Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (2): 291-298.doi: 10.11896/jsjkx.241000027

• Computer Network • Previous Articles     Next Articles

Task Scheduling in Heterogeneous Server Systems Based on Data Splitting and Energy-aware Strategies

YANG Chen1, XIAO Jing2, WANG Mi3   

  1. 1 Key Laboratory of Aerospace Information Security,Trusted Computing,Ministry of Education,School of Cyber Science,Engineering,Wuhan University,Wuhan 430072,China
    2 School of Computer Science,Wuhan University,Wuhan 430072,China
    3 State Key Laboratory of Surveying,Mapping and Remote Sensing Information Engineering,Wuhan University,Wuhan 430072,China
  • Received:2024-10-08 Revised:2024-12-03 Online:2025-02-15 Published:2025-02-17
  • About author:YANG Chen,born in 1998,postgra-duate.His main research interests include edge computing and task scheduling.
    WANG Mi,born in 1974,Ph.D,professor,Ph.D supervisor,recipient of the National Science Fund for Distinguish Young Scholars.His main research interests include high-precision remote-sensing image processing and so on.
  • Supported by:
    National Key Research and Development Program of China(2022YFB3902804).

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Abstract: Heterogeneous server platforms provide powerful computing capabilities for large systems but also pose challenges in system complexity and energy consumption management.This study delves into the energy-aware scheduling problem based on data splitting for dependent tasks in heterogeneous server systems.First,the system environment,dependent tasks,and data transmission patterns are modeled,and the energy-aware scheduling problem is formulated as a constrained optimization problem aimed at minimizing the completion time of task scheduling.Subsequently,an energy-aware scheduling algorithm(DSEA)based on data splitting and task prioritization strategies is proposed.This algorithm seeks approximate optimal startup times and server allocation plans for each task by optimizing data splitting strategies,task priorities,and weight-based energy allocation.To validate the effectiveness of the proposed method,1 000 jobs of varying lengths are randomly selected from the Alibaba cluster dataset for simulation experiments.Experimental results demonstrate that the DSEA algorithm exhibits significant performance advantages over three existing algorithms in various application scenarios.

Key words: Heterogeneous servers, Energy-aware, Data splitting, Dependent task scheduling, Task prioritization

CLC Number: 

  • TP311
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