Distributed Vertex-Cut Partitioning

• Published in: Distributed Applications and Interoperable Systems Vol. 8460; pp. 186 - 200
• Main Authors: Rahimian, Fatemeh, Payberah, Amir H., Girdzijauskas, Sarunas, Haridi, Seif
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Germany Springer Berlin / Heidelberg 2014; Springer Berlin Heidelberg
• Series: Lecture Notes in Computer Science
• Subjects: Big data; Color Exchange; Data analytics; Direct Neighbor; Graph Partitioning; Graph processing; Graph theory; Graphic methods; Interoperability; Iterative methods; Local Search Algorithm; Multiple machine; Parallel and distributed algorithms; Random assignment; Real-world graphs; Simulated Annealing; Simulated annealing techniques
• ISBN: 9783662433515; 3662433516
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-662-43352-2_15

Graph processing has become an integral part of big data analytics. With the ever increasing size of the graphs, one needs to partition them into smaller clusters, which can be managed and processed more easily on multiple machines in a distributed fashion. While there exist numerous solutions for edge-cut partitioning of graphs, very little effort has been made for vertex-cut partitioning. This is in spite of the fact that vertex-cuts are proved significantly more effective than edge-cuts for processing most real world graphs. In this paper we present Ja-be-Ja-vc, a parallel and distributed algorithm for vertex-cut partitioning of large graphs. In a nutshell, Ja-be-Ja-vc is a local search algorithm that iteratively improves upon an initial random assignment of edges to partitions. We propose several heuristics for this optimization and study their impact on the final partitioning. Moreover, we employ simulated annealing technique to escape local optima. We evaluate our solution on various graphs and with variety of settings, and compare it against two state-of-the-art solutions. We show that Ja-be-Ja-vc outperforms the existing solutions in that it not only creates partitions of any requested size, but also requires a vertex-cut that is better than its counterparts and more than 70% better than random partitioning.