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Research on computing task scheduling method for distributed heterogeneous parallel systems.

Xianzhi Cao1, Chong Chen1, Shiwei Li1

  • 1College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040, China.

Scientific Reports
|March 16, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel scheduling method for parallel task flows on diverse devices, significantly cutting energy use by 14.3% while meeting response time demands for greener computing.

Keywords:
Directed acyclic graphDynamic redundancyDynamic schedulingHeterogeneous parallel

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Area of Science:

  • Distributed Systems and Computing
  • Green Computing and Energy Efficiency
  • Artificial Intelligence for Scheduling

Background:

  • The proliferation of terminal devices necessitates efficient scheduling of massive parallel task streams on distributed platforms.
  • Computing resource providers face challenges in reliability, response times, cost reduction, and energy efficiency for green computing.
  • Heterogeneous parallel task flow scheduling is crucial for optimizing resource utilization and minimizing system energy consumption.

Purpose of the Study:

  • To investigate the heterogeneous parallel task flow scheduling problem with the objective of minimizing system energy consumption under strict response time constraints.
  • To develop a dynamic scheduling approach that enhances reliability and service quality while optimizing energy efficiency.
  • To address the core challenge of scheduling massive parallel task streams in the context of growing terminal devices.

Main Methods:

  • Modeling the task scheduling problem as a mixed-integer nonlinear programming problem using a Directed Acyclic Graph.
  • Proposing a dynamic scheduling method that integrates heuristic and reinforcement learning algorithms for task flow management.
  • Applying dynamic redundancy based on reliability analysis to improve system fault tolerance and service quality.

Main Results:

  • The proposed method achieved a 14.3% reduction in energy consumption compared to existing approaches.
  • Demonstrated significant improvements in energy efficiency for heterogeneous parallel task flow scheduling.
  • Validated the effectiveness of the dynamic scheduling and redundancy strategy on practical workflow instances.

Conclusions:

  • The developed dynamic scheduling method effectively minimizes system energy consumption while adhering to response time constraints.
  • Integrating heuristic and reinforcement learning with dynamic redundancy offers a robust solution for reliable and energy-efficient task scheduling.
  • The findings contribute to the realization of green computing through optimized scheduling in distributed environments.