Energy Aware Strategies for Scheduling Precedence Constrained Parallel Tasks in DVFS-enabled Cloud Datacenter

Articles in Press

Document Type : Original Article

Authors

1 Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran

2 Department of Computer Engineering, Babol Branch, Islamic Azad University, Babol, Iran

3 Department of Computer Engineering, Jouybar Branch, Islamic Azad University, Jouybar, Iran

Abstract

Energy management has been recently a major concern for those who work on cloud computing systems as it plays an important role in support the fast growth of data and computing centers. this paper a five-step reliability- and energy-aware scheduling algorithm called ERADCD; in this algorithm, duplication and clustering strategies and the DVFS technique are applied to a cloud center processor in which frequency and voltage are scaled dynamically. The ERADCD’s goals are the minimization of energy consumption and also meeting the task scheduling limit. Through the two initial steps, the proposed algorithm reduces the execution time and the energy consumption of the processors when performing the tasks in the DAG; this reduction is realized by intelligently combining the replication and clustering strategies. Then, at the third step, each task’s worst execution time is determined in each virtual machine at each frequency level. Then, the deadline for each task is specified and, in the fourth step, the tasks are assigned to the most suitable virtual machine by the DVFS technique. The main idea in the intelligent selection of the virtual machine by the DVFS technique is to use the long execution time of the tasks with the aim of decreasing the virtual machine’s frequency. The final step involves the development of a test dataset and testing different parameters on the DAG for the evaluation of the ERADCD’s effectiveness in comparison with other algorithms already existing in the literature.

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International Journal of Engineering

Articles in Press, Accepted Manuscript
Available Online from 09 April 2024

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