A Virtual Machine Placement Taxonomy

Pires, Fabio López; Barán, Benjamín

doi:10.1109/ccgrid.2015.15

Cited by 84 publications

(52 citation statements)

References 45 publications

(63 reference statements)

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“…The algorithm used by the operator for re‐optimizing the placement of VMs has large impact on multiple vital metrics: Energy consumption. A good VM placement optimization algorithm achieves low overall DC energy consumption, mainly by consolidating the VMs to as few PMs as possible (without violating performance objectives, see below).…”

Section: Introductionmentioning

confidence: 99%

Which is the best algorithm for virtual machine placement optimization?

Mann

Szabó

2017

Concurrency and Computation

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Summary One of the key problems for Infrastructure‐as‐a‐Service providers is finding the optimal allocation of virtual machines on the physical machines available in the provider's data center. Since the allocation has significant impact on operational costs as well as on the performance of the accommodated applications, several algorithms have been proposed for the virtual machine placement problem. So far, no objective comparison of the proposed algorithms has been provided; therefore, it is not known which one works best or what factors influence the performance of the algorithms. In this paper, we present an environment and methodology for such comparisons and compare 7 different algorithms using the proposed environment and methodology. Our results showcase differences of up to 66% between the effectiveness of different algorithms on the same real‐world workload traces, thus underlining the importance of objectively comparing the performance of competing algorithms.

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Section: Introductionmentioning

confidence: 99%

Which is the best algorithm for virtual machine placement optimization?

Mann

Szabó

2017

Concurrency and Computation

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“…The benefit of the AOF approach is that it reduces complexity and often improves runtime of the algorithm by limiting the search to a subspace of the feasible solutions. However, the drawback is that a correct combination of the objectives requires certain weights to be assigned to each objective, which often requires an in-depth knowledge of the problem domain [46]. Therefore, the assignment of the weights is essentially subjective [24].…”

Section: Rq3: Objectivesmentioning

confidence: 99%

Distributed virtual machine consolidation: A systematic mapping study

Ashraf

Byholm

Porres

2018

Computer Science Review

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Background: Virtual Machine (VM) consolidation is an effective technique to improve resource utilization and reduce energy footprint in cloud data centers. It can be implemented in a centralized or a distributed fashion. Distributed VM consolidation approaches are currently gaining popularity because they are often more scalable than their centralized counterparts and they avoid a single point of failure. Objective: To present a comprehensive, unbiased overview of the state-of-the-art on distributed VM consolidation approaches. Method: A Systematic Mapping Study (SMS) of the existing distributed VM consolidation approaches. Results: 19 papers on distributed VM consolidation categorized in a variety of ways. The results show that the existing distributed VM consolidation approaches use four types of algorithms, optimize a number of different objectives, and are often evaluated with experiments involving simulations. Conclusion: There is currently an increasing amount of interest on developing and evaluating novel distributed VM consolidation approaches. A number of research gaps exist where the focus of future research may be directed.

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“…Over the past few years, researchers have used a multitude of ways to develop novel VM consolidation approaches [3,[5][6][7][8][9][10][11][12][13][14][15]. Some of these approaches have also been reported in recent literature reviews [16,17].…”

Section: Introductionmentioning

confidence: 99%

“…The benefit of the AOF approach is that it reduces complexity and may improve the runtime of the algorithm by limiting the search to a subspace of the feasible solutions. However, the main drawback is that a correct combination of the objectives requires certain weights to be assigned to each objective, which often requires an in-depth knowledge of the problem domain [17]. Therefore, the assignment of the weights is essentially subjective [24].…”

Section: Introductionmentioning

confidence: 99%

Multi-objective dynamic virtual machine consolidation in the cloud using ant colony system

Ashraf

Porres

2017

International Journal of Parallel, Emergent and Distributed Sys

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In this paper, we present a novel multi-objective ant colony system algorithm for virtual machine (VM) consolidation in cloud data centers. The proposed algorithm builds VM migration plans, which are then used to minimize over-provisioning of physical machines (PMs) by consolidating VMs on under-utilized PMs. It optimizes two objectives that are ordered by their importance. The first and foremost objective in the proposed algorithm is to maximize the number of released PMs. Moreover, since VM migration is a resource-intensive operation, it also tries to minimize the number of VM migrations. The proposed algorithm is empirically evaluated in a series of experiments. The experimental results show that the proposed algorithm provides an efficient solution for VM consolidation in cloud data centers. Moreover, it outperforms two existing ant colony optimization based VM consolidation algorithms in terms of number of released PMs and number of VM migrations.

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A Virtual Machine Placement Taxonomy

Cited by 84 publications

References 45 publications

Which is the best algorithm for virtual machine placement optimization?

Which is the best algorithm for virtual machine placement optimization?

Distributed virtual machine consolidation: A systematic mapping study

Multi-objective dynamic virtual machine consolidation in the cloud using ant colony system

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