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

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  • Eduardo Camilo Inacio
  • Mario A.R. Dantas

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.

Suggested Citation

  • Eduardo Camilo Inacio & Mario A.R. Dantas, 2014. "A survey into performance and energy efficiency in HPC, cloud and big data environments," International Journal of Networking and Virtual Organisations, Inderscience Enterprises Ltd, vol. 14(4), pages 299-318.
    • Handle: RePEc:ids:ijnvor:v:14:y:2014:i:4:p:299-318
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    Cited by:

    1. Youssef Tabsh & Vida Davidavičienė, 2019. "Effects of ICT’s on energy management systems," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 6(4), pages 2194-2206, June.
    2. Khokhriakov, Semyon & Manumachu, Ravi Reddy & Lastovetsky, Alexey, 2020. "Multicore processor computing is not energy proportional: An opportunity for bi-objective optimization for energy and performance," Applied Energy, Elsevier, vol. 268(C).

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