Paper Info
Title
Towards the Automatic Detection of Efficient Computing Assets in a Heterogeneous Cloud Environment
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.
Year
DOI
Venue
2013
10.1109/CLOUD.2013.136
IEEE CLOUD
Keywords
Field
DocType
different data center,efficient computing assets,various efficiency criterion,data centre,manual grading,manual grading task,different criterion,automatic detection,time consuming manual task,heterogeneous cloud environment,computing asset,automatic efficiency,efficiency grade,cloud computing
IBM,Grading (education),Computer science,Real-time computing,Data center,Distributed computing,Cloud computing
Conference
ISSN
Citations 
PageRank 
2159-6182
0
0.34
References 
Authors
2
5
Name
Order
Citations
PageRank
Jesus Omana Iglesias1184.06
Nicola Stokes21126.52
Anthony Ventresque310817.08
Liam Murphy481174.94
James Thorburn5264.11