Reliability-aware task scheduling for energy efficiency on heterogeneous multiprocessor systems

Zexi Deng, Dunqian Cao, Hong Shen, Zihan Yan, Huimin Huang

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

Recent studies mainly focus on high performance or low power consumption for task scheduling on heterogeneous multiprocessor systems (HMSs). Dynamic voltage and frequency scaling (DVFS) is an important energy reduction technique, which adjusts the voltage and frequency of the processor while the task is executing. However, some studies have shown that reducing the voltage of processor increases the transient failure rate, which reduces system reliability. In this paper, we aim at addressing the scheduling problem of optimizing energy under makespan and reliability constraints on HMSs with DVFS. We first propose an improved whale optimization algorithm (WOA) deploying opposition-based learning and individual selection strategy, which can balance the exploration and exploitation ability. To maintain population diversity, we then apply a constrained rank-based method which retains some infeasible individuals in the population. Finally, we reschedule the Critical Path Nodes (CPNs) to further improve the performance of improved WOA. The main difference between our work and most previous works is that we study a new scheduling problem, and utilize an improved WOA algorithm integrating with rescheduling CPNs and a constrained rank-based method. Extensive experiments are conducted to evaluate our proposed algorithm, and the evaluation results show that our proposed algorithm is compelling in comparison with the state-of-the-art algorithms.

Original languageEnglish
Pages (from-to)11643-11681
Number of pages39
JournalJournal of Supercomputing
Volume77
Issue number10
DOIs
Publication statusPublished - Oct 2021
Externally publishedYes

Keywords

  • Heterogeneous multiprocessor systems
  • Opposition-based learning
  • Task scheduling
  • Whale optimization algorithm

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