DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

• For tasks, the UID of the direct object also
encoded as a symbol.
• For allocations, the allocation results observed.
A design objective of Castellan was to reduce the amount
of bandwidth consumed by the event traces while
retaining key data and minimizing CPU consumption.
This was accomplished through a variety of approaches,
including:
• Compression of UID and other symbols using a
space efficient symbol id based protocol.
• Detecting and transmitting only changes in the
allocation results for each task.
• Batching mechanisms, e.g. serializing batches of
messages rather than individual messages.
The generated stream of events can be delivered to the
Castellan server implement during planning. The message
transport between the client implementation and the
server implementation can be varied depending on the
application. Currently, a buffered relay is used to transmit
events through batches of serialized events. (A relay is an
point-to-point communications mechanism between
Cougaar agents.) This form of “in-band” communications
shares the communications channel with other Cougaar
message traffic and hence is more suitable for distributed
control applications in which agents need to be aware of
the detailed planning status of other agents within the
society. Alternative message transport implementations
(e.g. using a separate communications backplane) can
provide non-intrusive analysis for debugging and testing.
The scalability of Castellan as a real-time monitoring tool
is limited primary by the following factors:
• Bandwidth of the links between the monitoring
agents. For example, a 160,000 element event
trace from a 30 agent, 60 min. long planning run
in total takes approximately a total of 8 MB of
bandwidth. This is not significant for a LAN test
environment but may be an issue for real world
operating environments.
• The impact on CPU consumption for each agent
being monitored. In a small Cougaar society of
~30 agents, this was measured to impact total
planning time by approximately 5%.
• The processing bottleneck at the server agent.
While receiving the data is not very expensive,
inserting the results at run time into a SQL
database tends to overwhelm most typical
processors.
In large agent societies, we do not expect that all agents
can be monitored by a single server due to the volume of
data generated. Instead, Castellan can be configured to
monitor any subset of agents as desired, e.g. a community
or an enclave. For debugging and profiling purposes, the
data can then be merged after the run is complete.
3. Graph Reduction Using Subgraph
Isomorphism
This section describes a significant application of the
Castellan system that enables real-time and off-line
analysis of the planning functions of a distributed agent
system. Existing graph “clustering” and reduction
approaches have been used in data mining applications to
find meaningful repeated subgraphs within a larger graph.
[1][2]
The plan graphs generated during a single planning run of
a Cougaar agent society can be extremely large. For
example, the event traces generated from a relatively
small test planning society engaged in logistics planning
consisting of more than thirty agents resulted in over one
hundred sixty thousand events and a very large
corresponding plan graph with thousands of individual
tasks, plan elements and assets. In this section, a general
graph reduction algorithm is described that can be used to
reduce the size of the plan graph in a manner tailored to
specific applications.
We define a plan graph P={N,E} as a set of nodes
N={T,A} and a set of directed, attributed edges E={L,
X,G}. Here, the nodes N consist of a set of tasks T and a
set of assets A. The attributes associated with each task t
∈ T are expressed as a tuple (V,Uid,Agent) and can
include other properties depending on the amount of
detail collected within the event trace. The plan graph P
is a directed acyclic graph (DAG) since no cycles can
exist in the current Cougaar task grammar. The output of
the algorithm is a reduced graph R={N’,E’}.
The set of edges E consist of a set of allocations L, a set
of expansions X, and a set of aggregations G. Also, the
nature of the Cougaar task grammar dictates a number of
additional constraints on the plan graph. These include:
• Each task node t 1 ∈ T can be connected to either
an asset node s∈S through an allocation edge l
or another task node t 2 through an expansion or
allocation edges e ∈{X, G}.
• Each task node t ∈ T has exactly one edge (and
hence one parent node) except for those task
nodes which are connected by a set of edges G’
⊂G.
• The set of aggregation edges G is subdivided
into a set of disjoint subsets which have the same
destination node.