2014/11/17 by Md. Lisul Islam, Islam, Md. Lisul, Novia Nurain +5
Computer Science · #Artificial Intelligence (cs.AI) #Distributed #FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE) #Parallel #and Cluster Computing (cs.DC) #cs.AI #cs.DC #cs.NE
paper · pdf · doi:10.48550/arxiv.1411.4379
Accepted in the 1st International Conference on Networking Systems and Security 2015 (NSysS 2015)
arxiv created 2014/11/17 · arxiv updated 2014/11/18
Graph partitioning, a well studied problem of parallel computing has many applications in diversified fields such as distributed computing, social network analysis, data mining and many other domains. In this paper, we introduce FGPGA, an efficient genetic approach for producing feasible graph partitions. Our method takes into account the heterogeneity and capacity constraints of the partitions to ensure balanced partitioning. Such approach has various applications in mobile cloud computing that include feasible deployment of software applications on the more resourceful infrastructure in the cloud instead of mobile hand set. Our proposed approach is light weight and hence suitable for use in cloud architecture. We ensure feasibility of the partitions generated by not allowing over-sized partitions to be generated during the initialization and search. Our proposed method tested on standard benchmark datasets significantly outperforms the state-of-the-art methods in terms of quality of partitions and feasibility of the solutions.