Title: A survey into performance and energy efficiency in HPC, cloud and big data environments

Authors: Eduardo Camilo Inacio; Mario A.R. Dantas

Addresses: Department of Informatics and Statistics (INE), Federal University of Santa Catarina (UFSC), Florianópolis, SC, Brazil ' Department of Informatics and Statistics (INE), Federal University of Santa Catarina (UFSC), Florianópolis, SC, Brazil

Abstract: The growing demand for performance observed in many organisations can still be considered the main motivator for the evolution of high performance computing and, more recently, cloud environments. Part of this demand regards the need to deal with large and complex datasets, called big data. Performance improvement in such environments begins to be limited by energy consumption. Workload characterisation is a well-known approach to reproducing systems' behaviour. However, there are several methodologies, techniques and parameters that can be considered for a workload characterisation. As a result, we present a differentiated survey on workload characterisation focusing on performance and energy efficiency improvement on HPC, cloud and big data environments. After an extensive review and classification of research works, our study indicates that around 56.4% of the papers reviewed offer contributions to performance and energy efficiency improvement, and the growing interest in this subject has a rate of 7.86% per year.

Keywords: workload characterisation; performance improvement; energy efficiency; high performance computing; HPC; cloud computing; big data; energy consumption; literature review.

DOI: 10.1504/IJNVO.2014.067878

International Journal of Networking and Virtual Organisations, 2014 Vol.14 No.4, pp.299 - 318

Received: 26 Jul 2014
Accepted: 10 Sep 2014
Published online: 13 Mar 2015 *

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