Power-Efficient Assignment of Virtual Machines to Physical Machines

Aroca, Jordi Arjona; Anta, Antonio Fernández; Mosteiro, Miguel A.; Thraves, Christopher; Wang, Lin

doi:10.1007/978-3-319-13464-2_6

Cited by 4 publications

(3 citation statements)

References 34 publications

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“…Within this range, the optimal algorithm may assign the VMs to one or both PMs. If the optimal algorithm assigns to one PM, Inequality (47) applies. Otherwise, the competitive ratio ρ is…”

Section: Theorem 12mentioning

confidence: 99%

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Power-efficient assignment of virtual machines to physical machines

Aroca

Anta

Mosteiro

et al. 2016

Future Generation Computer Systems

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h i g h l i g h t s • Formal definition of 4 versions of the Virtual Machine Assignment problem (VMA). • Study of hardness of the offline version. • Study of competitiveness of the online version. • Proposal of algorithms for the online version. a b s t r a c tMotivated by current trends in cloud computing, we study a version of the generalized assignment problem where a set of virtual processors has to be implemented by a set of identical processors. For literature consistency, we say that a set of virtual machines (VMs) is assigned to a set of physical machines (PMs). The optimization criterion is to minimize the power consumed by all the PMs. We term the problem Virtual Machine Assignment (VMA). Crucial differences with previous work include a variable number of PMs, that each VM must be assigned to exactly one PM (i.e., VMs cannot be implemented fractionally), and a minimum power consumption for each active PM. Such infrastructure may be strictly constrained in the number of PMs or in the PMs' capacity, depending on how costly (in terms of power consumption) it is to add a new PM to the system or to heavily load some of the existing PMs. Low usage or ample budget yields models where PM capacity and/or the number of PMs may be assumed unbounded for all practical purposes. We study four VMA problems depending on whether the capacity or the number of PMs is bounded or not. Specifically, we study hardness and online competitiveness for a variety of cases. To the best of our knowledge, this is the first comprehensive study of the VMA problem for this cost function.

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“…Within this range, the optimal algorithm may assign the VMs to one or both PMs. If the optimal algorithm assigns to one PM, Inequality (47) applies. Otherwise, the competitive ratio ρ is…”

Section: Theorem 12mentioning

confidence: 99%

“…The current pace of technology developments, and the continuous change in business requirements, may rapidly yield a given proprietary computational platform obsolete, oversized, or insuf-✩ A preliminary version of this work [47] was presented at the ARMS-CC 2014 workshop [48]. ficient.…”

Section: Introductionmentioning

confidence: 99%

Power-efficient assignment of virtual machines to physical machines

Aroca

Anta

Mosteiro

et al. 2016

Future Generation Computer Systems

Self Cite

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“…Section 5 discusses some practical issues and provides some useful insights regarding real implementation. For succinctness, many of the proofs are left to the full version of this paper in [10].…”

Section: Introductionmentioning

confidence: 99%

Power-efficient Assignment of Virtual Machines to Physical Machines

Aroca

Anta

Mosteiro

et al. 2013

Preprint

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Motivated by current trends in cloud computing, we study a version of the generalized assignment problem where a set of virtual processors has to be implemented by a set of identical processors. For literature consistency, we say that a set of virtual machines (VMs) is assigned to a set of physical machines (PMs). The optimization criteria is to minimize the power consumed by all the PMs. We term the problem Virtual Machine Assignment (VMA). Crucial differences with previous work include a variable number of PMs, that each VM must be assigned to exactly one PM (i.e., VMs cannot be implemented fractionally), and a minimum power consumption for each active PM. Such infrastructure may be strictly constrained in the number of PMs or in the PMs' capacity, depending on how costly (in terms of power consumption) it is to add a new PM to the system or to heavily load some of the existing PMs. Low usage or ample budget yields models where PM capacity and/or the number of PMs may be assumed unbounded for all practical purposes. We study four VMA problems depending on whether the capacity or the number of PMs is bounded or not. Specifically, we study hardness and online competitiveness for a variety of cases. To the best of our knowledge, this is the first comprehensive study of the VMA problem for this cost function.

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Accelerated computation of the genetic algorithm for energy-efficient virtual machine placement in data centers

Ding

Tian²,

Wang³

et al. 2022

Neural Comput & Applic

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Energy efficiency is a critical issue in the management and operation of cloud data centers, which form the backbone of cloud computing. Virtual machine (VM) placement has a significant impact on energy-efficiency improvement for virtualized data centers. Among various methods to solve the VM-placement problem, the genetic algorithm (GA) has been well accepted for the quality of its solution. However, GA is also computationally demanding, particularly in the computation of its fitness function. This limits its application in large-scale systems or specific scenarios where a fast VM-placement solution of good quality is required. Our analysis in this paper reveals that the execution time of the standard GA is mostly consumed in the computation of its fitness function. Therefore, this paper designs a data structure extended from a previous study to reduce the complexity of the fitness computation from quadratic to linear one with respect to the input size of the VM-placement problem. Incorporating with this data structure, an alternative fitness function is proposed to reduce the number of instructions significantly, further improving the execution-time performance of GA. Experimental studies show that our approach achieves 11 times acceleration of GA computation for energy-efficient VM placement in large-scale data centers with about 1500 physical machines in size.

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Power-Efficient Assignment of Virtual Machines to Physical Machines

Cited by 4 publications

References 34 publications

Power-efficient assignment of virtual machines to physical machines

Power-efficient assignment of virtual machines to physical machines

Power-efficient Assignment of Virtual Machines to Physical Machines

Accelerated computation of the genetic algorithm for energy-efficient virtual machine placement in data centers

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