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Energy efficiency in big data complex systems: a comprehensive survey of modern energy saving techniques

机译:大数据复杂系统中的能源效率:对现代节能技术的全面调查

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摘要

Abstract The growing need of computation and processing has led to the generation of data centers. These data centers are usually comprised of hundreds of thousands of servers and other components. This complicated arrangement of the systems lead to the adoption of complex systems. Complex systems prevail in our society as combination of lots of entities, e.g., immune system, human brain and ecosystems. The adoption and interaction of the entities is possible through nonlinear interactions. The interaction between the components of the complex system is carried out in distributed fashion. Big data which is comprised of thousands of machines is also considered to be a form of complex adaptive systems which makes use of large entities, components and nonlinear interactions with each other. The development of such a complex systems raises certain challenges. Apart from management, energy is the most concerned one which is the core discussion of this research. This paper, surveys the state of the art on modern tools, techniques, architectures and algorithms which has been proposed and deployed to achieve energy efficiency in big data over the period of 2007–2015. We group existing approaches aimed at achieving energy efficiency in the complex paradigm of big data. In this categorization, we aim to provide an easy and concise view of the underlined model adapted by each approach in the context of big data.
机译:摘要对计算和处理的日益增长的需求导致了数据中心的产生。这些数据中心通常由成千上万的服务器和其他组件组成。系统的这种复杂布置导致采用复杂系统。复杂的系统在我们的社会中盛行,是许多实体的组合,例如免疫系统,人脑和生态系统。实体的采用和交互可以通过非线性交互来实现。复杂系统的组件之间的交互以分布式方式进行。由数千台机器组成的大数据也被认为是复杂的自适应系统的一种形式,该系统利用了大型实体,组件和彼此之间的非线性交互。这样复杂的系统的开发提出了某些挑战。除了管理,能源是最受关注的能源,这是本研究的核心讨论。本文调查了已提出并部署的现代工具,技术,体系结构和算法的最新技术,以实现2007-2015年期间大数据的能源效率。我们将旨在实现大数据复杂范例中的能源效率的现有方法进行分组。在此分类中,我们旨在提供一种简单明了的大数据环境下每种方法所适应的带下划线模型的视图。

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