Title: Improving scheduling efficiency by probabilistic execution time model in cloud environments

Authors: Peng Xiao; Dongbo Liu; Kaijian Liang

Addresses: School of Computer and Communication, Hunan Institute of Engineering, Xiangtan City, 411104 Hunan Province, China ' School of Computer and Communication, Hunan Institute of Engineering, Xiangtan City, 411104 Hunan Province, China ' School of Application and Technology, Hunan Institute of Engineering, Xiangtan City, 411104 Hunan Province, China

Abstract: Recently, cloud computing has become a promising paradigm for various kinds of large-scale applications. Due to the unpredictable characteristics of resource availability and workload intensity, execution latency still drastically impairs the performances of cloud applications. In this paper, we model the execution latency by a probabilistic distribution and propose a general task execution model which can be used in most of scenarios. By using the proposed execution time model, cloud administrators can easily refine their resource management or implement some fine-grained task scheduling policies for cloud applications in various cases. Massive experiments are conducted in a real-world cloud platform, and the results indicate the proposed model can be used in many existing scheduling policies for improving the efficiency of task execution.

Keywords: cloud computing; resource virtualisation; virtual machine; task scheduling.

DOI: 10.1504/IJNVO.2018.093651

International Journal of Networking and Virtual Organisations, 2018 Vol.18 No.4, pp.307 - 322

Received: 05 Jul 2016
Accepted: 27 Sep 2016
Published online: 31 Jul 2018 *

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