Operating Policies for Energy Efficient Dynamic Server Allocation

Nguyen, Thai Ha; Forshaw, Matthew; Thomas, Nigel

doi:10.1016/j.entcs.2015.10.025

Cited by 7 publications

(5 citation statements)

References 11 publications

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“…In our previous work [1], we presented a PEPA model that considers a variant of the high/low policy introduced in [15,19]. The maximum number of jobs is bounded at $N$.…”

Section: Related Workmentioning

confidence: 99%

“…It is worth noting that Slegers et al [21] proposed six policies and we have only considered three in this paper. Furthermore Nguyen et al [15] considered the case where servers may breakdown when powering up or down. Modelling these additional cases, as well as others from the literature, in PEPA would enable more comparisons to be made.…”

Section: Conclusion and Further Workmentioning

confidence: 99%

“…The cost of energy is one of the many challenges facing largescale computing. According to [15], data centre owners now expect to spend more capital on energy than their IT infrastructure, which currently contributes more to the total cost of ownership (TOC). The Environmental Protection Agency (EPA) has issued a report to the U.S. Congress about the energy efficiency of servers and data centres.…”

Section: Introductionmentioning

confidence: 99%

“…[20,21] considered the problem of finding the optimal share of servers to different services under variable load in order to minimise a performancebased cost function. This paper is based on the work of Slegers et al [19] and Nguyen et al [15]. It is focused on the notion that servers can be powered off and on according to demand in order to avoid the non-trivial energy requirements of idle servers.…”

Section: Introductionmentioning

confidence: 99%

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Modelling Energy Efficient Server Management Policies in PEPA

Alssaiari

Gining

Thomas

2017

Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion

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Power inefficiency has become a major concern for large scale computing providers. In this paper, we model turning servers on and off to keep a balance between capacity and energy saving. Several heuristic-based switching policies are introduced with a view to balance the cost between power saving and performance. Models are specified using a Markovian process algrebra, which allows explicit representation of system behaviour and facilitates numerical analysis using the supporting tools.

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“…In our previous work [1], we presented a PEPA model that considers a variant of the high/low policy introduced in [15,19]. The maximum number of jobs is bounded at $N$.…”

Section: Related Workmentioning

confidence: 99%

Section: Conclusion and Further Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modelling Energy Efficient Server Management Policies in PEPA

Alssaiari

Gining

Thomas

2017

Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion

Self Cite

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“…The high energy consumption problem is becoming increasingly essential due to cloud computing technology and the development of big numbers of data centers fast developments. The information center's quality and effectiveness can be expressed in terms of the quantity of electrical power supplied [6]. Cloud computing involves a large number of data centers spread around the globe.…”

Section: Introductionmentioning

confidence: 99%

Energy-efficient Scheduling of Cloud Data Center Servers

Kumar¹,

Rani²

2019

IJITEE

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Data center is a cost-effective infrastructure to store large data volumes and host large-scale service applications. Providers of cloud computing services are deploying data centers worldwide quickly. With lots of servers and switches. These data centers consume substantial quantities of energy, which contributes to high operating costs. Optimizing server and network energy consumption in information centers can therefore decrease operating costs. In a data center, power utilization is chiefly because of servers, network devices, and cooling systems, an effective energy-saving strategy is to consolidate computing and communication into fewer servers and network devices and then power off as many unneeded servers and network devices as possible. A new method of reducing the energy utilization of computer systems and networks in data centers while meeting the requirements of the cloud tenants for quality of service (QoS) is proposed here in this paper.

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