Toward sustainable data centers: a comprehensive energy management strategy

Guitart, Jordi

doi:10.1007/s00607-016-0501-1

Cited by 30 publications

(18 citation statements)

References 58 publications

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“…However, research has indicated that sustainable data centers can be achieved only upon the adoption of a system-wide procedure that involves the holistic management of different data center components. Guitart ( 2017 ) proposed a comprehensive management strategy for sustainable data centers that involves introducing energy as a driving force for operation programs. Recent research on the design and architecture of green data centers has mainly focused on the improvement of data center building design, computer room design, floor layout, the cooling and refrigeration system, the electrical system, and the IT equipment.…”

Section: Literature Reviewmentioning

confidence: 99%

“… Data center level Energy consumption per unit area Quotient of the energy consumption of a data center and the total data center area. Haywood et al 2012 ; Daim et al 2013 ; Woodruff et al 2014 ; Oró et al 2015 ; Shuja et al 2016 ; Guitart 2017 ; Sheme et al 2018 ; Kwon 2020 Data center location Sufficient power supply capacity, low price of electricity supply, and low annual average temperature. Waste heat utilization Resulting data center collects and reuses waste heat.…”

Section: Literature Reviewmentioning

confidence: 99%

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Evaluating the sustainability of big data centers using the analytic network process and fuzzy TOPSIS

Yang

2021

Environ Sci Pollut Res

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The big data revolution has created data center sustainability problems, whose solutions require the consideration of environmental factors. The purpose of this study is to establish a big data center sustainability evaluation index and provide guidance for sustainable data center construction. This research formulated a big data center sustainability evaluation model that integrates multiple-criteria decision-making methods based on the analytic network process and fuzzy technique for order preference by similarity to an ideal solution (TOPSIS). Furthermore, a case study was used to examine the proposed model. The refrigeration system, layout and ventilation, data center location, data volume, and server power consumption are the five most crucial factors in determining the sustainability level of a big data center. The areas that require further development are the balancing of tasks on different IT equipment, renewable energy use, and waste heat utilization. This research provides a method or guide that can be used by managers when they build new big data centers or upgrade and optimize existing big data centers to make them more sustainable. This study is the first to assess the sustainability of a big data center according to multiple criteria decision-making methods, in which fuzzy theory is applied to evaluate the imprecise and subjective judgments of decision-makers. This study provides a systematic evaluation framework that is based on qualitative and quantitative criteria and comprises the four factors of big data level, equipment level, room level, and data center level. Big data is new oil, but it is not clean oil. It is both a vital driver of economic growth and a source of environmental damage. We need to ensure that big data centers are run in a sustainable way.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Evaluating the sustainability of big data centers using the analytic network process and fuzzy TOPSIS

Yang

2021

Environ Sci Pollut Res

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“…It is very challenging to schedule provisioned resources for workload execution and maintain reliability and sustainability of cloud service simultaneously [6] [52]. Cloud resource scheduling is a tedious task due to the problem of finding the best match of resource-workload pair based on the user QoS requirements [31] [39] [46].…”

Section: Resource Provisioning and Schedulingmentioning

confidence: 99%

Holistic resource management for sustainable and reliable cloud computing: An innovative solution to global challenge

Gill

Garraghan

Stankovski

et al. 2019

Journal of Systems and Software

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Minimizing the energy consumption of servers within cloud computing systems is of upmost importance to cloud providers towards reducing operational costs and enhancing service sustainability by consolidating services onto fewer active servers. Moreover, providers must also provision high levels of availability and reliability, hence cloud services are frequently replicated across servers that subsequently increases server energy consumption and resource overhead. These two objectives can present a potential conflict within cloud resource management decision making that must balance between service consolidation and replication to minimize energy consumption whilst maximizing server availability and reliability, respectively. In this paper, we propose a cuckoo optimization-based energy-reliability aware resource scheduling technique (CRUZE) for holistic management of cloud computing resources including servers, networks, storage, and cooling systems. CRUZE clusters and executes heterogeneous workloads on provisioned cloud resources and enhances the energy-efficiency and reduces the carbon footprint in datacenters without adversely affecting cloud service reliability. We evaluate the effectiveness of CRUZE against existing stateof-the-art solutions using the CloudSim toolkit. Results indicate that our proposed technique is capable of reducing energy consumption by 20.1% whilst improving reliability and CPU utilization by 17.1% and 15.7% respectively without affecting other Quality of Service parameters.

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“…Garg and Buyya [80] presented a survey on energy-efficient techniques for cloud datacenters for green cloud computing. A broad review of energy efficiency of the cooling and power supply subsystems is presented by Guitart [87]. Hameed et al [46] reviewed the energy efficient resource allocation techniques for cloud computing systems.…”

Section: Related Surveys and Our Workmentioning

confidence: 99%

A Taxonomy and Future Directions for Sustainable Cloud Computing

2018

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The cloud computing paradigm offers on-demand services over the Internet and supports a wide variety of applications. With the recent growth of Internet of Things (IoT) based applications the usage of cloud services is increasing exponentially. The next generation of cloud computing must be energy-efficient and sustainable to fulfil the end-user requirements which are changing dynamically. Presently, cloud providers are facing challenges to ensure the energy efficiency and sustainability of their services. The usage of large number of cloud datacenters increases cost as well as carbon footprints, which further effects the sustainability of cloud services. In this paper, we propose a comprehensive taxonomy of sustainable cloud computing. The taxonomy is used to investigate the existing techniques for sustainability that need careful attention and investigation as proposed by several academic and industry groups. Further, the current research on sustainable cloud computing is organized into several categories: application design, sustainability metrics, capacity planning, energy management, virtualization, thermal-aware scheduling, cooling management, renewable energy and waste heat utilization. The existing techniques have been compared and categorized based on the common characteristics and properties. A conceptual model for sustainable cloud computing has been proposed along with discussion on future research directions.

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Toward sustainable data centers: a comprehensive energy management strategy

Cited by 30 publications

References 58 publications

Evaluating the sustainability of big data centers using the analytic network process and fuzzy TOPSIS

Evaluating the sustainability of big data centers using the analytic network process and fuzzy TOPSIS

Holistic resource management for sustainable and reliable cloud computing: An innovative solution to global challenge

A Taxonomy and Future Directions for Sustainable Cloud Computing

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