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HTD: heterogeneous throughput-driven task scheduling algorithm in MapReduce


As one of the most popular parallel data processing models, data analysis system MapReduce has been widely used in many fields. Task scheduling is the core module in MapReduce system, and the quality of the scheduling algorithm directly affects the processing capacity of the system. Since new nodes need to be continuously added in the cluster to improve the processing capacity of the cluster, objectively, the heterogeneity of the cluster is caused. Heterogeneous environment is common in practical application scenarios, but there has been little research on task scheduling in heterogeneous environment. For this reason, this paper presents an in-depth study of task scheduling in heterogeneous environment and proposes a new task scheduling algorithm HTD. First, we give a formal definition of the throughput-driven task scheduling problem in a heterogeneous environment. Second, we design the scheduling algorithm HTD, which quickly obtains the completion sequence of a jobs set and optimizes the task scheduling details in heterogeneous environment. Finally, a series of experiments show the efficiency and effectiveness of the algorithm.

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This work is supported by the National Natural Science Foundation of China (Grant Nos. 61602076, 61702072, 62002039, 61976032), the China Postdoctoral Science Foundation funded projects (Grant Nos. 2017M611211, 2017M6211, 2019M661077), the Natural Science Foundation of Liaoning Province (Grant No. 20180540003), CERNET Innovation Project (Grant No. NGII20190902).

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Correspondence to Xite Wang.

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Wang, X., Wang, C., Bai, M. et al. HTD: heterogeneous throughput-driven task scheduling algorithm in MapReduce. Distrib Parallel Databases 40, 135–163 (2022).

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  • MapReduce
  • Scheduling
  • Heterogeneous
  • Throughput