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Analyzing large graph data is an essential part of many modern applications,such as social networks. Due to its large computational complexity, distributedprocessing is frequently employed. This requires graph data to be dividedacross nodes, and the choice of partitioning strategy has a great impact on theexecution time of the task. Yet, there is no one-size-fits-all partitioningstrategy that performs well on arbitrary graph data and algorithms. Theperformance of a strategy depends on the characteristics of the graph data andalgorithms. Moreover, due to the complexity of graph data and algorithms,manually identifying the best partitioning strategy is also infeasible. In thiswork, we propose a machine learning-based approach to select the mostappropriate partitioning strategy for a given graph and processing algorithm.Our approach enumerates viable partitioning strategies, predicts the executiontime of the target algorithm for each, and selects the partitioning strategywith the fastest estimated execution time. Our machine learning model istrained on features extracted from graph data and algorithm pseudo-code. Wealso propose a method that augments real execution logs of graph tasks tocreate a large synthetic dataset. Evaluation results show that the strategiesselected by our approach lead to 1.46X faster execution time on averagecompared with the mean execution time of the partitioning strategies and about0.95X the performance compared to the best partitioning strategy.

Machine Learning-based Selection of Graph Partitioning Strategy Using the Characteristics of Graph Data and Algorithm