Analysing Hadoop power consumption and impact on application QoS

Conejero, Javier, Rana, Omer Farooq ORCID: https://orcid.org/0000-0003-3597-2646, Burnap, Peter ORCID: https://orcid.org/0000-0003-0396-633X, Morgan, Jeffrey, Caminero, Blanca and Carrión, Carmen 2016. Analysing Hadoop power consumption and impact on application QoS. Future Generation Computer Systems 55 , pp. 213-223. 10.1016/j.future.2015.03.009

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Abstract

Energy efficiency is often identified as one of the key reasons for migrating to Cloud environments. It is stated that a data center hosting the Cloud environment is likely to achieve greater energy efficiency (at a reduced cost) compared to a local deployment. With increasing energy prices, it is also estimated that a large percentage of operational costs within a Cloud environment can be attributed to energy. In this work, we investigate and measure energy consumption of a number of virtual machines running the Hadoop system, over an OpenNebula Cloud. Our workload is based on sentiment analysis undertaken over Twitter messages. Our objective is to understand the tradeoff between energy efficiency and performance for such a workload. From our results we generalize and speculate on how such an analysis could be used as a basis to establish a Service Level Agreement (SLA) with a Cloud provider—especially where there is likely to be a high level of variability (both in performance and energy use) over multiple runs of the same application (at different times). Among the service level objectives that might be included in a SLA, Quality of Service (QoS) related metrics (i.e., latency) are one of the most challenging to support. This work provides some insight on the relationship between power consumption and QoS related metrics, describing how a combined consideration of these two metrics could be supported for a particular workload.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	Elsevier
ISSN:	0167-739X
Date of Acceptance:	9 March 2015
Last Modified:	15 Mar 2024 07:24
URI:	https://orca.cardiff.ac.uk/id/eprint/74136

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