The growth rate in big data and internet of things (IoT) is far exceeding the computer performance rate at which modern processors can compute on the massive amount of data. The cluster and cloud technologies enriched by machine learning applications had significantly helped in performance growths subject to the underlying network performance. Computer systems have been studied for improvement in performance, driven by user’s applications demand, in the past few decades, particularly from 1990 to 2010. By the mid of 2010 to 2023, albeit parallel and distributed computing was omnipresent, but the total performance improvement rate of a single computing core had significantly reduced. Similarly, from 2010 to 2023, our digital world of big data and IoT has considerably increased from 1.2 Zettabytes (i.e., sextillion bytes) to approximately 120 zettabytes. Moreover, in 2022 cloud datacenters consumed 200TWh of energy worldwide. However, due to their ever-increasing energy demand which causes emissions, over the past years the focus has shifted to the design of architectures, software, and in particular, intelligent algorithms to compute on the data more efficiently and intelligently. The energy consumption problem is even greater for large-scale systems that involve several thousand servers. Combining these fears, cloud service providers are presently facing more challenges than earlier because they fight to keep up with the extraordinary network traffic being produced by the world’s fast-tracked move to online due to global pandemics. In this paper, we deliberate the energy consumption and performance problems of large-scale systems and present several taxonomies of energy and performance aware methodologies. We debate over the energy and performance efficiencies, both, which make this study different from those previously published in the literature. Important research papers have been surveyed to characterise and recognise crucial and outstanding topics for further research. We deliberate numerous state-of-the-art methods and algorithms, stated in the literature, that claim to advance the energy efficiency and performance of large-scale computing systems, and recognise numerous open challenges.

中文翻译：

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跨数据中心的可持续计算：支持模型和技术回顾

大数据和物联网 (IoT) 的增长速度远远超过了现代处理器对海量数据进行计算的计算机性能速度。机器学习应用丰富的集群和云技术极大地帮助了底层网络性能的性能增长。在过去的几十年里，特别是从1990年到2010年，计算机系统在用户应用需求的推动下，一直在研究性能的提高。到2010年中期到2023年，虽然并行和分布式计算已经无所不在，但总的性能提升率单个计算核心的数量显着减少。同样，从 2010 年到 2023 年，我们的大数据和物联网数字世界已从 1.2 泽字节（即 60 亿字节）大幅增加到大约 120 泽字节。此外，到 2022 年，全球云数据中心消耗的能源为 200TWh。然而，由于能源需求不断增加而导致排放，过去几年，重点已转向架构、软件设计，特别是智能算法的设计，以更高效、更智能地计算数据。对于涉及数千台服务器的大型系统来说，能源消耗问题更加严重。综合这些担忧，云服务提供商目前面临着比以前更多的挑战，因为他们努力跟上由于全球流行病而导致世界快速转向在线所产生的巨大网络流量。在本文中，我们讨论了大型系统的能耗和性能问题，并提出了几种能源和性能感知方法的分类法。我们对能量和性能效率进行了争论，这使得这项研究与之前发表的文献不同。我们对重要的研究论文进行了调查，以描述和识别进一步研究的关键和突出主题。我们审议了文献中所述的许多最先进的方法和算法，这些方法和算法声称可以提高大规模计算系统的能源效率和性能，并认识到许多开放的挑战。