Abstract
The current trend for parallelizing scalable graph analysis is to use major big-data tools such as MapReduce, Spark/GraphX, and Storm. Each programming framework uses graph analysis strategies based on either (1) message passing among graph vertices or (2) screening of graph edges. The former keeps exchanging messages among all neighboring vertices until all of them fall into a stable state and thus no more messages are generated. The latter examines the connectivity of edges to find particular network shapes, (e.g., counting the number of triangles). Although these two approaches are well established as conventional graph solutions, the underlying algorithms may not be clear enough to keep track of how vertices, edges, and messages are processed in parallel. Contrary to these conventional approaches, agent-based graph analysis drives agents over a graph, which allows programmers to code their solutions from the drivers seat view. However, there are performance drawbacks, mainly resulting from agent management overheads, that make it impractical to use agents. The Multi-Agent Spatial Simulation (MASS) library uses reactive agents, small enough to spawn, move, and get terminated quickly. By implementing two optimization techniques in MASS: agent population control and asynchronous agent migration, we have facilitated agent-based graph analysis. This paper presents these two techniques, demonstrates agents intuitive programmability, and their competitive execution performance.
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Acknowledgments
The cluster system used for this research was donated by Fluke Corporation. Two of the authors were supported by the UWB Computing & Software System Division’s research assistantship. We would also like to thank Mr. Sinchai DeLong, the divisional senior computer specialist for all his technical supports.
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Gordon, C., Mert, U., Sell, M., Fukuda, M. (2019). Implementation Techniques to Parallelize Agent-Based Graph Analysis. In: De La Prieta, F., et al. Highlights of Practical Applications of Survivable Agents and Multi-Agent Systems. The PAAMS Collection. PAAMS 2019. Communications in Computer and Information Science, vol 1047. Springer, Cham. https://doi.org/10.1007/978-3-030-24299-2_1
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