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

被引：0

作者：

Inacio, Eduardo Camilo ^{[1
]}

Dantas, Mario A.R. ^{[1
]}

机构：

[1] Department of Informatics and Statistics (INE), Federal University of Santa Catarina (UFSC), Florianópolis, SC

来源：

International Journal of Networking and Virtual Organisations | 2014年 / 14卷 / 04期

关键词：

Big data; Cloud computing; Energy efficiency; High performance computing; HPC; Performance improvement; Workload characterisation;

D O I：

10.1504/IJNVO.2014.067878

中图分类号：

学科分类号：

摘要：

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. Copyright © 2014 Inderscience Enterprises Ltd.

引用

页码：299 / 318

页数：19

共 54 条

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