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An energy-aware QoS enhanced method for service computing across clouds and data centers

Version 2 2024-06-05, 05:25
Version 1 2016-06-30, 17:33
conference contribution
posted on 2024-06-05, 05:25 authored by W Dou, X Xu, S Meng, S Yu
QoS plays a key role in evaluating a service or a service composition plan across clouds and data centers. Currently, the energy cost of a service's execution is not covered by the QoS framework, and a service's price is often fixed during its execution. However, energy consumption has a great contribution in determining the price of a cloud service. As a result, it is not reasonable if the price of a cloud service is calculated with a fixed energy consumption value, if part of a service's energy consumption could be saved during its execution. Taking advantage of the dynamic energy-Aware optimal technique, a QoS enhanced method for service computing is proposed, in this paper, through virtual machine (VM) scheduling. Technically, two typical QoS metrics, i.e., the price and the execution time are taken into consideration in our method. Moreover, our method consists of two dynamic optimal phases. The first optimal phase aims at dynamically benefiting a user with discount price by transparently migrating his or her task execution from a VM located at a server with high energy consumption to a low one. The second optimal phase aims at shortening task's execution time, through transparently migrating a task execution from a VM to another one located at a server with higher performance. Experimental evaluation upon large scale service computing across clouds demonstrates the validity of our method.

History

Pagination

80-87

Location

Yangzhou, China

Start date

2015-10-30

End date

2015-11-01

ISBN-13

9781467385374

Language

eng

Publication classification

E Conference publication, E1 Full written paper - refereed

Copyright notice

2015, IEEE

Editor/Contributor(s)

[Unknown]

Title of proceedings

CBD 2015: Proceedings of the Advanced Cloud and Big Data 2015 International Conference

Event

Advanced Cloud and Big Data. International Conference (3rd : 2015 : Yangzhou, China)

Publisher

IEEE

Place of publication

Piscataway, N.J.

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