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Memory management optimization strategy in Spark framework based on less contention

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Abstract

The parallel computing framework Spark 2.x adopts a unified memory management model. In the case of the memory bottleneck, the memory allocation of active tasks and the RDD(Resilient Distributed Datasets) cache causes memory contention, which may reduce computing resource utilization and persistence acceleration effects, thus affecting program execution efficiency. To this end, we propose less contention management strategy, abbreviated as MCM, to reduce the negative impact of memory contention. MCM is divided into two steps: Firstly, the task minimum memory priority guarantee algorithm is priority to meet the minimum resources of tasks for execution, to optimize the number of active tasks. Secondly, considering contention costs, the persisted location selection algorithm dynamically selects the best storage location to improve the effect of persistence acceleration. The experimental results comfirm that MCM has wonderful adaptability and scalability. In the case of serious memory bottleneck, MCM obviously reduces job execution time. Compared with similar works, such as only_memory, only_disk, memory_and_disk, DMAOM and SACM, MCM reduces the execution time by 28.3% .

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Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author or the first author on reasonable request.

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Acknowledgements

This work was supported by Henan Province Science and Technology R &D Project (Grant No: 212102210078) and Henan Province Major Science and Technology Project (Grant No: 201300210400).

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Authors and Affiliations

  1. School of Software, Henan University, Kaifeng, 475000, China

    Yixin Song, Junyang Yu, JinJiang Wang & Xin He

  2. Intelligent Data Processing Engineering Research Center of Henan Province, Kaifeng, China

    Yixin Song, Junyang Yu, JinJiang Wang & Xin He

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  1. Yixin Song
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  2. Junyang Yu
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Correspondence to Junyang Yu.

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Song, Y., Yu, J., Wang, J. et al. Memory management optimization strategy in Spark framework based on less contention. J Supercomput 79, 1504–1525 (2023). https://doi.org/10.1007/s11227-022-04663-5

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