Comparing VM-Placement Algorithms for On-Demand Clouds

Mills, Kevin L.; Filliben, James J.; Dabrowski, Christopher

doi:10.1109/cloudcom.2011.22

Cited by 162 publications

(116 citation statements)

References 23 publications

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“…There are different policies existing for virtual machine placement. Mills et al [7] discuss about 3 policies. In first fit policy virtual machines are allocated on the first host with sufficient available resources.…”

Section: Related Workmentioning

confidence: 99%

A Combined Bin Packing Vm Allocation and Minimum Loaded Vm Migration Approach for Load Balancing in Iaas Cloud Datacenters

Arya¹

2015

IJRET

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Section: Related Workmentioning

confidence: 99%

A Combined Bin Packing Vm Allocation and Minimum Loaded Vm Migration Approach for Load Balancing in Iaas Cloud Datacenters

Arya¹

2015

IJRET

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“…The VM type and the number of VMs requested by users depend on the user type (bag-of-tasks or web-requests), and are based on the related probabilities. The VM type related probability is shown in the last column of Table 4 and is derived from the work by Mills et al [21].…”

Section: Performance Evaluationmentioning

confidence: 99%

Energy and Carbon-Efficient Placement of Virtual Machines in Distributed Cloud Data Centers

Khosravi

Garg

Buyya

2013

Euro-Par 2013 Parallel Processing

101

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Abstract. Due to the increasing use of Cloud computing services and the amount of energy used by data centers, there is a growing interest in reducing energy consumption and carbon footprint of data centers. Cloud data centers use virtualization technology to host multiple virtual machines (VMs) on a single physical server. By applying efficient VM placement algorithms, Cloud providers are able to enhance energy efficiency and reduce carbon footprint. Previous works have focused on reducing the energy used within a single or multiple data centers without considering their energy sources and Power Usage Effectiveness (PUE). In contrast, this paper proposes a novel VM placement algorithm to increase the environmental sustainability by taking into account distributed data centers with different carbon footprint rates and PUEs. Simulation results show that the proposed algorithm reduces the CO2 emission and power consumption, while it maintains the same level of quality of service compared to other competitive algorithms.

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“…Some of this work does not minimize energy [15,30,33] or it applies to a model different than ours (VM migration [35,36], knowledge of future load [31,36], feasibility of allocation [13], multilevel architecture [32,35,27], interconnected VMs [14], etc.). On the other hand, some of the experimental work where minimization of energy is evaluated focus on a more restrictive cost function [39,27,40].…”

Section: Related Workmentioning

confidence: 99%

“…To the best of our knowledge, previous work on VMA has been only experimental [18,29,32,37] or has focused on different cost functions [19,4,13,17]. First, we provide an overview of previous theoretical work for related assignment problems (storage allocation, scheduling, network design, etc.).…”

Section: Related Workmentioning

confidence: 99%

Power-Efficient Assignment of Virtual Machines to Physical Machines

Aroca

Anta

Mosteiro

et al. 2014

Adaptive Resource Management and Scheduling for Cloud Computing

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Abstract. 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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Comparing VM-Placement Algorithms for On-Demand Clouds

Cited by 162 publications

References 23 publications

A Combined Bin Packing Vm Allocation and Minimum Loaded Vm Migration Approach for Load Balancing in Iaas Cloud Datacenters

A Combined Bin Packing Vm Allocation and Minimum Loaded Vm Migration Approach for Load Balancing in Iaas Cloud Datacenters

Energy and Carbon-Efficient Placement of Virtual Machines in Distributed Cloud Data Centers

Power-Efficient Assignment of Virtual Machines to Physical Machines

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