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Statistical model checking-based evaluation and optimization for cloud workflow resource allocation

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journal contribution
posted on 2020-01-01, 00:00 authored by Mingsong Chen, Saijie Huang, Xin Fu, Xiao LiuXiao Liu, Jifeng He
Due to the existence of resource variations, it is very challenging for Cloud workflow resource allocation strategies to guarantee a reliable Quality of Service (QoS). Although dozens of resource allocation heuristics have been developed to improve the QoS of Cloud workflow, it is hard to predict their performance under variations because of the lack of accurate modeling and evaluation methods. So far, there is no comprehensive approach that can quantitatively reason the capability of resource allocation strategies or enable the tuning of parameters to optimize resource allocation solutions under variations. To address the above problems, this paper proposes a novel framework that can evaluate and optimize resource allocation strategies effectively and quantitatively. By using the statistical model checker UPPAAL-SMC and supervised learning approaches, our framework can: i) conduct complex QoS queries on resource allocation instances considering resource variations; ii) make quantitative and qualitative comparisons among resource allocation strategies; iii) enable the tuning of parameters to improve the overall QoS; and iv) support the quick optimization of overall workflow QoS under customer requirements and resource variations. The experimental results demonstrate that our automated framework can support both the Service Level Agreement (SLA) negotiation and workflow resource allocation optimization efficiently.

History

Journal

IEEE transactions on cloud computing

Volume

8

Issue

2

Season

Apr-Jun

Pagination

443 - 458

Publisher

Institute of Electrical and Electronics Engineers

Location

Piscataway, N.J.

ISSN

2168-7161

eISSN

2372-0018

Language

eng

Publication classification

C1 Refereed article in a scholarly journal