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Variation-aware resource allocation evaluation for cloud workflows using statistical model checking

conference contribution
posted on 2015-01-01, 00:00 authored by S Huang, M Chen, Xiao LiuXiao Liu, D Du, X Chen
Aiming at minimizing service operating costs and SLA (Service Level Agreement) violations, various resource allocation strategies have been investigated to support Cloud service providers' decision making. However, due to the service execution time variation, traditional optimal resource allocation strategies cannot achieve the best performance in practice. To address this problem, we propose an automated variation-aware evaluation framework for resource allocation strategies based on statistical model checker UPPAAL-SMC. Our framework can systematically evaluate the performance of resource allocation strategies under variations, and conduct complex queries on the quality of service. The experimental results show that our framework can not only filter inferior solutions efficiently, but also can enable the tuning of requirement constraints. Since our approach can be fully automated, the human efforts in resource allocation strategy evaluation can be significantly reduced.

History

Event

Big Data and Cloud COmputing. IEEE International Conference (4th : 2014 : Sydney, New South Wales)

Pagination

201 - 208

Publisher

IEEE

Location

Sydney, New South Wales

Place of publication

Piscataway, N.J.

Start date

2014-12-03

End date

2014-12-05

ISBN-13

9781479967193

Language

eng

Publication classification

E Conference publication; E1.1 Full written paper - refereed

Copyright notice

2014, IEEE

Title of proceedings

BdCloud 2014 : Proceedings of the 4th IEEE International Conference on Big Data and Cloud Computing