Chuan Li, Xuanlin Wen*
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3843-3875, 2025, DOI:10.32604/cmc.2025.063047
- 19 May 2025
Abstract Spark performs excellently in large-scale data-parallel computing and iterative processing. However, with the increase in data size and program complexity, the default scheduling strategy has difficulty meeting the demands of resource utilization and performance optimization. Scheduling strategy optimization, as a key direction for improving Spark’s execution efficiency, has attracted widespread attention. This paper first introduces the basic theories of Spark, compares several default scheduling strategies, and discusses common scheduling performance evaluation indicators and factors affecting scheduling efficiency. Subsequently, existing scheduling optimization schemes are summarized based on three scheduling modes: load characteristics, cluster characteristics, and matching More >
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