Dynamic resource allocation for an energy efficient VM architecture for cloud computing

Alsadie, D, Tari, Z, Alzahrani, E and Zomaya, A 2018, 'Dynamic resource allocation for an energy efficient VM architecture for cloud computing', in Proceedings of the Australasian Computer Science Week Multiconference (ACSW 2018), Brisbane, Australia, 29 January - 2 February 2018, pp. 1-8.


Document type: Conference Paper
Collection: Conference Papers

Title Dynamic resource allocation for an energy efficient VM architecture for cloud computing
Author(s) Alsadie, D
Tari, Z
Alzahrani, E
Zomaya, A
Year 2018
Conference name ACSW 2018
Conference location Brisbane, Australia
Conference dates 29 January - 2 February 2018
Proceedings title Proceedings of the Australasian Computer Science Week Multiconference (ACSW 2018)
Publisher Association for Computing Machinery
Place of publication New York, United States
Start page 1
End page 8
Total pages 8
Abstract Minimizing power consumption is a vital consideration in the modern-day development of cloud computing. One of the major challenges reported in cloud computing is the consumption of power by computing resources due to improper allocation of resources over improperly sized virtual machines (VM). In spite of many efforts, the existing solutions are only able to meet the requirement for minimizing power consumption to a limited extent, due to their lack of optimized allocation of computing resources. The primary aim of the proposed work is to make effective use of the computing resources of the cloud for minimizing power consumption. It employs the concept of mapping appropriately sized VMs to a group of tasks in a data center, in order to reduce its power consumption. It involves the clustering of tasks on the basis of their computing requirements and finding a suitably sized VM with the required computing resources. The efficient use of computing resources on the basis of their actual requirements for a group of tasks helps to save a substantial amount of power. The proposed work is evaluated for its superiority over representational techniques using Google cloud traces as benchmark dataset. The experimental results showed an improvement of 8.42% in power consumption compared to representational techniques using fixed-sized VMs in the field. The proposed approach also achieves an improvement of 62% in the number of instances of VMs created for hosting the task workload, while maintaining a low task rejection rate.
Subjects Distributed and Grid Systems
Keyword(s) Cloud computing
Scheduling
Energy-Saving
Task Clustering
Virtualization
DOI - identifier 10.1145/3167918.3167952
Copyright notice © 2018 Association for Computing Machinery
ISBN 9781450354363
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