Redundant virtual machine placement for fault-tolerant consolidated server clusters

Machida, Fumio; Kawato, Masahiro; Maeno, Yoshiharu

doi:10.1109/noms.2010.5488431

Cited by 109 publications

(52 citation statements)

References 14 publications

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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%

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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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

View full text Add to dashboard Cite

show abstract

“…In on-demand clouds, there are potentially two types of VM-placement decisions to be made: (1) initial placement [13][14][15][16][17][18][19][20][21][22][23] and (2) migration (and/or resizing) of VMs over time [24][25][26][27][28][29][30], as PM availability changes, as consolidation is needed to conserve power and in response to the degree to which service-level agreements (SLAs) are being achieved. Most previous research on initial VM placement considered only PMs within a single cloud, but in one case [22] placement decisions considered which of several clouds to choose.…”

Section: Related Workmentioning

confidence: 99%

“…When VMs communicate, placing them on the same cluster makes good sense because communication among the VMs will be local to a cluster switch. Most existing research [13][14][15][16][17][18][19][20][21][22][23] considers PMs as an unstructured pool, where restricting VMs to a shared cluster would be accomplished by designating a Boolean attribute, one of potentially many attributes over which some optimization algorithm or binpacking heuristic would be executed. In our study, guided by the open-source code in Eucalyptus (v1.6) [6], we adopt explicit use of two distinct decisions levels: (1) choosing a cluster for all VMs in a given request and then (2) choosing specific PMs within the selected cluster.…”

Section: Related Workmentioning

confidence: 99%

“…Various criteria have been used to order qualified PMs. For example, many researchers [13,16,18,23,27] adopt ordering heuristics based on the literature associated with online bin packing [7][8]. Other schemes extend those heuristics by adding specific attributes (e.g., CPU usage, network and disk controller usage, and memory usage), summarized into a weighted value used to order PMs or to assign categories (e.g., star ratings [15]) that can be used to order PMs.…”

Section: Related Workmentioning

confidence: 99%

“…Other schemes extend those heuristics by adding specific attributes (e.g., CPU usage, network and disk controller usage, and memory usage), summarized into a weighted value used to order PMs or to assign categories (e.g., star ratings [15]) that can be used to order PMs. In some schemes, attributes used to order PMs are determined by individual VM users [23,30,31], while in other schemes attributes are determined by the provider [13,14,19,27], or user and provider attributes are combined [21,22,23,26]. To limit our study, we elected to use heuristics based on those found in online bin-packing literature.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Comparing VM-Placement Algorithms for On-Demand Clouds

Mills

Filliben

Dabrowski

2011

2011 IEEE Third International Conference on Cloud Computing Technology and Science

162

112

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Abstract-Much recent research has been devoted to investigating algorithms for allocating virtual machines (VMs) to physical machines (PMs) in infrastructure clouds. Many such algorithms address distinct problems, such as initial placement, consolidation, or tradeoffs between honoring service-level agreements and constraining provider operating costs. Even where similar problems are addressed, each individual research team evaluates proposed algorithms under distinct conditions, using various techniques, often targeted to a small collection of VMs and PMs. In this paper, we describe an objective method that can be used to compare VMplacement algorithms in large clouds, covering tens of thousands of PMs and hundreds of thousands of VMs. We demonstrate our method by comparing 18 algorithms for initial VM placement in on-demand infrastructure clouds. We compare algorithms inspired by open-source code for infrastructure clouds, and by the online bin-packing literature.

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Network failure–aware redundant virtual machine placement in a cloud data center

Zhou

Wang

Hsu

et al. 2017

Concurrency and Computation

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Summary Cloud has become a very popular infrastructure for many smart city applications. A growing number of smart city applications from all over the world are deployed on the clouds. However, node failure events from the cloud data center have negative impact on the performance of smart city applications. Survivable virtual machine placement has been proposed by the researchers to enhance the service reliability. Because of the ignorance of switch failure, current survivable virtual machine placement approaches cannot achieve the best effect. In this paper, we study to enhance the service reliability by designing a novel network failure–aware redundant virtual machine placement approach in a cloud data center. Firstly, we formulate the network failure–aware redundant virtual machine placement problem as an integer nonlinear programming problem and prove that the problem is NP‐hard. Secondly, we propose a heuristic algorithm to solve the problem. Finally, extensive simulation results show the effectiveness of our algorithm.

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Redundant virtual machine placement for fault-tolerant consolidated server clusters

Cited by 109 publications

References 14 publications

Power-Efficient Assignment of Virtual Machines to Physical Machines

Power-Efficient Assignment of Virtual Machines to Physical Machines

Comparing VM-Placement Algorithms for On-Demand Clouds

Network failure–aware redundant virtual machine placement in a cloud data center

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