Title: An adaptive load management service in federated cloud platforms

Authors: Peng Xiao; Yongjian Li

Addresses: Department of Computer Science, Hunan Institute of Engineering, No.88 Fu-Xing Road, Xiangtan City 411104, China ' College of Electrical and Information Engineering, Hunan Institute of Engineering, Xiangtan 411104, China

Abstract: Recently, cloud platforms have become a promising distributed paradigm for providing flexible computing capability to various high-end applications. Unfortunately, traditionally load management service can not meet the requirements of cloud platforms due to its elastic resource provision characteristic. In this paper, we present a novel adaptive load management service which applies the fuzzy inference model to implement the load-dispatching rules. In this way, we can quantitatively describe the highly nonlinear workload patterns through a small number of simple rules and then make robust load-dispatching decisions. Extensive experiments are conducted in a campus-oriented federated cloud platform to investigate the effectiveness and performance of the proposed load management service, and the results indicate that it can precisely capture the runtime characteristics of workloads and significantly improve the performance of load management service in large-scale cloud environments.

Keywords: loud computing; resource virtualisation; virtual machine; scheduling algorithm.

DOI: 10.1504/IJSTM.2018.093346

International Journal of Services Technology and Management, 2018 Vol.24 No.4, pp.374 - 390

Received: 19 Dec 2016
Accepted: 22 Feb 2017
Published online: 25 Jul 2018 *

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