Approaches for optimizing virtual machine placement and migration in cloud environments: A survey

Filho, Manoel C. Silva; Monteiro, Claudio C.; Inácio, Pedro R. M.; Freire, Mário M.

doi:10.1016/j.jpdc.2017.08.010

Cited by 101 publications

(66 citation statements)

References 85 publications

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“…Cloud providers make use of varied VM placement techniques. Silva Filho et al [8] give a completeresearch survey on placement of VM, optimization and relocation in cloud set up. They have deliberated the problem formulations, benefits, and inadequacies of relevant research.…”

Section: Related Workmentioning

confidence: 99%

“…They have deliberated the problem formulations, benefits, and inadequacies of relevant research. A variety of goals for VM placement have been well-thought-out in preceding research, which includes power saving, minimizing the cost, load balancing, decreasing SLA violations, network latency, congestion, and service down time [8]. As per the goals of placement, VM placement approaches can be differentiated into two categories [9]:…”

Section: Related Workmentioning

confidence: 99%

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A Two-Stage Heuristic Hybrid Evolutionary Algorithm for Virtual Machine Migration and Placement in Cloud Bursting

2019

IJRTE

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The platform for cloud computing offers virtualization and a dynamic pool of resources to the consumers of the cloud. The acceptance and demand of cloud is growing on a regular basis. Cloud computing offers utility-based consumer services across the globe on a pay as you go strategy. Live Virtual Machine (VM) migration sets the basic foundation for cloud management. It hasakey role in reducing operating expenditure and revising quality of service without disruption of cloud services running in the VM. A lot of research has been done to yield better performance in live VM migration and has seen noteworthy development and accomplishment. However, some crucial problems require results and enhancement. With the growth of new cloud computing models like Mobile Edge Computing, certain problems related to optimization need to be addressed. The primary aim of this research work is to emphasize on optimum functioning of live migration. A migration algorithm to consolidate the computational resources, storage resources and network resources dynamically with a two-stage heuristic hybrid evolutionary algorithm is discussed. The resources are consolidated to cut down the energy and cost utilization depending upon the evolutionary Particle Swarm Optimization and the Ant Colony Optimization algorithms. These algorithms can rapidly identify the migrating virtual machines and locate their positions respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Two-Stage Heuristic Hybrid Evolutionary Algorithm for Virtual Machine Migration and Placement in Cloud Bursting

2019

IJRTE

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“…In other words, VM migration is an important technique used in VM consolidation to reduce the number of PMs . The goal is to reduce energy consumption by turning off underloaded PMs and avoid SLAV caused by overloaded PMs …”

Section: Introductionmentioning

confidence: 99%

“…When a PM is overloaded in order to prevent SLAVs and energy wastage, a number of VMs must migrate to other PMs for an uninterruptible execution. In addition to (beside) SLAVs, by increasing the response time, servers will stay active for a longer time which leads to more energy consumption …”

Section: Introductionmentioning

confidence: 99%

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Burst‐aware virtual machine migration for improving performance in the cloud

Rahmani

Khajehvand

2020

Int J Communication

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Burst is a common pattern in the user's requirements, which suddenly increases the workload of virtual machines (VMs) and reduces the performance and energy efficiency of cloud computing systems (CCS). Virtualization technology with the ability to migrate VMs attempts to solve this problem. By migration, VMs can be dynamically consolidated to the users' requests. Burst temporarily increases the workload. Ignoring this issue will lead to incorrect decisions regarding the migration of VMs. It increases the number of migrations and Service Level Agreement Violations (SLAVs) due to overload. This may cause waste of resources, increase in energy consumption, and misplaced VMs. Therefore, a burst-aware method for these issues is proposed in this paper. The method consists of two algorithms: one for determining the migration time and the other for the placement of VMs. We use the PlanetLab real dataset and CloudSim simulator to evaluate the performance of the proposed method. The results confirm the advantages of the method regarding performance compared to benchmark methods.

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Comparison of workload consolidation algorithms for cloud data centers

Ponto

Kecskeméti

Mann

2021

Concurrency and Computation

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Workload consolidation is an important method for the efficient operation of cloud data centers, impacting important quality attributes such as resource utilization and power consumption. Many different approaches have been proposed for workload consolidation, but few comparative studies were executed to date. Therefore, it is unclear which of the proposed approaches work best in which situation. In this article, we present a comprehensive simulation‐based comparison of five workload consolidation techniques. We introduce a general framework for workload consolidation techniques to the DISSECT‐CF simulator to foster the development and comparison of efficient data center consolidation algorithms. We use this framework to evaluate the effectiveness of a first fit best fit decreasing heuristic, a custom heuristic, and three population‐based metaheuristics (genetic algorithm, artificial bee colony, and particle swarm optimization). The evaluation is based on a wide variety of real‐world workload traces. The five algorithms are compared in terms of total energy consumption, the duration of the simulation, and the number of migrations. Based on the results, there is no generally best consolidation technique. The results deliver insight into the pros and cons of the algorithms as well as the impact of different parameters. In particular, the results show that population‐based metaheuristics do not offer a significant gain in terms of solution quality to compensate for the increased simulation time.

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Approaches for optimizing virtual machine placement and migration in cloud environments: A survey

Cited by 101 publications

References 85 publications

A Two-Stage Heuristic Hybrid Evolutionary Algorithm for Virtual Machine Migration and Placement in Cloud Bursting

A Two-Stage Heuristic Hybrid Evolutionary Algorithm for Virtual Machine Migration and Placement in Cloud Bursting

Burst‐aware virtual machine migration for improving performance in the cloud

Comparison of workload consolidation algorithms for cloud data centers

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