Sustainable computing across datacenters: A review of enabling models and techniques

Abstract

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 $\sim$ 200TWh of energy worldwide. However, due to their ever-increasing energy demand which causes ${\mathrm{CO}}_2$ 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.