Next Article in Journal
Real-Time Detection of DoS Attacks in IEEE 802.11p Using Fog Computing for a Secure Intelligent Vehicular Network
Previous Article in Journal
Multiple Modulation Strategy of Flying Capacitor DC/DC Converter
Article Menu

Export Article

Open AccessArticle

An Energy Efficient Task Scheduling Strategy in a Cloud Computing System and its Performance Evaluation using a Two-Dimensional Continuous Time Markov Chain Model

1
School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
2
Department of Intelligence and Informatics, Konan University, Kobe 658-8501, Japan
3
Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(7), 775; https://doi.org/10.3390/electronics8070775
Received: 23 April 2019 / Revised: 27 June 2019 / Accepted: 9 July 2019 / Published: 11 July 2019
(This article belongs to the Section Networks)
  |  
PDF [678 KB, uploaded 11 July 2019]
  |  

Abstract

With ongoing energy shortages and rises in greenhouse emissions worldwide, increasing academic attention is being turned towards ways to improve the efficiency and sustainability of cloud computing. In this paper, we present a performance analysis and a system optimization of a cloud computing system with an energy efficient task scheduling strategy directed towards satisfying the service level agreement of cloud users while at the same time improving the energy efficiency in cloud computing system. In this paper, we propose a novel energy-aware task scheduling strategy based on a sleep-delay timer and a waking-up threshold. To capture the stochastic behavior of tasks with the proposed strategy, we establish a synchronous vacation queueing system combining vacation-delay and N-policy. Taking into account the total number of tasks and the state of the physical machine (PM), we construct a two-dimensional continuous-time Markov chain (CTMC), and produce an infinitesimal generator. Moreover, by using the geometric-matrix solution method, we analyze the queueing model in the steady state, and then, we derive the system performance measures in terms of the average sojourn time and the energy conservation level. Furthermore, we conduct system experiments to investigate the proposed strategy and validate the system model according to performance measures. Statistical results show that there is a compromise between the different performance measures when setting strategy parameters. By combining different performance measures, we develop a cost function for the system optimization. Finally, by dynamically adjusting the crossover probability and the mutation probability, and initializing the individuals with chaotic equations, we present an improved genetic algorithm to jointly optimize the sleep parameter, the sleep-delay parameter and the waking-up threshold. View Full-Text
Keywords: cloud computing system; task scheduling; energy conservation; sleep-delay timer; waking-up threshold; Markov chain; cost function; intelligent searching algorithm; joint optimization cloud computing system; task scheduling; energy conservation; sleep-delay timer; waking-up threshold; Markov chain; cost function; intelligent searching algorithm; joint optimization
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Zhao, W.; Wang, X.; Jin, S.; Yue, W.; Takahashi, Y. An Energy Efficient Task Scheduling Strategy in a Cloud Computing System and its Performance Evaluation using a Two-Dimensional Continuous Time Markov Chain Model. Electronics 2019, 8, 775.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Electronics EISSN 2079-9292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top