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EATSDCD: A green energy-aware scheduling algorithm for parallel task-based application using clustering, duplication and DVFS technique in cloud datacenters

Barzegar, B ; Sharif University of Technology | 2019

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  1. Type of Document: Article
  2. DOI: 10.3233/JIFS-171927
  3. Publisher: IOS Press , 2019
  4. Abstract:
  5. Energy consumption and performance metrics have become critical issues for scheduling parallel task-based applications in high-performance computing systems such as cloud datacenters. The duplication and clustering strategy, as well as Dynamic Voltage Frequency Scaling (DVFS) technique, have separately been concentrated on reducing energy consumption and optimizing performance parameters such as throughput and makespan. In this paper, a dual-phase algorithm called EATSDCD which is an energy efficient time aware has been proposed. The algorithm uses the combination of duplication and clustering strategies to schedule the precedence-constrained task graph on datacenter processors through DVFS. The first phase focuses on a smart combination of duplication and clustering strategy to reduce makespan and energy consumed by processors in an effort to execute Directed Acyclic Graph (DAG) while satisfying the throughput constraint. The main idea behind EATSDCD intended to minimize energy consumption in the second phase. After determining the critical path and specifying a set of dependent tasks in non-critical paths, the slack time for each task in non-critical paths was distributed among all dependent tasks in that path. Then, the frequency of DVFS-enabled processors is scaled down to execute non-critical tasks as well as idle and communication phases, without extending the execution time of tasks. Finally, a testbed is developed and different parameters are tested on the randomly generated DAG to evaluate and illustrate the effectiveness of EATSDCD. It was also compared against duplication and clustering-based algorithms and DVFS-based algorithms. In terms of energy consumption and makespan, the results show that our proposed algorithm can save up to 8.3% and 20% energy compared against Power Aware List-based Scheduling (PALS) and Power Aware Task Clustering (PATC) algorithms, respectively. Furthermore, there is 16% improvement over Parallel Pipeline Latency Optimization (PaPilo) algorithm with Encur = 1.2Enmin(G). In comparison with Reliability Aware Scheduling with Duplication (RASD) algorithm, the execution time has been reduced in heterogeneous environments. © 2019 IOS Press and the authors. All rights reserved
  6. Keywords:
  7. Cloud data centers ; Dynamic voltage and frequency scaling (DVFS) ; Energy consumption ; Slack time ; Throughput ; Cluster analysis ; Directed graphs ; Dynamic frequency scaling ; Energy efficiency ; Energy utilization ; Green computing ; Parallel processing systems ; Scheduling ; Scheduling algorithms ; Voltage scaling ; Directed acyclic graph (DAG) ; Dynamic voltage and frequency scaling ; Dynamic voltage frequency scaling ; High performance computing systems ; Reducing energy consumption ; Task duplication ; Clustering algorithms
  8. Source: Journal of Intelligent and Fuzzy Systems ; Volume 36, Issue 6 , 2019 , Pages 5135-5152 ; 10641246 (ISSN)
  9. URL: https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs171927