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Towards the Automatic Detection of Efficient Computing Assets in a Heterogeneous Cloud Environment
Date Issued
2013-06-03
Date Available
2015-10-05T08:45:17Z
Abstract
In a heterogeneous cloud environment, the manual grading of computing assets is the first step in the process of configuring IT infrastructures to ensure optimal utilization of resources. Grading the efficiency of computing assets is however, a difficult, subjective and time consuming manual task. Thus, an automatic efficiency grading algorithm is highly desirable. In this paper, we compare the effectiveness of the different criteria used in the manual grading task for automatically determining the efficiency grading of a computing asset. We report results on a dataset of 1,200 assets from two different data centers in IBM Toronto. Our preliminary results show that electrical costs (associated with power and cooling) appear to be even more informative than hardware and age based criteria as a means of determining the efficiency grade of an asset. Our analysis also indicates that the effectiveness of the various efficiency criteria is dependent on the asset demographic of the data centre under consideration.
Sponsorship
Science Foundation Ireland
Other Sponsorship
Lero
Type of Material
Conference Publication
Publisher
Institute of Electrical and Electronic Engineers (IEEE)
Copyright (Published Version)
2013 IEEE
Language
English
Status of Item
Peer reviewed
Conference Details
IEEE 6th International Conference on Cloud Computing, Santa Clara Marriott, CA, USA, 28 June - 3 July, 2013
This item is made available under a Creative Commons License
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