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HEPart: A balanced hypergraph partitioning algorithm for big data applications
HEPart: یک الگوریتم پارتیشن بندی فوق العاده گرافیکی متعادل برای برنامه های داده بزرگ-2018 Minimizing the query cost among multi-hosts is important to data processing for big data applications.
Hypergraph is good at modeling data and data relationships of complex networks, the typical big data
applications, by representing multi-way relationships or interactions as hyperedges. Hypergraph parti
tioning (HP) helps to partition the query loads on several hosts, enabling the horizontal scaling of large
scale networks. Existing heuristic HP algorithms are generally vertex hypergraph partitioning, designed
to minimize the number of cut hyperedges while satisfying the balance requirements of part weights
regarding vertices. However, since workloads are mainly produced by group operations, minimizing
query costs landing on hyperedges and balancing the workloads should be the objectives in horizontal
scaling. We thus propose a heuristic hyperedge partitioning algorithm, HEPart. Specifically, HEPart
directly partitions the hypergraph into K sub-hypergraphs with a minimum cutsize for vertices, while
satisfying the balance constraint on hyperedge weights, based on the effective move of hyperedges. The
performance of HEPart is evaluated using several complex network datasets modeled by undirected
hypergraphs, under different cutsize metrics. The partitioning quality of HEPart is then compared with
alternative hyperedge partitioners and vertex hypergraph partitioning algorithms. The experimental
findings demonstrate the utility of HEPart (e.g. low cut cost while keeping load balancing as required,
especially over scale-free networks).
Keywords: Hypergraph partitioning ، Hyperedge partitioning ، Load balancing ، Big data |
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