common characteristics of these
various types of grids, and then describe various issues that arise in
attempting to characterize agent grids in particular. In particular,
we look at various design decisions that need to be made in defining an
agent grid, and identify related technologies that should be integrated
in building agent grids. Section 5 summarizes our conclusions and
areas where more work will be needed.
of Grids and Grid-Like Systems
2.1 Computational Grids
The basic concept of a computational grid is defined in [FK99b].
The term grid is used to indicate an analogy with the electrical
power grid. Just as a power grid links sources of electrical power together,
and provides for widespread access to and distribution of that power (with
associated load-balancing and other services), a computational grid is
"a hardware and software infrastructure that provides dependable, consistent,
pervasive, and inexpensive access to high-end computational capabilities".
The concept of a grid as an infrastructure is important because "…a computational
grid is concerned, above all, with large-scale pooling of resources, whether
computer cycles, data, sensors, or people. Such pooling requires significant
hardware infrastructure to achieve the necessary interconnections and software
infrastructure to monitor and control the resulting ensemble". The intent
of a [computational] grid is to provide [computational] services that are
and inexpensive. The term "grid" is only slowly becoming associated
with the concept of creating a giant computational environment out of a
distributed collection of files, databases, computers, and external devices.
The term "metacomputing" is also frequently used.
[FK99b] identifies five major application classes for computational
For application development, a grid must provide
both appropriate programming models and a range of services.
The paper notes that there is currently no consensus on what programming
model is the most appropriate for a grid environment. Models that have
been proposed include:
Distributed supercomputing, in which the grid
is used to aggregate computational resources to address very large problems
that cannot be handled on a single system. Examples include distributed
interactive simulation (e.g., military simulations), and simulation of
physical processes (e.g., stellar dynamics and climate modeling).
High-throughput computing, in which the grid
is used to schedule large numbers of loosely-coupled or independent tasks,
with the goal of putting unused processor cycles to work. Examples include
the use of multiple distributed workstations to solve hard cryptographic
or complex design problems.
On-demand computing, in which the grid is
used to meet short-term requirements for resources that cannot be cost-effectively
or conveniently located locally (i.e., providing the ability to share scarce
resources). The resources may be computation, but may also include software,
data repositories, specialized sensors and other devices, etc. Unlike the
distributed supercomputing applications, these applications are often driven
by cost-performance concerns rather than absolute performance. Particular
challenges in these applications have to do with the dynamic resource requirements,
and the potentially large population of users and resources involved. The
challenges include resource location, scheduling, code management, configuration,
fault-tolerance, security, and payment mechanisms.
Data-intensive computing, in which the grid
is used to synthesize new information from data maintained in geographically-distributed
repositories, digital libraries, and databases. The synthesis process is
often computationally and communication intensive as well. Challenges in
this class of applications are the scheduling and configuration of complex,
high-volume data flows through the network and multiple levels of processing.
Collaborative computing, concerned
primarily with enabling and enhancing human-to-human interactions. Examples
include collaborative design activities, and "virtual worlds". In many
cases, these applications involve providing the distributed participants
with shared access to data and computational resources, in which case these
applications share characteristics with the other application classes.
Services that must be provided include:
low-level techniques such as datagram/stream communication
(UDP, TCP, Multicast)
shared memory/multithreading (e.g., distributed shared
message passing (MPI, PVM)
remote procedure call (DCE)
Relevant technologies come from areas such as distributed
file systems and databases, distributed operating systems (particularly,
such areas as load balancing and process and data migration), parallel
and distributed programming, and network management.
security: authentication, authorization, and
data concepts: memory, files, databases
shared address space
resource management: acquisition, allocation,
accounting and payment
The paper also notes that "computational infrastructure,
like other infrastructures, is fractal, or self-similar at different
scales. We have networks between countries, organizations, clusters, and
computers; between components of a computer, and even within a single computer."
The paper describes systems at the scales of end system, cluster, intranet,
and internet, the basic idea being that these constitute different scales
at which similar computational services should be provided (mimicing those
provided at the smallest scale, in the individual computer). Of course,
it is then necessary to look at how those similar services must be provided
as the scale changes, since different technologies must typically be employed.
[GFLH98] and [FK99b] describe several projects
developing technology for computational grids. A simple example is
(Parallel Virtual Machine, <http://www.epm.ornl.gov/pvm/pvm_home.html>),
a software package that permits a heterogeneous collection of Unix computers
hooked together by a network to be used as a single large parallel computer.
PVM is very portable, and the source code has been compiled on a wide variety
of machines. Related facilities are provided by MPI (Message
Passing Interface) [GLS94], a community-generated standard for message
passing used to interconnect multiple machines. UCLA's Project Appleseed
<http://exodus.physics.ucla.edu/appleseed/appleseed.html> is an example
of how MPI can be used to link together a cluster of computers (Macintoshes
in this case) to provide "a plug and play parallel computer" in support
of numerically-intensive processing.
[GG99] provides an environment in which a collection of workstations, vector
supercomputers, and parallel supercomputers connected by LANs and larger-scale
networks appears to the user as a single very powerful computer.
Legion uses object-oriented design techniques to "simplify the definition,
deployment, application, and long-term evolution of grid components".
The Legion architecture defines a complete object model that includes object
abstractions for computer resources, storage systems, and other object
classes. Inheritance can be used to specialize the behavior of these
objects to support specific requirements. The use of reflection
(the representation of parts of the underlying system as objects that can
be directly operated on to access and change system behavior) is particularly
important in Legion. For example, host objects represent Legion processors.
One or more host objects run on each computing resources included in Legion.
These objects create and manage processes for application-level Legion
objects. Object classes invoke the operations of host objects to
activate their instances on the computing resources that the host objects
represent. Representing computing resources as Legion objects abstracts
the heterogeneity of different host computing platforms, and allows resource
owners to manage and control their resources within the context of the
system. Reflection is also an important technology in providing systemic
properties (sometimes called ilities) such as reliability, survivability,
and security, and quality of service characteristics, in large-scale computer
Globus <http://www-fp.globus.org/> [FK99c]
is developing basic software infrastructure for computations that integrate
geographically distributed computational and information resources. Globus
is based on the assumptions that:
Globus thus focuses on defining a toolkit of low-level
services for security, communication, resource location, resource allocation,
process management, and data access. These services are then used
to implement higher-level services, tools, and programming models.
According to [GFLH98], "Globus has withstood many tests, including a recent
one involving battlefield simulations distributed across more than 30 machines
and representing the independent activity of more than 100,000 tanks, trucks,
and other units."
Grid architectures should provide basic services,
but not prescribe particular programming models or higher-level architectures.
Grid applications require services beyond those provided
by today's commodity technologies.
[FF97a,b,c] discuss the concept of "High-Performance
Commodity Computing", the idea that computational grids should be based
on emerging commodity network computing technologies such as CORBA, DCOM,
and JavaBeans, together with the Web and conventional networking approaches.
The papers discuss a three-tier architecture which integrates these technologies.
This approach is in contrast with the more specialized grid architectures
proposed in Legion and Globus (although these could be integrated to support
lower-tier services). The authors particularly emphasize the importance
of the emerging "Object Web", integrating the Web, distributed objects,
and databases, in the development of computational grid technology.
The focus of much of this work appears to be on
large-scale computing problems, although the technology is clearly not
limited to those applications. Other grid concepts discussed below
extrapolate ideas in distributed supercomputing to more complex applications.
For example, in distributed supercomputing, the paradigmatic application
is often that of a single large computing "job". The program is run,
and a result is produced. A grid is required simply because the job
is too large for a single machine. In other grid concepts, the application
is of a more continuous nature. This means it must be possible for
participants to enter and leave the grid, load distribution is even more
dynamic (because the load and its requirements change more dynamically),
etc. The next subsection describes a new twist on more familiar applications
supported by computational grid concepts.
2.2 Computing Fabrics
Another grid-related "vision" is presented in a series
of articles describing what is referred to as Computing Fabrics <http://www.infomaniacs.com/>.
As described in these articles, the Computing Fabric consists of nodes,
which are packages of processors, memory, and peripherals, linked together
by an interconnection facility. Within the Fabric are regions of
nodes and interconnections that are so tightly coupled that they appear
to be a single node. These are called cells. This tight
coupling is obtained using hardware, software, or both. Cells in
the Fabric are then loosely coupled with each other. The coupling
between cells appears differently from the coupling between the components
of a node. The Fabric as a whole, or each cell in it, can grow or
shrink in a modular fashion, by adding or removing nodes and links.
Nodes from the Fabric surrounding a cell can join that cell, and nodes
within a cell may leave that cell and join the surrounding Fabric.
In addition, cells can divide and merge. Each cell presents the image
of a single system, even though it can consist of many nodes.
The articles give ubiquitous network computing
as an example of an application made possible by the Fabric. The
first aspect of the application is network computing: each
user can access their individual "desktop" (configuration, including all
applications, data, etc.) from anywhere on the network. To this is
added ubiquitous computing, in which processors, displays, and input
devices are everywhere. Users are tracked by sensors, and their location
information is used to direct their applications and data to the appropriate
devices that are located where the user is located. This changes
as the person moves. There is no need for users to explicitly login
to access their computing spaces, they are just "there". The Fabric
helps avoid the need for the universal presence of sufficient computing
power, displays, and input devices necessary to run whatever applications
the user wishes to run locally. In this scenario, processors are
located all over, e.g., throughout buildings ("as populous as wall sockets,
perhaps more so"), and are interconnected by low latency, high bandwidth
connections. When the user is stationary, the user's tasks run on
a local cell, consisting of processors in the general vicinity, which work
together as a single system. If the tasks require it (and they can
be paid for), additional processors can be added (thousands of them, if
necessary); the computing resources are configured as required to
run the software the user wants to run. As the user moves, their
cell moves with them. Processing nodes leave the user's cell as their
distance makes their communications latencies more than some threshold
level, and are replaced by nodes that enter the cell as the user gets near
them. A new generation of wearable processor, display, and input
devices rounds out the picture.
Technically the concept of Computing Fabrics involves
ideas that are somewhat similar to those of the computational grid, but
the application focus is somewhat different. Technologies relevant
to the creation of the Computing Fabric concept include:
The authors note that full exploitation of the Computing
Fabric concept requires the integration of distributed object technologies
and database technologies. For example, technologies such as Microsoft's
and Sun's Jini support code developed using object technology being automatically
distributed using a distributed object infrastructure running atop massively
distributed clusters. Large-scale DBMSs already exploit parallelism
and multi-system clustering. DBMSs need to be further exploded into
interoperable components that can more fully utilize Fabrics. Logical-level
models and views become increasingly important as data and processing are
distributed over the Fabric, and as data is organized on increasingly large
scales. The Web (and XML) will also need to be included, as representing
a large scale distributed data store.
Distributed shared memory architectures
Modularly scalable multiprocessor interconnect facilities
(in addition to networking technologies at scales from LANs to the Internet;
the idea here is that "buses converge with networks")
Distributed operating systems (e.g., SGI's Cellular
Irix distributed Unix)
Distributed object systems supporting mobile objects
Integrations of the above two technologies, as in
the later stages of Microsoft's Millennium <http://www.research.microsoft.com/sn/Millennium/>.
The increasing integration of Java with CORBA, plus additional infrastructure
at lower levels (e.g., to support load management) would provide similar
Jini, and distributed shared object spaces (JavaSpaces,
IBM's T Spaces).
2.3 DoD C4ISR Grid Concepts
The U.S. Department of Defense (DoD) has developed
a number of advanced information system concepts employing the idea of
grids to support advanced Command, Control, Communications, Computers,
Intelligence, Surveillance, and Reconnaissance (C4ISR) capabilities.
The grid concepts used in these systems are very ambitious and powerful,
embodying the idea of being able to integrate not only global computing
and communications resources, but also sensors, weapons, etc., in extremely
flexible, custom-tailored combinations to achieve mission objectives.
For example, the Advanced Battlespace Information System (ABIS)
[ABIS96] concept describes a set of information services, technologies,
and tools to support C4ISR. The ABIS concept
was produced by a task force composed of operational and technical personnel
from all Services, the JCS, and major DoD agencies involved with C4ISR
systems. The foundation of the framework is an information
grid, described in the "Grid Capabilities Working
Group Results" section of [ABIS96] <http://www.dtic.mil/dstp/96_docs/abis/volume5/abis501.htm>).
The C4I For The Warrior (C4IFTW) [J695] concept
sets forth a 21st century vision of a global information infrastructure
referred to as the global grid that will provide virtual connectivity
from anywhere to anywhere instantaneously on warrior demand. This
grid connects commanders, sensors, weapons systems, etc., and is made up
of a web of computer controlled telecommunications grids that transcends
industry, media, government, military, and other nongovernment entities.
The Network-Centric Warfare (NCW) concept [CG98, DC498] develops
these ideas somewhat further. Network-Centric Warfare is described
as a derivative of network-centric computing. Just as network-centric
computing is being exploited to provide competitive advantage in the commercial
business sector, the emerging concepts of Network-Centric Warfare exploit
information superiority to provide a competitive edge in warfare.
Grid concepts are key elements in Network-Centric Warfare.
These information system concepts have in common
the idea of organizing the system into separate functional layers, each
employing a specialized grid. The separate grids defined in the Network-Centric
Warfare concept are representative of this organization:
An example of an existing operational architecture
that employs network-centric operations to increase combat power is the
U.S. Navy's Cooperative Engagement Capability (CEC). CEC networks
the sensors, command and control, and shooters of the Carrier Battle Group's
platforms to develop a sensor grid and an engagement grid. The mission-specific
sensor grid generates a high level of battlespace awareness by fusing data
from multiple sensors, enabling quantum improvements in track accuracy,
continuity, and target identification over standalone sensors. The
CEC engagement grid exploits this awareness by extending the battlespace,
and engaging incoming targets in depth with multiple shooters.
The information grid is an information environment
including communications, processing, information repositories, and value-added
services that provide users with an ability to find information, obtain
processing services, and exchange information. It is a federated,
heterogeneous system-of-systems that provides "dial tone", "web tone",
and "data tone", and provides the infrastructure for network-centric computing
and communications. Voice, data, and video can be transmitted via
point-to-point or direct broadcast. Embedded capabilities for Information
Assurance will prevent intrusive attack and assure commanders that their
information will be valid. Warfighters will be able to connect to this
grid anywhere and at any time, and will be able to craft their own information
environment by selecting the types of services, information, and interfaces
that are appropriate to their missions and styles of operations.
The grid will provide connectivity and information that will adapt to changing
situations and be responsive to the warfighter's need for knowledge.
It will adapt to the constraints imposed by connectivity at the tactical
levels and will be able to organize resources within the global infrastructure
to service the needs of the warfighters. Participants in the grid
may include civil, commercial, and foreign organizations, and Grid functionality
will extend to all types of users in joint and combined operations.
As a result, the grid must cope with the heterogeneity of the commercial
world, and of allies and potential coalition partners. The ABIS description
of the information grid explicitly mentions that intelligent agents will
be included to assist the users in finding and retrieving information,
so that they are not overwhelmed with the massive amount of information
and sources available in the grid.
Sensor grids can be viewed as sets of sensor
peripherals and sensor applications that are installed on the
information grid. The sensor peripherals consist of space-,
air-, ground-, sea-, and cyberspace-based sensors. These sensors
can be based on dedicated sensor platforms, weapons platforms, or deployed
by individual soldiers. The sensor peripherals also include, e.g.,
embedded sensors that track levels of consumables (e.g., fuel or munitions).
The sensor applications consist of the software applications associated
with specific sensor peripherals, as well as the software applications
that enable multi-mode sensor tasking and data fusion. Individual,
custom-tailored sensor grids can be created from the sensor resources available
on the grid. Dynamic sensor tasking, data fusion, and effective distribution
of information over the information grid provide increased battlespace
awareness, and an increased ability to synchronize this battlespace awareness
with military operations.
Engagement grids can be viewed as sets of
peripherals and shooter applications that operate on the information
grid. The shooter peripherals consist of geographically-distributed
air-, land-, sea-, and cyberspace-based "shooters" (weapons systems).
The shooter applications consist of software for command and control
and weapon employment. Some of these shooter applications implement
high-speed automated weapon-target pairing algorithms. These algorithms
can rapidly determine near optimal weapon-target pairings subject to time-varying
constraints, such as number and value of remaining targets, number of remaining
shooter rounds, and the probability of kill of remaining rounds.
The concept is similar to what occurs in automated securities trading,
where the expertise of the trader is embedded in high-speed automated trading
software. As with sensor grids, custom-tailored (e.g., mission-specific)
engagement grids can be created from the resources available on the grid.
The engagement grids exploit the battlespace awareness provided by the
sensor grids to enable new operational capabilities for force employment.
The Information Superiority Chapter of the 1998
Joint Warfighting Science and Technology Plan [DDRE98] describes the composition
of the information, sensor, and engagement grids as forming a C4ISR
grid that supports DoD's concept of Information Superiority:
the "degree of dominance in the information domain that permits the conduct
of operations without effective opposition". The plan also identifies
high-level functional capabilities required for Information Superiority,
which of them are supported by the C4ISR grid, and key technologies the
grid must support, including:
Some DARPA activities also refer to grid concepts.
For example, the Advanced Technology Architecture for Information Superiority
(ATAIS) architecture [BFHH+98] uses "grid" in a number of places and senses
(i.e., "nformation grid, communication grid (in the sense of the Internet),
and a full-scale computational grid).
dynamic allocation of computing resources
fault avoidance and recovery mechanisms
tailored search and retrieval of information
multimode, multilingual interface services
automated mediators and database management system
massive data storage and manipulation
robust, adaptive, automated, context-based information
tools for projecting and visualizing C4ISR grid capabilities
in terms of projected operational needs
2.4 Other Grid and Grid-like
[Tho98b] mentions other examples of grid-like concepts.
Many of these concepts are somewhat high level as compared with those described
in Sections 2.1-2.3.
The electrical power grid. The electrical
power grid has already been mentioned as the analogy on which computational
grids have been based. If viewed globally, it uses wires connected
in some physical topology to carry electricity across the world delivering
power to enable a rich variety of applications that need and will pay for
the power received. At a slightly lower level of abstraction it is
connected physically by wires and also by a collection of standards to
provide a uniform quality of service, though standards vary in different
locales. The power grid is an example of a physical grid.
The Transportation Grid. This is another
physical grid. Transportation grids carry vehicles or materials.
Examples are the national highway system, railroads, ships, planes, and
pipelines. Each of these can be viewed as a subgrid of the Transportation
Grid distinguished by its own infrastructure. The different grids
have different properties (capacities, speeds, connection topologies),
but they interoperate (or federate) at connection or bridge points to,
e.g., allow transfers between planes and trucks.
Data Dissemination Grids - The DARPA Intelligent
Integration of Information (I**3), Battlefield Data Dissemination (BADD)
and Agile Information Control Environment (AICE) programs describe information
architectures for connecting thousands of data sources to thousands of
queriers across global networks. New infrastructure technologies
needed include wrappers, caching, push-pull, and channels.
Geographic Grids - Paper and digital maps
as well as GPS provide the infrastructure for a geo grid, in the sense
that objects of interest are integrated by locating them with respect to
common coordinate systems. The geographic information systems (GIS)
community, including the National Image and Mapping Agency (NIMA), view
such grids as an underpinning for command and control. They are also
important in other domains, such as agriculture and real estate.
Supply Chains, Virtual Enterprises, and Simulation
Architectures - While supply chains and virtual enterprises are not
generally referred to as grids, they could be viewed as federations of
a collection of organizations from the point of view of integrating the
production and delivery of goods and services, the result being that the
community of organizations acts like one large virtual organization or
logistics grid. A concrete example is the DARPA Advanced Logistics
Program (ALP), which is developing a software architecture consisting of
a federation of clusters (agents) that wrap logistics organizations to
rapidly define and support detailed logistics plans in heterogeneous environments.
The Defense Modeling and Simulation Office (DMSO) High Level Architecture
(HLA) similarly provides a federation architecture for connecting together
simulations - the simulations share a common clock and send messages to
each other using a common bus and content format; players can enter or
leave the simulation dynamically.
Social and Cultural Networks - The following
"grids" exist to hold our society together: physical laws, families,
tribes, religion, rules, laws, unions, armies, government, common language,
financial system, writing system, printing systems, home addresses, libraries,
mail system, media, fast food, personal computers, VCR standards, etc.
These grids exist simultaneously and interact (there is no overarching
grid), each involves some notion of something being shared, and a support
infrastructure. Some grids are decentralized (money) and depend on
shared (cultural) assumptions as well as indexes of supply and demand.
These things aren't very often referred to as "grids", but are certainly
sometimes referred to as "networks" (e.g., you "network" among your acquaintances),
which emphasizes the relationship between the concepts of "grid" and "network".
DARPA ISO's Control of Agent-Based Systems (CoABS) program is exploring
a new kind of grid called the agent grid. Here, the grid concept
is applied to agents, since a key "vision" of the program is the concept
of a grid as a means of making agent-based systems more interoperable and
pervasive. The agent grid can be described by requirements at two
levels: application and functional.
3. The CoABS
3.1 Application Level Requirements
Application level requirements describe benefits that applications receive
by using a grid. At the application level, the agent grid is defined
as an enabling technology needed for command and control as a main ingredient
in supporting DoD's Information Superiority concept. The notion of
the agent grid is important in DoD as one of the architectural constructs
that might make a variety of command and control systems easier to build,
maintain, scale, evolve, adapt, and survive. These systems are characterized
From the DoD perspective, agent technology is expected to help:
multi-year lifetimes and evolving and changing requirements
more components than any group of designers can design or even understand
design by groups that do not know about each other
adaptable and scalable to large or small sizes
systems management without explicitly monitoring all components all the
time (there are too many components to do this)
Proposed characteristics of the agent grid are described in [CoA98],
reduce the 60% of time in command and control systems spent manipulating
stovepipe systems, and incrementally replace stovepipes with more reliable,
scalable, survivable, evolvable, adaptable systems
make it much easier to snap together future systems to meet flexible needs
in uncertain environments
connect the $40B worth of DoD equipment that currently only interoperates
with one or two other components, permitting better situation assessment,
resource sharing, and logistics support
reduce system complexity
help solve data blizzard and information starvation problems
Further discussions of CoABS grid ideas are provided in [Ket98,99;
Tho98b; Pis98b]. Some additional characteristics expected from an
agent grid are:
It is a distributed electronic environment.
When agents enter the grid, they receive status information, and their
activities are modified and integrated with other activities in the grid.
E.g., "When your personal assistant connects to the grid, it tells the
grid where you are, what you are doing, how your resources are configured,
which supplies you need, and so on."
It encompasses both computer and other resources, and allows them to be
used by other agents. "Your forces might be dynamically reassigned to a
new plan; your computer equipment, currently underutilized, might briefly
be recruited to run a meteorological simulation by a load-balancing agent;
due to your personal expertise in Arabic, you might receive documents to
translate, or perhaps not if the grid realizes that your time is already
claimed by other responsibilities."
"All resources - mental and material, human and non-human, permanent and
ephemeral - are balanced by the grid. Goals are reconciled by agents in
the grid and priorities are established."
"Whatever kind of agent you are [including both humans and pieces of equipment]
when you enter the grid, you immediately become part of a larger, coherent
system. And when you leave the grid, to travel, sleep, or shut out the
hubbub for a while, the grid prepares for your return by generating status
reports, reading and summarizing your mail, planning how to use your resources,
and so on."
It provides the ability to easily connect heterogeneous components together
to form coherent aggregates.
humans and agents can connect to the grid anytime from anywhere and get
the information and capability they need
it scales to millions of agents so agents are pervasive and information
and computation is not restricted to machine or organization boundaries
it provides/supports agents that act for users and interact with them,
wrap data sources, filter information, plan and execute tasks, and coordinate
with other agents
it enables teams led by humans and staffed by agents
it supports dynamic configuration, reorganization, and adaptability of
associated resources (software components, applications, data, agents,
etc.) to solve a variety of C4ISR problems
3.2 Functional Requirements
From a functional point of view, the CoABS grid application-level characteristics
suggest that the agent grid knows not only about agents, but about their
computational requirements (e.g., how they can be broken up into processes,
so they can be distributed across multiple computers), and about available
computational (and other) resources. Hence, the agent grid appears
to incorporate both the concepts of Computational Grid and the Computing
Fabric, in the sense of providing a unified, heterogeneous distributed
computing environment in which computing resources are seamlessly linked.
In addition, the agent grid extends the idea "upward" to agents.
These agents play the roles of applications whose computations can be distributed
within this distributed computing environment, resources that can be used
within this environment, and infrastructure components of this environment.
At the same time, there appears to be an interface between the computational
and agent layers, so that at least some agents, e.g., those that do load
balancing, can operate on the computing level grid. Furthermore,
since the agent grid is defined as encompassing other resources (e.g.,
forces), agent grid ideas are also consistent with aspects of the DoD grid
concepts. For example, agents are explicitly mentioned as components
in parts of these DoD grid concepts, and agents could well be the
implementations of choice for many of the applications incorporated in
these grids. Agents could also serve as wrappers of resources (and
mediators between them) in these architectures. The assumption is
that "agent technology" (viewed broadly) provides mechanisms for late binding,
reconfiguration, load balancing optimizations, achieving and maintaining
systemic properties like survivability and scalability, and coordinating
teams and organizations.
Building the grid suggested by these requirements would appear to involve
all the computational grid issues of system management (and associated
metadata), distributed computation and load balancing, mobile code, security,
etc., as well as the "agent-level" versions of those issues (issues which,
e.g., reflect the semantics of the entities on whose behalf the agents
are functioning, or the resources which the agents wrap). This requires
a way of describing resources and capabilities, and resource requirements
and tasks, and a way to map between them, at both agent and computational
levels. This grid also apparently involves the need for a way of defining
higher-level goals, i.e., a way to define the goals of the grid itself,
that are optimized by the load-balancing, etc. that is going on. These
goals are presumably at a higher-level than those of individual agents
(although these might also be characterized as the goals of higher-level
agents, or agents of higher authority, rather than goals of the grid per
All this suggests that one view of the CoABS grid could be that of a
framework which combines the capabilities of a computational grid and those
of an Agent System architecture. This would mean that it would need
to incorporate services provided by agent system architectures, such as
communications, lifecycle services, ACL and knowledge representation, transactions,
metering and charging, matchmaking/facilitating/negotiation, security,
persistence facilities, system management, and mobility (see, e.g., [Pis98a;
KT98; Paz98a,b; Tho98a]). The result would resemble a form of "agentized"
Object Services Architecture (OSA), that is, an agent bus architecture
similar to the OMG Object Management Architecture. This in turn raises
a number of issues, such as:
At the same time, other views of an agent grid are also possible.
For example, we might note that there are many individual agent systems
(and many notions of agent) today consisting of a collection of agents
and an agent biosphere (shared support services and resources). Hence,
we might argue that the grid should be something that helps us connect
these together so they will interoperate, possibly allowing agents to leave
one system/biosphere and go to another, or leave one grid and go to another.
For example, an agent grid might be a federation of agent systems which
allows some sharing and interoperation (but how much), or a meta agent
system that generalizes agent systems.
Is an OSA a good GSA (Grid Services Architecture)? a good start toward
What are the characteristics of a good grid service?
Do object services in an OSA have these characteristics "out of the box"?
Is a GSA component necessarily expected to be "smarter" compared to an
OSA component? why, and in what ways?
In addition, the agent grid is likely to depend not only on computational
grids, but also on other "lower-level" grids or similar unifying technology
such as Web technology, distributed object systems, etc. The CoABS program
assumes that these other kinds of technology (distributed objects, simulation,
network management, ...) are useful and will be needed, in addition to
agent technology. Hence, whatever we mean by agent technology, it
must co-exist with other useful and already pervasive technology.
This suggests the view that the agent grid might be considered as, in some
sense, a technology layer that enables other grids. That this might
be the case can be seen in the grids described earlier - in each case we
can imagine agent technology adding value to these grids. If that
is so, then there are several challenges including:
understanding what constitutes the agent technology grid (as an abstraction
understanding how the agent technology grid complements related technology
demonstrating that higher level grids are enabled by the agent technology
3.3 Initial Progress
At this point, work on the DARPA CoABS project focused specifically on
developing grid system ideas and implementations includes:
The following section describes various ways of viewing what an agent grid
is, and describe the associated issues that must be addressed in determining
the characteristics of an agent grid.
[Paz98d] examines Sun's Jini as a potential grid infrastructure component.
Potential advantages are Java run-anywhere portability, possible Jini pervasiveness,
source code availability (with license), and availability of a starter
kit of services. Initial examples show Jini running in embedded systems
like hotel rooms, TVs, printers, etc. so there is a presumption that Jini
frameworks can be used to model complex systems .
[Pis98a] focuses on the namespace and trader aspects of a global grid.
It mainly views the grid as a namespace and registry that must scale.
[Ket98] takes a use case view of the grid and generates a grid abstract
machine that takes the view of the grid as similar to an agent system and
consisting of a backplane registry of events that describe agent, service,
and resource operations and status. The paper is insightful in that
it captures the system view of a grid (sense B) while trying to make minimal
assumptions. It does not commit to any control tradeoff between agents
and the grid and so is underspecified (by design). By itself the
paper does not identify or answer many of the grid issues raised later
in this paper, but it provides a framework for doing so more precisely.
[Ket99] considers the entire grid vision, DoD motivations, the architecture,
and some architectural issues. "The Grid is not meant to provide
a uniform agent architecture that all components must adhere to, but rather
a bridge between agent (and other component) architectures, allowing interoperability
across these architectures but not replacing all of the services provided
by these infrastructures." The paper also contains a description
of the grid as a particular collection of incrementally evolving implemented
Grid services are federated using Jini to support registered agent-based
communities across the Internet.
Access Framework - grid access mechanism for message handling and ACL translation
Directory - white + yellow pages manager
Logging - message log manager
Visualization - grid activity and status manager
Brokerage - recruitment and mediation manager
Translation - ACL translation to/from KQML and FIPA ACL (TBD)
[TBPV99] does not describe a grid as a single system but rather as a collection
of separately useful component subsystems which can interoperate.
One of the subsystems is a WebTrader that uses Web search services to index
XML service advertisements contained in Web documents. Using this
approach the WebTrader inherits industrial strength, scalability and pervasiveness
to support trading, an assumed grid service. Another subsystem is
Tao, an agent system that uses email to transport FIPA ACL messages encoded
in XML between agents. Like WebTrader, Tao inherits advantages of an existing
infrastructure (e.g., support for disconnected operation, firewalls, and
security) to provide a scalable and potentially pervasive agent communication
bus. This work takes a componentized view of grids - rather than
assuming a grid is a fixed collection or minimal set of infrastructure
capabilities, this view assumes that gridness is the goal, and individual
components can be viewed as providing improved grid infrastructure capabilities.
From the examples of grids in earlier sections, we
can begin to identify aspects, properties, and characteristics of grids
to see what would be desirable in an agent grid (since we get to define
that term, and in possibly multiple ways). These observations might
begin to be the basis for architectural design principles and patterns
for agent-based grids. At very least they add to our vocabulary about
grids, creating terms for the "grid ontology" which can be mined for potential
requirements and use cases to consider in defining the grid architecture.
In this section, we first describe some problems with defining the concept
of a "grid". We then attempt to synthesize aspects of the grid concepts
already described, in order to identify common characteristics of grids.
We then focus on the specific characteristics of agent grids.
in Defining the Agent Grid
4.1 The Problem of Defining
"Agent" and "Grid"
Attempts to come up with a precise definition of
"grid" run into difficulties similar to those found in trying to come up
with a precise definition of "agent" (similar definitional difficulties
have surrounded the word "object", although these have been to some extent
reduced by its operational definition in various object systems).
Since we are trying to characterize the agent grid, we need to consider
these difficulties in defining both terms.
[Bra97b] observes that attempts to define the
term "agent" have taken two approaches: ascription and description.
Definition by ascription recognizes the fact that, while there is
often little commonality among the details of various "agent" concepts,
they all have a "family resemblance". This leads to the idea that
"agent-ness is in the eye of the beholder". In other words, definition
by ascription says that agent-ness "cannot ultimately be characterized
by listing a collection of attributes, but rather consists fundamentally
as an attribution on the part of some person" [VV95]. As [Bra97b]
notes, "This insight helps us understand why coming up with a once-and-for-all
definition of agenthood is so difficult: one person's 'intelligent
agent' is another person's 'smart object'; and today's 'smart object'
is tomorrow's 'dumb program'."
The problem with ascription is that it allows
practically anything to be described as an agent, making communication
about agent concepts difficult among people who do not share the same point
of view. A useful "filter" for using "agent" to describe a piece
of software is that it should be useful to do so; that is, calling
something an agent should in some useful sense distinguish it from concepts
we already understand. For example, [Bra97b] quotes [Sho93] as observing:
"It is perfectly coherent to treat a
light switch as a (very cooperative) agent with the capability of transmitting
current at will, who invariably transmits current when it believes that
we want it transmitted and not otherwise; flicking the switch is
simply our way of communicating our desires. However, while this
is a coherent view, it does not buy us anything, since we essentially understand
the mechanism sufficiently to have a simpler, mechanistic description of
A descriptive definition of an agent, on the
other hand, typically involves a set of attributes, which a given agent
might have to a greater or lesser extent. Numerous sets of such attributes
exist [Bra97b], and there is much discussion about which attributes best
characterize agents. In the course of the next few years we must
tease these (possibly orthogonal) attributes apart and understand what
each technology is adding to the picture, especially if we want a large
body of industry and DoD to adopt this next generation technology.
For the purposes of this paper, we can use as
a working assumption that agents are (some of):
Moreover, we assume that agents are objects
or components, in the sense that agents have identity (you can tell
one agent from another), they have their own state and behavior (distinct
from that of other agents), and they have interfaces by/through which they
communicate with each other and with "other things". For example, [Bra97b]
refers to agents as "objects with an attitude" in this sense.
Here, we are using objects as a generic modeling or abstraction mechanism,
independently of whether agents are implemented as objects (using
object-oriented programming techniques). Object interfaces can encapsulate
"smart things", e.g., more or less smart agents, and human beings;
for example, "firstname.lastname@example.org" is the identifier of an interface to which
messages can be sent. Object interfaces can also encapsulate "dumb
things", e.g., conventional software objects, or dogs ("on the Internet,
no one knows you're a dog"). In some cases the messaging protocols
between these various kinds of objects will be relatively simple (e.g.,
conventional object RPC between distributed software objects, or commands
sent to hardware), while in other cases they will be more complicated (agent
communication language (ACL) sent between agents, or the email flow between
people); however, similar abstraction principles can apply to objects
at all levels.
autonomous - agents are proactive, goal directed
and act on their own performing tasks on your behalf
adaptive - agents dynamically adapt to and learn
about their environment. They are adaptive to uncertainty and change.
mobile - agents move to where they are needed
cooperative - agents coordinate and negotiate to
achieve common goals. They are self-organizing and can delegate.
interactive - agents interoperate with humans, other,
legacy systems, and information sources
social - they work together in communities, may have
One way to unify the ascriptive and descriptive
view of agents is to view the maximal agent as potentially having all behaviors
found in the agent attributes list, and that degenerate forms of agents
are those containing fewer than all properties. In this view, objects
are agents without these extra agent attributes. This helps explain
how agents might literally be "objects with an attitude."
A similar situation regarding ascriptive and descriptive
definitions exists in attempting to precisely define "grid" and, in particular,
"agent grid". We can get a general idea of what "gridness" is from
the "family resemblance" of the grid examples presented earlier, and try
to apply that general idea to the concept of an "agent grid". Sets
of grid attributes which could be used in a descriptive definition
of a grid are presented in [FK99a]. In addition, if we were to consider
an agent grid as a generalization of an agent system architecture, the
list of grid services could be used as descriptive attributes of agent
grids, together with sets of attributes given in [HS98]. However,
whether an agent grid is a form of agent system architecture is precisely
one of the definitional issues that must be considered.
The definitions of "grid" found in dictionaries generally imply some
concept of a "network" or "mesh". This is certainly the generic idea
of a grid. Many things can be referred to as "grids" in this simple
sense, including, in the context of computer systems, the Internet, the
Web, or the objects in a CORBA-based distributed object system (which form
an interconnected network by virtue of the references the objects have
to each other). However, the grid concepts described earlier imply
additional requirements, a stronger cohesiveness. If we are going
to use the term "grid" in a computer context, the example of "mis-ascription"
cited above becomes relevant: in the same sense that it buys us nothing
to refer to a light switch as an "agent", it buys us nothing to refer to
the Web as a "grid", even if it might be technically accurate to do so.
In other words, if we are going to use a new term such as "grid" to describe
particular computer-based systems, it will be helpful to explicitly identify
the properties we want to associate with those systems that distinguish
them from computer-based systems we are already familiar with (such as
the Internet, the Web, distributed object systems, etc.), and for which
we already have other names. Things get even more complex when agents
are included, since we understand less about agents at this point than
about some of these other technologies.
In the sections that follow, we describe some
basic ideas for use in characterizing the concept of an agent grid.
We first discuss some general attributes that seem to apply to computer-related
grids in general. We then discuss agent grids in particular.
4.2 General Characteristics of
By looking at the "family resemblance" of the grid
concepts, we can say that a grid is fundamentally a mechanism or concept
for integrating or sharing physical or logical things which can
be considered as a single unit. In considering an integrating mechanism,
it is useful to focus on:
We can think of the things (resources) to be integrated
in a computer-related grid as (in a rough order of increasing "semantic
what things are being integrated
what integrating those things means
Integrating these things involves:
computation, as in the computational grids of Section
data and information, as in the "information grid"
of Section 2.3
agents ("smarter" software), as in the CoABS grid
people (e.g., as users of these systems)
These, in turn, involve a number of more detailed,
but still general, capabilities, including:
the ability to link resources with/into the grid
via some kind of interconnection mechanism (and where the interconnection
mechanism is itself considered as a collection of resources); this
is the basic "network" characteristic of any grid
the ability for any grid participant to use any of
these resources ("local" or "non-local") to perform some task
the ability to compose grid resources to form
new combined resources that can be used in the same way as the individual
resources (including treating the entire grid as a resource if necessary)
A number of observations can be made in connection
with the points made so far:
the ability for the grid to maintain state information
about itself - this involves static metadata (e.g., database schemas) for
describing available resources, dynamic metadata for describing the current
status of resources (for managing resource usage), as well as any state
information that defines the resources themselves (e.g., program code,
database data); moreover, as a general rule, the more "grid-like"
a system is, the richer this metadata and other descriptive information
the ability for the grid to discover the existence
of new resources as they connect to the grid (and add their resource descriptions
to its state)--this involves more than simply white/yellow pages services
for recording the existence of resources, but also includes well-defined
mechanisms, such as those in Jini
<http://java.sun.com/products/jini/>, for resources to announce
their arrival as grid members, and for grid services to take notice.
the ability for the grid to match resources with
requests (e.g., trader/broker services), and use the resources to satisfy
the ability for the grid to form resource aggregates
(groupings of resources) that match "aggregate" requests--this is more
straightforward if requests and resources are always defined using the
same "units" (e.g., computer cycle or memory requirements) than at higher
levels, where requirements involve very different types of resources, or
the need to perform human-level tasks.
the ability for the grid to monitor (and hopefully
optimize) the use of resources being provided--this includes such things
as load balancing, query optimization, or maintenance of quality of service,
and typically requires the use of information at several semantic levels
and mappings between them (e.g., to manage quality of service of a video
presentation in terms of adjusting network bandwidth [Man99b]).
the ability for the grid to deal with resources entering
or leaving the grid (or temporarily becoming unavailable).
the ability for the grid to do these things autonomously
(and automatically) to a significant extent, i.e., without a great deal
of manual intervention. Also, the ability of the grid to provide
a system management interface so humans or agents can conveniently monitor
it and possibly manually intervene to control it.
"Gridness" can be thought of as a continuum.
At one end, there is the simple interconnection or network of resources,
as in the dictionary definitions of "grid". We can think of such
a network as a "loose grid", if we must use the term "grid" for these networks
at all. At the other end, there are the systems that allow the interconnected
resources to function as well-integrated units, as in the grid concepts
described in Section 2 (particularly the DoD concepts described in Section
2.3, and the CoABS grid as described in [CoA98]). We can refer to
these systems, which exhibit the characteristics described above (and possibly
other defining characteristics not yet identified) in the strongest sense,
as "true grids". This "true grid" endpoint of the "gridness" continuum
is, of course, an arbitrary designation. Systems will exist at various
points along the continuum, becoming "stronger grids" as they exhibit these
"gridness" characteristics to a greater extent.
A key aspect of grids is composition of resources
in a sense that goes beyond simply interconnecting them (although interconnection
is clearly required). The compositional facilities provided by grids
can apply at all levels, including hardware/computational power, data and
software (software including both individual components and services, and
composition including such things as interoperability and formation of
aggregates), and agents and people (e.g., formation of communities and
teams). These compositions of resources are applied to "composed
tasks" (i.e., tasks that go beyond separately accessing or invoking the
individual resources): in the transportation grid, the composed task
is generically "provide access to resources"; in the power grid,
it's "provide power"; in computers, it's presumably "provide computation"
(or, more abstractly, "perform service/task"). At the agent/human
level, the tasks are suitably abstract (e.g., as "translate document" is
enabled by the CoABS Grid knowing that there's a connected person who understands
Arabic). Ideally, we want these compositions to exhibit a fractal
property or, looking at them the other way around, we want composition
to exhibit a closure property. This means that the resource
compositions should have characteristics that are similar to those of individual
resources at the same level of abstraction, so that we can treat the compositions
as resources themselves. For example, the computational grid seamlessly
forms a large virtual computer from individual computers in a network,
forming something that looks like yet another computer (which itself could
be further aggregated). Similarly, relational database theory emphasizes
the idea that operations on data such as joins should exhibit a closure
property, permitting newly formed aggregates of data to be operated on
in the same way as the pieces from which they were formed. A similar
idea can apply to agents. It should be possible to form teams or
communities of agents that are interacted with as if they were single agents,
with the group transparently dividing up any resulting work that has to
be done. Grids also tend to emphasize the dynamic aspects
of composition, i.e., that it should be possible to easily form compositions
of resources, then break them up when the resources are no longer needed,
for recomposition elsewhere.
Since grids tend to be thought of as "units",
grids tend to involve some level of unified (but not necessarily centralized)
management or government. The government consists of rules, policies,
and mechanisms. Grid government might come in many forms (central
authority, de-centralized, democratic, etc.). An interesting grid
necessarily implies a certain amount of autonomous behavior of units of
the grid, in some cases to the extent that the grid's properties can be
"emergent" being defined in as "stable macroscopic patterns arising from
the local interaction of agents." Part of this government is the
economy, both because the grid's activities and use of the
grid generally costs something, and because of the need to allocate resources.
In considering grid government, care is needed to match the level of abstraction
of the management with the level of abstraction of the grid. For
example, the Internet has a certain amount of load management at the network
level, but this does not make it a computational grid, even though it does
connect numerous computers. A level of management at the computational
level would be required for that.
Partly as a corollary of a grid being both a unit,
and having compositional properties, there may be more than one grid.
For example, there can be local and global grids, or a hierarchy of grids
(not just global and local), and larger grids can be constructed be composing
smaller (perhaps local) grids. It may be possible for local grids
to operate independently from larger grids of which they may temporarily
be a part. Local grids can provide heterogeneous enclaves where
different standards, policies, or systemic properties (adaptability, scalability,
security, reliability, etc.) hold. This also implies that a grid
has a boundary.
Whether the composition of resources involves
the resources depends on the kind of grid and its applications (there is
invariably movement of some sort, but not necessarily of the resources).
For example, the composition of resources in a transportation grid necessarily
involves moving those resources from where they are to where they are needed.
In a computational grid, the resources are generically "computational capacity".
In conventional computer networks, the capacity itself doesn't move, instead,
the load is moved. However, specific groupings of capacity
("virtual capacity") can seem to move as sharing arrangements and
interconnections are set up and torn down (as in the case of the Computing
Fabric of Section 2.2). Data is moved in a computer network in the
same way that resources are moved on a transportation grid. In the
case of distributed object systems, there can be either movement of load
alone (e.g., in CORBA systems, where objects are static, messages representing
load are sent to them, and messages representing results are returned),
movement of resources (in the case of Java objects), or both (e.g., even
in a Java-based network, some services, or special purpose devices such
as sensors, may not be able to move). Similar considerations apply
to agent systems.
Grids involve participants that provide
to the grid as well as those that are benefited or enabled by the grid
(grid users). There is a great deal of asymmetry in some grid-related
technologies that sometimes must be dealt with in order to build "true
grids" from these technologies. For example, it is straightforward
to think of connecting personal computers to the Internet in order to access
information. It is less straightforward to think of these personal
computers as being part of the Internet in the sense of having their file
systems and computational facilities fully integrated with the Internet
in order to form a computational grid in the sense of Section 2.1.
To do this, additional technical issues must be addressed (e.g., security).
From another point of view, it is generally more straightforward to integrate
data than it is computational capabilities. Typically this is because (a)
the interfaces (for others to gain access to attached computing resources)
are not as well developed as they are for data, and (b) the mechanisms
for effectively using the added computation are not as well developed either
(e.g., in a local network it may be possible to run an application located
on someone else's machine, but it is not as easy to distribute a computation
over several machines).
The relationship between a grid in a "loose" sense
and a grid in the stronger sense of Section 2 is generally that the "loose"
grid is or can be used as part of an organization that constitutes a "true
grid". Finding the actual grid may sometimes require considering
a wider context, or adding additional technology. For example,
the transport grid (or a subset, like "the railroad grid") may be viewed
as just the network of transport connections and the points connected.
However, this grid was created in the context of higher-level desires by
people to move/share resources (food and other goods). It is the
unification of the transport links, together with the higher-level control
mechanisms (and to some extent the economic system that provides the "tasking")
that creates a grid in the stronger sense. The Internet is another
example. At one level, the Internet may be thought of as a loose form of
grid, because it provides network connectivity among multiple computers.
However, considering it a grid in a stronger sense requires additional
technology. For example, [ABIS96] notes that while the Internet might
be a model for the ABIS information grid, it lacks attributes such as security,
and resource allocation based on (mission) priority, needed to support
their idea of a grid. Considering a wider context can also identify
relationships between the Internet and a stronger grid concept. For
example, via Internet email, it is possible for people to organize collaborative
efforts, integrating the activities of widely-scattered people. This does
not mean that the Internet, or Internet email, by itself, constitutes a
grid. However, considering the connected people as part of the "system"
enables that system to be thought of more realistically as a grid, with
the Internet as a part, and with higher-level organizational strategy and
goals being provided by the people involved. Similarly, distributed
computer networks are at the heart of the computational grids described
in Section 2.1, but additional mechanisms must be added to those networks
in order to form grids in the stronger sense. Expanding the context can
help us see both the grid that was intended, and also what additional components
and mechanisms would be necessary to form a "true grid". This suggests
that we might want to look at technologies, such as the Web and distributed
object systems, that clearly exhibit certain characteristics that we associate
with grids. However, we want to look at them not as grids in the
fullest sense, but as "proto-grids", and then look carefully for the additional
technologies that could be added to them to create grids in the stronger
sense, as a way of pinning down what a "true grid" really is. In
addition, we want to look at how a grid at one level may be enabled (implemented)
on top of a grid at another level (as, e.g., a supply chain grid might
be implemented using Web connectivity and/or by email, fax, snail mail,
...), and at the mappings and bridges/adapters required between them.
The example of viewing email plus people and organizational
goals as a grid raises an additional openness issue. Here the grid
does not have closed system boundaries, and its enabling mechanisms (email)
might be used for many purposes, not just to further organizational goals.
So (open) grid enabling technology may be as important to identify as closed
"true grid" technology. A corollary to grid openness is that a "true
grid" can coexist with loose grids. For instance, a computer can
run conventional programs and, at the same time, participate in a true
grid that shares some of its resources among a community.
Finally, as stated in the final bullet above,
"gridness" seems to imply that the system is "aware of itself" to a certain
extent, and has the ability to carry out its tasks "itself", without a
great deal of manual intervention. For example, any interconnected
group of distributed computers could be used as a much larger "virtual
computer" by employing programmers to cope with all of the distributed
programming and other problems necessary to use these resources in specific
applications. That does not mean that this set of distributed computers
by itself is a grid. What differentiates a computational grid is
the fact that the grid itself provides services over and above the computers
and network to help support the "virtual computer" abstraction (possibly
to a greater or lesser extent), and alleviate at least some of the detailed
programming that would otherwise be necessary. Similar comments apply
to grids at other levels.
4.3 Viewing the Agent Grid from
We now turn to the agent grid as a special case of
the general idea of a grid. In attempting to characterize the
"agent grid" more precisely, we need to recognize that the term can be
used to refer to different things, and convey different perspectives (although
these perspectives are related). Each of these perspectives involves
resolving different issues about the definition in order to pin it down
4.3.1 Viewing the Agent Grid
as a Collection of Agent-related Mechanisms and Protocols
One view of the agent grid (sense A) is that
it refers to a collection of agent-related mechanisms, constructs (interfaces,
protocols, ...), and universal assumptions that augment object and
other technology to increase the ability to create dynamically composable
systems. Many of these mechanisms can be independently
studied and could be standardized so that communities that used these standard
mechanisms could more easily develop interoperable "agent-based" capabilities.
example, a thesis of the DARPA CoABS program is that agent technology will
provide some missing ingredients in an evolving list of architectural mechanisms
that will make software composition much easier in the future. Examples
of these agent-related mechanisms would (somewhat redundantly) include:
trading, matchmaking, facilitating, advertising, negotiating, brokering,
yellow pages, constraints, rules, inference, planning, schedulers, control
algorithms, ontologies, agent communication languages, agent system frameworks,
conversational protocols, mobility frameworks, models of teams, models
of markets, models of trust, user models, learning algorithms, and models
However, while these mechanisms may be "agent-related",
many of them have been used in or derive from non-agent systems (e.g.,
such systems have employed trading and brokering; the limited forms
of some "ontologies" are roughly equivalent to other metadata mechanisms
such as database schemas and Web metadata mechanisms). Moreover,
there is a great deal of "not-strictly-agent-related" technology that is
also useful in building composable software systems or information access
frameworks, such as component and object distribution and mobility frameworks,
messaging services, event monitoring, catalog and directory services, publish
and subscribe mechanisms, security mechanisms, transactions, persistence,
query facilities, load balancing, etc.
Moreover, within this general view there are several
subviews. On the one hand, the agent grid may simply be a name
for an (unstructured) agent technology layer consisting of mechanisms and
services (sense A1). Another subview is that the agent grid
is a specific construct or mechanism within that layer for making services
and resources available (sense A2) (in which case it belongs in
the set of agent technologies above). Within this view, the grid
may have the narrow purpose of just being a registry mechanism (sense
A2a) for tracking which agents, services, and resources are assigned
to an agent system and monitoring key events associated with these.
Alternatively, the grid may be a backplane or organizing framework itself
into which all the agent and non-agent services and mechanisms plug (sense
A2b , which is closely related to sense B below). In this
view, the agent grid refers not only to a collection of technologies, but
also to a particular framework or organization of the technologies, similar
to the way OMG's OMA organizes object technologies.
Issues related to these views of the grid are:
what new technologies and mechanisms can we identify
and add to the agent technology list. Can we more precisely define
the ones we have already? Can we get industry standards groups to
adopt these specifications? Can we influence COTS vendors to supply
how do we avoid reinventing non-agent technologies
and mechanisms? how do we insure that the agent mechanisms will gracefully
interoperate with these useful non-agent mechanisms (since agent systems
will not do everything)? Can we stand on the shoulders and not the
toes of potentially competing technologies, for instance,
the meta computing grid and DARPA programs like Quorum
define non-agent mechanisms for distributing computation and organizing
the distributed object community provides ways of
modeling and distributing applications and provides collections of middleware
the distributed simulation community provides ways
to federate collections of simulations with a shared transport, data model,
and notion of time.
how do we insure that we do not simply supply toolboxes
with mechanisms (glue) in them, but also standard construction techniques
for putting the parts together in creating real systems? What sort of (open)
frameworks can be used to connect all the parts (agent and non-agent) as
well as future technologies?
4.3.2 Viewing the Agent Grid
as a Composition or Federation of Agent Systems
Another view of the agent grid (sense B) is
that it is a kind of super or meta agent system for connecting agents
and agent systems. This system could be built using the standard
mechanisms (of sense A) as well as existing non-agent technology (e.g.,
middleware). The benefit of this view is to show how the agent mechanisms
discussed above make it easier to construct and structure agent systems-of-systems,
using grid-aware protocols, resources, and services as well as foreign
wrapped agent systems and also legacy non-agent systems. In this
role, the grid is responsible for providing services and allocating resources
among its members. This is equivalent to sense A2b above, that the
grid becomes a form of framework.
Among the issues relevant to this view of the grid is grid government.
is a tradeoff between individual agent autonomy and (logically) centralized
control (or at least control external to an agent). The grid is a
balancing act in providing services and resources. How much does
the grid have to understand about the tasks of the agents? To what
extent does the grid make these decisions or is it just a mechanism.
One could define an empowered active grid as the locus of control making
the key decisions of whether agents can have resources and which agents
have priority - an almost socialist scheme in which the grid has the power
to provide resources, rewards and benefits and is empowered to assign or
remove tasks from agents. Agents only have to report status to the
grid and receive tasks and that is all. At another extreme, the passive
grid just supplies mechanisms for registration but the agents provide the
control. The mechanism might now be market driven with some notion
of cost and market conditions as the arbiter of decision-making with agents
making the decisions. In between, both the grid and the agents in it share
control and either divide control (e.g., so the grid handles low level
issues like mobility for load balancing, replication for fault tolerance,
but not semantic control issues), or agents and the grid must negotiate
for control with each other. In this latter variant, by a symmetry
argument, the grid must effectively be an agent in so far as it itself
negotiates with agents. If the grid only understands some kind of
currency (electricity, gridbucks) then it may not have to understand much
about the agents registered with the grid. If it must be able to
reason about different agents' tasks (can it see their state, or must it
ask?), then the grid must have authority and control over agents to make
some decisions or task them, again acting like an agent.
Another dimension of the grid as a system view
involves the architecture of the grid. One architecture would define
a flat (global) grid (a backplane) where the grid supplies services, resources
and optimization to a very large collection of agents and/or agent systems.
In a variant of this architecture, agents that are currently not registered
with any agent system may or must be registered with the grid so they can
be found. This makes the grid a kind of agent system itself but one
that manages homeless agents. In another variant grid architecture,
the flat (logical) grid is really a federated composition of a large number
of smaller grids or agent systems. This permits some local control
and local differences within each such grid domain. Perhaps different
local grids offer different services or make policy decisions differently.
Two fairly different variations on this federation-of-grids architecture
are worth describing:
Another family of issues concerns whether services
supplied by the grid are themselves agents. This immediately raises
the question of what constitutes an agent. Is it that the entity
communicates via Agent Communication Language or that it contains its own
thread of control (other definitions exist). Many of the services
that agent systems might need may also be supplied by middleware object
services. So agents must be able to interoperate with these services,
either directly, via agent wrappers, or by reimplementing the services
in agent systems).
The grid federation is not logically a flat grid
but rather a hierarchy (like the Internet router hierarchy), graph, or
other kind of federation. Agents register with agent systems and
agent systems (not agents) register with the grid, which is effectively
an agent system itself. So there are hierarchies of agent systems.
There is no central meta grid at all but rather each
agent system that wants to participate in the grid chooses to implement
a collection of protocols that permit it to interoperate with other agent
systems also implementing the grid protocols. Now, no central grid
implementation has primacy but rather it is the universal adoption of shared
protocols that constitutes the grid. In this view, a grid implementation
that provides a collection of services would really just be yet another
agent system, one that might be available to interoperate with existing
agent systems. Many variations of this architecture can exist where
various levels of interoperation are supported among participating agent
4.3.3 Viewing Agent Grids as
Another possible view of grids (sense C) is
as coherent organizational units. In this view, grids are not just
part of the underlying mechanics of an agent system (mechanism or framework)
but are first-class modeling entities meant to represent aspects of the
problem being modeled, helping to make the modeled world of agents one-to-one
with reality. Examples of such a view of grids might be
In this sense of "grid," members have defined roles,
resources, and responsibilities related to the purpose of that grid.
Over time, member roles can change so that a child becomes an adult, a
worker, a parent, and a grandparent. Similarly, these organizational
grids themselves enter, evolve, and leave modeled systems. Resources
are allocated to specific parts of organizational grids (not to some giant
grid in the sky). Time, money, energy, knowledge, respect, and legal
contracts are the glue that binds organizational grids and how the agent
spends its efforts. For example, a battalion grid registers into
a mission grid which includes registering its control, resources, and services.
It seems likely that the mission grid will continue to treat the battalion
as an organizational unit and not as a flattened collection of resources
that can be given away to others - resources are assigned to force units
for a reason - so this kind of grid is a unit of organization and encapsulation.
My doing my mission versus you doing yours versus us cooperating seems
within the province of agent reasoning -- no omnipotent backplane grid
oversees each organizational grid's behavior and resources to insure global
optimization (at the expense of each organizational grid's autonomy); instead
an organizational grid acts with its own autonomy, and a collection of
such grids interoperates, competes, or cooperates emergently depending
on goals and plans of the individuals and organizations. Responsibilities
are related to and help define roles. An agent might be a member
of many organizational grids simultaneously, all of which account for some
of the agent's objectives.
enterprises including virtual enterprises that have
articles of confederation including IP rules and some profit or non-profit
org charts containing a hierarchy of responsibilities
including management, direct and overhead activities. Military force
structures and company org charts are historically predominantly hierarchical.
They reduce information flows from bottom to top and so handle information
overload and communication bandwidth issues.
teams that come together for a short time to do some
herds, flocks, ... with emergent behavior
A technical issue raised by this view is that
if a grid is going to serve as an organizational entity and be referred
to as a unit, then it must have identity, have an interface, etc.
In other words, it must have many, if not all, of the characteristics of
an agent. This may involve defining grids as actual (composite) agents
(or ensembles), or defining a single agent in the grid as a representative
or connection point for the grid. This is related to the issues discussed
in Section 4.3.2, because the desire to use grids to model organizational
entities may drive the sets of structures you want to be able to construct
with grid technology. Another observation related to this view is
that real organizations and systems such as logistics supply chains have
a lot of built-in structure. For example, one logistics supply center
generally knows its main suppliers and specific ways to find others - that
is, they have worked out the way they do the work they spend most of their
time doing. This indicates that agent grid technology should not
concentrate only on dynamic aspects (e.g., matchmaking) and overlook facilities
for modeling existing, more static relationships.
We may or may not choose to call these organizational
units "grids" - we might rather call them teams, ensembles, or even ensemble
agents, and reserve the word "grid" for large-scale or even global-scale
infrastructure. But many of the attributes of a grid are shared with
organizational units - especially organizational goals and their interaction
with sharing of resources controlled by the organization. Even if
not considered as grids, it is clear that organizational units must be
modeled in many agent application scenarios. Their existence accounts
for many use cases of why we, our co-workers, or our competitors
do what we do. Agents will have to model and understand command hierarchies.
So it is most likely that some of the most important agent contributions
will happen while modeling organizational units.
4.4 The Need for Unified Grid
The agent grid (at least as described in [CoA98]),
in wishing to control non-agent resources (such as computing resources)
as well as agents, raises an important additional requirement concerning
agent grids, namely the need for grid-like capabilities at multiple technical
The same requirement is illustrated by the DoD grid
architectures described in Section 2.3. We can think of this as involving
both the need for these individual levels to become more "grid-like", and
the need for these different levels of grid-like capabilities to be combined.
computational grids and computing fabrics (in the
sense of Section 2.1 and 2.2)
the Internet and the Web
distributed object systems (CORBA, DCOM)
agent systems (and agent grids in the CoABS sense)
In considering gridness at these individual levels,
we need not say much about grids at the level of computation, since
the computational grid is our original, paradigmatic computer-related grid.
Grid-like systems also exist at the level of data. By analogy
with general grid principles, data-level grids would interconnect pieces
of data, and enable the interconnected collection of data to be used in
different combinations for various purposes. An obvious candidate
for "gridness" at this level is a database. A database constitutes
a data grid in a loose sense, since it forms an interconnected collection
of related pieces of data. Moreover, a database system also provides
query processing, transaction processing, and metadata (schema and data
dictionary) facilities that enable the database to be treated as a unified
whole, which is a key characteristic of a grid. At the same time,
conventional DBMSs are limited in their support to data of relatively limited
types. We might expect true "data grids" to provide support for many
more data types than current DBMSs. In addition, DBMSs would more
closely resemble "true grids" by incorporating additional self-management
and organizing facilities. For example, an active DBMS that monitored
its own content, and could automatically incorporate attached new data
sources, would exhibit more "true grid" characteristics than current "static"
DBMSs. Ideally, such capabilities would also be extended to allow
the connection of heterogeneous databases to form federations, based on
common metadata, ontology, and conceptual schema concepts, much more readily
than is now the case. DBMS functionality could also be distributed
into the network so that "the network is the DBMS". This trend is
related to the information mediator architectures of the DARPA I*3, BADD,
and AICE programs, as well as to information agents [Tho98a].
The World Wide Web is another example of the variety
of data that we would expect to be included in a "data grid". The
Web includes a wide variety of data types, including not only HTML pages,
but also files of many types (including various document formats, spreadsheets,
etc.). The Web is in many respects a primitive form of distributed
database (using its own particular data representations), similar in many
respects to early network databases. Once a page is posted to a Web
server, it potentially (assuming it points to other pages, and other pages
point to it) becomes part of an interconnected collection of data whose
component pages can be readily and uniformly accessed. However, the
mechanisms needed for unifying this collection into a more coherent whole
are at a relatively early stage. Examples of additional technologies
needed to make the Web more of a "true grid" include better data representation
technologies such as XML, query and transaction support, additional metadata
representation technology and standardized vocabularies, WebDAV (an HTTP
extension for supporting Web Distributed Authoring and Versioning) and
other technologies for making the creation and modification of Web content
as straightforward as retrieval of content, and mechanisms such as URIs
for separating the identity of data from its location (which, in turn,
forms the basis of load balancing mechanisms that can direct requests to
alternative sites), and related mechanisms for dealing with information
that is removed or is temporarily unavailable. These and other Web
technologies are described in [Man98a,b; Man99a,c].
The addition of behavior (code, software)
to data moves us into the realm of systems based on objects, e.g.,
distributed object systems such as CORBA-based systems, or object DBMSs.
In such an object system, the basic "grid" is formed of interconnected
objects (interconnected by virtue of the references objects contain to
other objects). These objects are pre-packaged units of data and
associated software. If we consider the relationships between the
data that forms an object's state and the code which defines its methods
as an additional part of the interconnection that forms the "grid", we
can also think of an object grid as an interconnected data and
software grid. The Web can increasingly be thought of as a form
of object grid as well [Man98a,b; Man99a], due to such things as the increasing
use of scripted Web pages and Java in the Web for integrating behavior
with Web content, and the development of additional technology to more
thoroughly integrate Web and object technologies, such as the Document
Object Model [Woo98], and Web-based remote procedure call mechanisms.
As with the systems discussed in connection with
"data grids", if we add specific additional capabilities or "services"
to the basic network (of objects in this case), the systems become more
like "true grids". For example, an object DBMS provides an integrated
collection of query, transaction, and other facilities that enable the
collection of objects in an object database to behave in a much more cohesive,
"grid-like" fashion. Similarly, the addition of CORBAservices such
as transaction, query, trading (yellow pages), etc. services to the basic
CORBA-enabled network of distributed objects moves a CORBA-based system
in the direction of becoming more grid-like. However, there is much
work to be done to raise object systems to the level of "true grids", containing
well-integrated services, that provide a virtual, distributed, shared object
space, and which transparently handle the load balancing, reliability,
and other issues associated with "true grids". At the same time,
of course, these systems are attempting to address very difficult problems,
about which there is still much debate. For example, there is a considerable
amount of debate in programming and architectural circles as to the extent
to which it is practically possible to achieve transparency when dealing
with both local and distributed objects (see, e.g., [WWWK94]).
In addition to the need for the individual technical
levels described above to become more grid-like, the resulting grids themselves
need to be unified. An agent-level grid supporting this
requirement should provide both grid capabilities at the computation and
data/object levels in support of agents, as well as grid capabilities
at these other levels enabled by agents. Both these types
of support are important in making the maximum use of agent-level capabilities.
For example, agent-level grids (and also object-level grids) can take advantage
of the capabilities of underlying computational grids in supporting their
load balancing and quality-of-service requirements (particularly where
the higher-level grids can interact directly with the lower levels to exert
control). Operational agent grids will also need to interact with
data and object systems (which hopefully will become grids at these levels),
since much information and software functionality that will need to be
accessible to agent grids will continue to exist in these systems.
At the same time, the technical demands of grid
concepts at all levels require increasing amounts of "intelligence", collaborative
ability, adaptability, component mobility, etc.; in other words,
characteristics frequently associated with agents. For example [Bra97b]
discusses the use of agent technology in simplifying and enhancing distributed
computing capabilities, and in particular enhancing intelligent interoperability
in such systems. One such use is the incorporation of agents as resource
managers. He notes: "A higher level of interoperability would
require knowledge of the capabilities of each system, so that secure task
planning, resource allocation, execution, monitoring, and possibly, intervention
between the systems could take place. To accomplish this, an intelligent
agent could function as a global resource manager." Further distributing
these functions among multiple agents, "A further step toward intelligent
interoperability is to embed one or more peer agents within each cooperating
system. Applications request services through these agents at a higher
level corresponding more to user intentions than to specific implementations,
thus providing a level of encapsulation at the planning level, analogous
to the encapsulation provided at the lower level of basic communications
protocols." Agents can also assist in providing better user interfaces
for such distributed systems. As [Bra97b] observes, "In the future,
assistant agents at the user interface and resource-managing agents behind
the scenes will increasingly pair up to provide an unprecedented level
of functionality to people."
[Gen97] also describes the role of agents in enabling
interoperability in distributed systems. In his approach, agents
and facilitators are organized into a federated system, in which agents
surrender autonomy in exchange for the facilitator's services. Facilitators
coordinate the activities of agents and provide other services such as
locating other agents by name (white pages) or by capability (yellow pages),
direct communication, content-based routing, message translation, problem
decomposition, and monitoring. On startup, an agent initiates an
ACL connection to the local facilitator and provides a description of its
capabilities. It then sends the facilitator requests when it cannot
supply its own needs, and is expected to act to the best of its ability
to satisfy the facilitator's requests.
The integration of agents with other levels requires
the use of object/component technology, together with reflective (self-referencing)
capabilities combined with extensive metadata. For example, [Bra97b]
observes: "A key enabler is the packaging of data and software into
components that can provide comprehensive information about themselves
at a fine-grain level to the agents that act upon them. Over time,
large undifferentiated data sets will be restructured into smaller elements
that are well-described by rich metadata, and complex monolithic applications
will be transformed into a dynamic collection of simpler parts with self-describing
programming interfaces. Ultimately, all data will reside in a "knowledge
soup", where agents assemble and present small bits of information from
a variety of data sources on the fly as appropriate to a given context.
In such an environment, individuals and groups would no longer be forced
to manage a passive collection of disparate documents to get something
done. Instead, they would interact with active knowledge media
that integrate needed resources and actively collaborate with them on their
tasks." The Web, in its role as the beginnings of a data/object grid,
can be said to be moving in this direction now. This is particularly
true when technologies for addressing finer-grained portions of Web documents
(e.g., XML, and related technologies) and for attaching behavior to Web
data are considered [Man98a,b; Man99a]. [Bra97b] also identifies
the need for such agents systems to be able to interact with both object
systems and more conventional software: "Ideally, each software component
would be "agent-enabled", however, for practical reasons components may
at times still rely on traditional interapplication communication mechanisms
rather than agent-to-agent protocols."
Objects provide a generic modeling or abstraction
mechanism for looking at the wide range of resources that need to be included
at all levels in such a combined system. An object in this sense is simply
an encapsulated unit that has identity, an interface (possibly more than
one), and communicates via messages with other objects and the "outside".
This use of objects mirrors the use of objects as a general modeling mechanism
in the ISO Reference Model of Open Distributed Processing (RM-ODP) [ISO95].
RM-ODP is intended to describe any distributed processing system (including,
in some cases, the roles of humans that may be involved in the system),
and its use of objects as a modeling abstraction is not meant to imply
that the system is actually implemented using object-oriented programming
techniques. However, while object abstractions need not necessarily be
implemented using object-oriented programming, the use of these abstractions
makes the application of object technologies such as CORBA, Jini, etc.
Representing the computational and communication
components of a computational grid as objects, as illustrated in the Legion
system's reflective capabilities, allows these components to be both uniformly
represented within the architecture, and managed in a straightforward way
by higher level components. The approach of representing computer
or network components as objects for management purposes is well-known
in both network and computer system management technologies. Data
can be represented as objects in a straightforward fashion, by defining
object interfaces containing get (read) and set (write) operations.
The World Wide Web Consortium Document Object Model [Woo98] is an example
of a set of such interfaces designed to provide object-oriented interfaces
to Web data. Such interfaces provide programs and agents with more
uniform access to information represented both as data (e.g., in databases,
on file systems, or in the Web) in distributed object systems, and also
support the integration of more "intelligence", in the form of behavior,
with such data. Finally, as noted in Section 4, object interfaces
can encapsulate "smart things", e.g., agents and human beings. In
some cases the messaging protocols between these various kinds of objects
will be relatively simple (e.g., conventional object RPC between distributed
software objects, or commands sent to hardware), while in other cases they
will be more complicated (agent communication language (ACL) sent between
agents, or the email flow between people); however, similar abstraction
principles can apply to objects at all levels.
In such an integrated architecture, there is also
a need to define additional forms of organization on the available resources
in addition to the various grid technical levels of computational, data,
etc.. For example, it may be desirable to build functional
layers of grids, such as the information, sensor, and engagement grids
identified in the DoD grid architectures described in Section 2.3.
Building grids at each of these functional layers would require use of
technologies from more than one, and possibly all, of the technical grid
levels. In addition, large scale distributed object systems increasingly
are being designed with 3- (or sometimes multi-) tier architectures [MGHH+98].
These architectures involve the division of the system's components (and
object definitions) into functional tiers based on the different functional
concerns they address. For example, a typical 3-tier architecture has a
tier for objects representing user interface elements, a tier for business
or application objects, and a tier for database servers. The business object
tier separates out the common definitions of enterprise operations and
semantics from the more specialized concerns addressed in the other tiers.
Other examples of such organization include the use of Common Schema concepts
[Man98c] or enterprise-level ontologies (enterprise-wide agreements on
common semantics), and specialized schemas/ontologies for use by specialized
user communities (together with mappings to the common definitions where
Semantics-based mappings between the different
technical levels in such an architecture are also required. For example,
the ATAIS architecture document [BFHH+98] describes a series of interoperability
levels: isolated, co-habitable, syntactic, semantic, seamless, and
adaptive. The computational grid idea can be characterized as emphasizing
high levels of interoperability on this spectrum, but at a low level of
abstraction (i.e., in terms of computing resources). The agent grid
often involves a much higher level of abstraction. Other levels (e.g.,
data, objects) are, in a sense, in between these extremes. Raising
the level of abstraction complicates providing "gridness" (deep integration)
because the requirements on one side, and the available resources/services
on the other, are more semantically heterogeneous (unlike, e.g., "memory"
and "CPU bandwidth"), and thus both characterizing them, and matching requirements
with resources, becomes harder. An example of this is the complexity
of addressing quality-of-service (QoS) issues, which involves defining
mappings between "quality" measures at higher levels, and resource allocations
at lower levels [Man99b].
Such additional levels of organization provide
the basis for more interoperability among the various technical levels
of resources contained in the system, and hence help enhance the ability
of these resources to operate as a "true grid".
At the same time, it is worth pointing out that
there is not likely to be one ultima grid or all-purpose unified true grid
framework as technology evolves. Instead, migration paths will likely
connect grids and grid levels incrementally where it is most useful to
do so. But recognizing that that is what is happening will likely
make grid composition easier as architectural patterns for doing so become
4.5 Open Issues about the Agent
Based on the preceding discussion, the following
is a partial list of agent grid issues that need to be resolved in defining
an agent grid:
What will the benefits be to applications that make
use of an agent grid?
If commercial-off-the-shelf component software technology
is not easy to compose today and assuming this is a goal of the agent grid,
how will agent technology help?
Is the agent grid a kind of (super) agent or agent
Does it negotiate with resident agents for resources?
Do grids and agent systems both provide services
within their biosphere (boundary).
If computational grids support sharing computations,
then what sharing do agent grids support? Possible answers:
information, knowledge, decisions, requests and responses, plans?
agent capabilities of all kinds including the resources that can be assigned
to agents, like processor usage, disk space, etc.
Are there definitional properties of agent grids?
Is there any minimal or maximal set of properties
that we can agree on for something to be an agent grid? A minimal grid
may be lightweight in providing a least number of services. Is no
services the minimum or must a grid at least provide an agent registry?
Can just any registry do?
Is the criterion for agent gridness based
on a certain set of technical mechanisms (e.g., makes use of an ACL) or
just any system with (some of) these properties: autonomy, adaptive,
cooperative, mobile, interoperable.
How much do we need to know about agents to define
the agent grid? Does the same grid support agents that are mobile,
intelligent, complex, autonomous, reactive, etc.
Are agents fully autonomous including being independent
of the/any grid? Or can they be dependent on specific grid services
being available (and fail if the service becomes unavailable)?
Are there non-grid agents, that is, agents outside
any grid? If so, how do they interoperate with grid agents?
How are agents and grid services related? For
instance, do agents implement the long list of services that the grid provides
or is that underlying component software? Is there a difference between
a service being an agent and being controlled by an agent?
Does each agent contain a planner or is a planning
service global to a collection of agents?
Is a grid an abstraction layer defined by emergent
properties (implicit in the way agents interact with each other) or an
How can we avoid the grid as "yet-another-architecture"?
What is the appropriate relationship between required grid capabilities
and capabilities in existing multi-agent architectures, distributed object
systems, DBMSs, simulation systems, network management systems, workflow,
and meta computing systems? How do we make the best use of these
existing capabilities in building agent grids?
Scalability, Federation, and System-of-system
Does the grid actively control services, resources
and optimization? Can the grid unilaterally take resources
away from agents that have them?
Where are the control points where different control
algorithms might be substituted into the grid architecture
Are agents actively part of the grid or are they
end users of the grid?
How much does the grid have to understand about the
tasks of the agents?
Are some grid governments/economies inherently better
than others? e.g., socialist vs. market
How are resource allocations made in the grid (e.g.,
is competition for resources based on a marketplace concept)?
Pervasiveness andGrid Economy
Will one grid scale to support millions of agents,
or are there many such grids?
How do agent grids federate or interoperate?
How are grids federated? e.g., global grid, flat
grid, hierarchy of grids
Do agents seek out/lookup different kinds of grids
depending upon their needs? Do grids seek out agents/systems to join
What happens if agents interoperate that come from
differently configured grids?
Can agents belong to multiple grids simultaneously?
How should quality of service (QoS) and system-wide
properties like those covered in [Man99b] be architected into the agent
grid? These include: reliability, security, manageability, administrability,
evolvability, flexibility, affordability, usability, understandability,
availability, scalability, performance, deployability, configurability,
adaptability, mobility, responsiveness, interoperability, maintainability,
degradability, durability, accessibility, accountability, accuracy, demonstrability,
footprint, simplicity, stability, fault tolerance, timeliness, schedulability,
survivability, simplicity, openness, seamlessness, safety, and trust.
Do some grids have these properties and others not?
If different grids contain different policy choices
or different services, how does that affect agents communicating across
Can we add new services and -ilities to a grid once
it is deployed (grid evolution)? how transparent is addition or subtraction
of services and ilities?
How can we accommodate heterogeneity in local grids
and still guarantee systemic properties across grid federations?
If the system has an -ility, is the grid tasked with
monitoring or full enforcement? Is -ility maintenance local or global?
How do we foster an economy of componentized agent
software? What are the roadblocks and what is missing?
micro licensing component software and leasing resources
across the network
like many grid services, licensing’s degenerate form
is no licensing.
Agents and component software cannot succeed without
an economic model that makes broad communities get value from them.
One way to do this is via licensing space on your machine, capabilities
and services, data sources, …
A model of licensing might be critical for CoABS
to succeed in the large.
How do we populate the grid with millions of agents
and/or advertisements for services?
Do planning techniques scale for Internet and programming
The concept of a "grid" is a generally useful idea,
but only if it means something more than an ordinary collection of distributed
resources. Ideally, it implies some higher level integration of distributed
resources beyond simply connecting them. Additional work is needed
to identify the details of the added functionality required to go beyond
simple distributed collections of resources to the formation of "true grids".
The grid concept can be applied at a number of individual technical levels
(computation, data, object, agent). Grids have been defined and demonstrated
at the computation level already. At other levels, more advanced
integrating mechanisms have been defined (e.g., federated DBMSs at the
data level), but these do not yet approach the level of grids.
and Future Work
The notion of an agent grid in particular is,
on the surface, not too controversial. It is clear that
At the same time, there are several ways to characterize
the agent grid and many open issues. For example, few would agree
to one global construct called the agent grid or one such implemented
system that makes all optimization tradeoffs and provides all system-wide
control. As a result, there is more work to be done in areas including:
there are several kinds or levels of grids and there
is a body of agent infrastructure technology and requirements that could
be useful in developing grids (e.g., ACLs, matchmakers, agent wrappers,
the need for agent system interoperability)
the grid might be related to agent systems either
as providing a master agent registry mechanism or by itself being a meta
agent-like organizational units (ensembles) act like
grids in that they control resources.
Looking more broadly, to advance the use of the grid
concept, the general idea of a grid needs to be applied at multiple technical
levels (computation, data, object, agent) in order to identify in detail
the specific technologies which would enable the creation of grids from
collections of distributed resources at each of these levels. Grids
at these individual levels are useful by themselves, but the maximum advantage
comes when these different levels of grid capabilities are combined.
There is a need for additional work to develop a unifying technical grid
architecture which incorporates these separate grid levels, and identifies
mappings between them. The development of grid concepts at these
various technical levels will require more work in areas such as:
issue identification and resolution
identification and further development of technology
underpinnings (e.g., Web and distributed object technologies)
identification of grid service requirements
how to obtain system-wide properties (-ilities and
quality of service issues)
interoperability and loose coupling
functionality and pervasiveness demonstrations
The discussion in this paper does not replace the
need to address these detailed technical issues. However, it does
provide a way of thinking about the general ideas which the various grid
concepts have in common, and the issues they raise, which hopefully can
be helpful in attempts to address them.
the identification and composability of resources
in these systems (reducing the need for explicit programmer or other human
the dynamic aspects of systems (e.g., resources entering
and leaving, load balancing)
greater generality, ubiquity, and mobility of computing
resources and applications
loose coupling between layers so different layers
can be at different stages of development.
concepts or enterprise-level ontologies (enterprise-wide
agreements on common semantics), and specialized schemas/ontologies for
use by specialized user communities. Semantics-based mappings between
the different technical levels in such an architecture are also required.
application requirements to understand how future
applications can be written to take advantage of grid architectures.
This research was sponsored by the Defense Advanced
Research Projects Agency and managed by the U.S. Air Force Research Laboratory
under contract F30602-98-C-0159. The views and conclusions contained in
this document are those of the authors and should not be interpreted as
representing the official policies, either expressed or implied, of the
Defense Advanced Research Projects Agency, U.S. Air Force Research Laboratory,
Department of Defense, or the United States Government. We acknowledge
helpful discussions and input from Paul Pazandak (OBJS), Venu Vasudevan
(OBJS), Richard Ivanetich (IDA), Al Piszcz (MITRE), Brian Kettler (ISX),
Jeff Bradshaw (Boeing), and Doyle Weishar (Global Infotek).
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