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dc.contributor.authorGupta, Abhishek
dc.description.abstractMany agent-based simulation kernels rely on message passing in their core implementation. As the number of agents in a simulation increases or as the complexity of their communication expands the number of messages can increase exponentially. This is troublesome because the message content itself may be quite small, while the overhead, including message headers can dominate bandwidth and processing time. In these cases message passing becomes bottleneck to scalability: The overhead of message exchange may saturate the network and degrade performance of the simulation. One approach to this challenge that has been investigated in related networking and simulation research centers on combining or “piggybacking” multiple small messages together with a consolidated header. In many applications performance improves as larger, but fewer messages are sent. However, the pattern of message passing is different in the case of agent-based simulation (ABS), and this approach has not yet been explored for ABS systems. In this work we provide an overview of the design and implementation of a message piggybacking approach for ABS systems using the SASSY platform. SASSY is a hybrid large scale distributed agent-based simulation system that provides an agent–based API to a PDES kernel. We provide a comparative performance evaluation for implementations in SASSY with a combined RMI, and shared memory message passing approach, and RMI only. We also show performance of our new adaptive message clustering mechanism that clusters messages when advantageous and avoids clustering when the overhead of clustering dominates.
dc.subjectAgent Based Simulation
dc.subjectDistributed Simulation,
dc.titleAdaptive message clustering for distributed agent based systems
dc.description.departmentComputer Science
dc.description.majorComputer Science
dc.description.advisorMaria Hybinette
dc.description.committeeMaria Hybinette
dc.description.committeeLakshmish Ramaswamy
dc.description.committeeKang Li

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