Abstract
As dynamic graph data have been actively used, incremental graph partition schemes have been studied to efficiently store and manage large graphs. In this paper, we propose a vertex-cut based novel incremental graph partitioning scheme that supports load balancing in a distributed environment. The proposed scheme chooses the load of each node that considers its storage utilization and throughput as the partitioning criterion. The proposed scheme defines hot data that means a particular vertex frequently searched among graphs requested by queries. We manage and utilize hot data for graph partitioning. Finally, we perform vertex-cut based dynamic graph partitioning by using a vertex replication index, the load each node, and hot data to distribute the load evenly in a distributed environment. In order to verify the superiority of the proposed partitioning scheme, we compare it with the existing partitioning schemes through a variety of performance evaluations.
Highlights
Graph data are used to express the relationship or interaction between users or objects
We propose a novel graph partitioning scheme to improve the processing performance of graph queries in a large dynamic graph environment
In this paper, we proposed a novel dynamic graph partitioning scheme considering the load of a node and hot data to handle a large dynamic graph
Summary
Graph data are used to express the relationship or interaction between users or objects. The proposed scheme presents a partitioning policy that considers the vertex replication ratio, storage utilization, and throughput to improve the system processing performance through the load distribution. We propose a novel graph partitioning scheme to improve the processing performance of graph queries in a large dynamic graph environment. 3) Vertex-cut based dynamic graph partitioning on distributed systems: The proposed scheme utilizes a vertex replication index in addition to node load and hot data to perform vertex-cut based dynamic graph partitioning. The proposed scheme further considers the vertex replication index because the higher the number of nodes is, the lower the load to store is. It performs efficient dynamic graph partitioning by reflecting these three characteristics.
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