期刊
IEEE ACCESS
卷 9, 期 -, 页码 65254-65265出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3075457
关键词
Throughput; Distributed databases; Performance evaluation; Indexes; Load modeling; Load management; Real-time systems; Distributed processing; graph partitioning; graph stream; hot data; vertex-cut partitioning
资金
- National Research Foundation of Korea (NRF) - Korea government (MSIT) [2019R1A2C2084257]
- Institute of Information & Communications Technology Planning & Evaluation (IITP) - Korea government (MSIT) [2014-3-00123]
- MSIT (Ministry of Science and ICT), Korea under the Grand Information Technology Research Center support program [IITP-2021-2020-0-01462]
- AURI (Korea Association of University, Research institute and Industry) - Korea Government (MSS: Ministry of SMEs and Startups) [S3047889]
- Ministry of Health & Welfare (MOHW), Republic of Korea [S3047889] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This paper proposes a novel incremental graph partitioning scheme based on vertex-cut, supporting load balancing and utilizing hot data for partitioning. The superiority of the proposed scheme is verified through performance evaluations, comparing it with existing partitioning schemes.
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.
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