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

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 s...

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Vydáno v:	Computer science review Ročník 52; s. 100620
Hlavní autoři:	Zakarya, Muhammad, Khan, Ayaz Ali, Qazani, Mohammed Reza Chalak, Ali, Hashim, Al-Bahri, Mahmood, Khan, Atta Ur Rehman, Ali, Ahmad, Khan, Rahim
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Inc 01.05.2024
Témata:	Clouds Datacenters Energy efficiency Resource management Datacenters Energy efficiency Clouds Resource management Performance
ISSN:	1574-0137
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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 ∼ 200TWh of energy worldwide. However, due to their ever-increasing energy demand which causes CO2 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. •A brief description of resource management methods for virtualisation technologies.•Various resource allocation and migration techniques are illustrated with algorithms.•Analysis of energy, performance, and cost aware methods and their mapping to taxonomies.•To enhance performance of computation with increased data traffic, methods are discussed.•Further unexplored gaps that should be accomplished in future research are presented.
AbstractList	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 CO2 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. •A brief description of resource management methods for virtualisation technologies.•Various resource allocation and migration techniques are illustrated with algorithms.•Analysis of energy, performance, and cost aware methods and their mapping to taxonomies.•To enhance performance of computation with increased data traffic, methods are discussed.•Further unexplored gaps that should be accomplished in future research are presented.
ArticleNumber	100620
Author	Zakarya, Muhammad Al-Bahri, Mahmood Ali, Ahmad Ali, Hashim Khan, Atta Ur Rehman Khan, Rahim Khan, Ayaz Ali Qazani, Mohammed Reza Chalak
Author_xml	– sequence: 1 givenname: Muhammad orcidid: 0000-0001-7070-6699 surname: Zakarya fullname: Zakarya, Muhammad email: mohd.zakarya@awkum.edu.pk, MZakarya@su.edu.om organization: Faculty of Computing and Information Technology, Sohar University, Oman – sequence: 2 givenname: Ayaz Ali surname: Khan fullname: Khan, Ayaz Ali email: ayazkhan@ulm.edu.pk organization: Department of Computer Science, University of Lakki Marwat, Pakistan – sequence: 3 givenname: Mohammed Reza Chalak surname: Qazani fullname: Qazani, Mohammed Reza Chalak email: MRCQazani@su.edu.om organization: Faculty of Computing and Information Technology, Sohar University, Oman – sequence: 4 givenname: Hashim surname: Ali fullname: Ali, Hashim email: hashimali@awkum.edu.pk organization: Department of Computer Science, Abdul Wali Khan University, Mardan, Pakistan – sequence: 5 givenname: Mahmood surname: Al-Bahri fullname: Al-Bahri, Mahmood email: MBahri@su.edu.om organization: Faculty of Computing and Information Technology, Sohar University, Oman – sequence: 6 givenname: Atta Ur Rehman surname: Khan fullname: Khan, Atta Ur Rehman email: a.khan@ajman.ac.ae organization: College of Engineering and Information Technology, Ajman University, United Arab Emirates – sequence: 7 givenname: Ahmad surname: Ali fullname: Ali, Ahmad email: ahmadali@szu.edu.cn organization: College of Computer Science and Software Engineering, Shenzhen University, China – sequence: 8 givenname: Rahim surname: Khan fullname: Khan, Rahim email: rahimkhan@awkum.edu.pk organization: Department of Computer Science, Abdul Wali Khan University, Mardan, Pakistan
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Snippet	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...
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