Towards the Automatic Detection of Efficient Computing Assets in a Heterogeneous Cloud Environment

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Title: Towards the Automatic Detection of Efficient Computing Assets in a Heterogeneous Cloud Environment
Authors: Iglesias, Jesus Omana
Stokes, Nicola
Ventresque, Anthony
Murphy, Liam, B.E.
Thorburn, James
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Date: 3-Jun-2013
Online since: 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.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: Institute of Electrical and Electronic Engineers (IEEE)
Copyright (published version): 2013 IEEE
Keywords: Asset utilization costAsset efficiency grading
DOI: 10.1109/CLOUD.2013.136
Language: en
Status of Item: Peer reviewed
Conference Details: IEEE 6th International Conference on Cloud Computing, Santa Clara Marriott, CA, USA, 28 June - 3 July, 2013
Appears in Collections:Computer Science Research Collection
PEL Research Collection

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