Graph partitioning algorithms for minimizing inter-node communication on a distributed system | Posted on:2014-01-01 | Degree:M.S.L | Type:Thesis | University:The University of Toledo | Candidate:Gadde, Srimanth | Full Text:PDF | GTID:2458390008952707 | Subject:Computer Science | Abstract/Summary: | | Processing large graph datasets represents an increasingly important area in computing research and applications. The size of many graph datasets has increased well beyond the processing capacity of a single computing node, thereby necessitating distributed approaches. As these datasets are processed over a distributed system of nodes, this leads to an inter-node communication cost problem (also known as inter-partition communication), negatively affecting the system performance. This research proposes new graph partitioning algorithms to minimize the inter-node communication by achieving a sufficiently balanced partition. Initially, an intuitive graph partitioning algorithm using Random Selection method coupled with Breadth First Search is developed for reducing inter-node communication by achieving a sufficiently balanced partition. Second, another graph partitioning algorithm is developed using Particle Swarm Optimization with Breadth First Search to reduce inter-node communication further. Simulation results demonstrate that the inter-node communication using PSO with BFS gives better results (reduction of approximately 6% to 10% more) compared to the RS method with BFS. However, both the algorithms minimize the inter-node communication efficiently in order to improve the performance of a distributed system. | Keywords/Search Tags: | Inter-node communication, Graph, Distributed, Algorithms, System | | Related items |
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