An energy-efficient VM placement method for cloud data centers using a hybrid genetic algorithm

Kaaouache, Mohamed Amine; Bouamama, Sadok

doi:10.1108/jsit-10-2017-0089

Cited by 14 publications

(7 citation statements)

References 24 publications

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“…In order to discover the Pareto optimum solutions, population-based techniques employ the idea of dominance in their screening process. The techniques used are Genetic [133], Ant Colony Optimization (ACO) [134], Memetic [135], Firefly [136], Whale optimization [137], Sine-Cosine Algorithm and the Salp Swarm Algorithm [138]. Single solution-based algorithms begin with a single solution, which is then modified and transformed throughout the optimization process.…”

Section: Vm Placementmentioning

confidence: 99%

Energy efficiency in cloud computing data centers: a survey on software technologies

2022

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Cloud computing is a commercial and economic paradigm that has gained traction since 2006 and is presently the most significant technology in IT sector. From the notion of cloud computing to its energy efficiency, cloud has been the subject of much discussion. The energy consumption of data centres alone will rise from 200 TWh in 2016 to 2967 TWh in 2030. The data centres require a lot of power to provide services, which increases CO2 emissions. In this survey paper, software-based technologies that can be used for building green data centers and include power management at individual software level has been discussed. The paper discusses the energy efficiency in containers and problem-solving approaches used for reducing power consumption in data centers. Further, the paper also gives details about the impact of data centers on environment that includes the e-waste and the various standards opted by different countries for giving rating to the data centers. This article goes beyond just demonstrating new green cloud computing possibilities. Instead, it focuses the attention and resources of academia and society on a critical issue: long-term technological advancement. The article covers the new technologies that can be applied at the individual software level that includes techniques applied at virtualization level, operating system level and application level. It clearly defines different measures at each level to reduce the energy consumption that clearly adds value to the current environmental problem of pollution reduction. This article also addresses the difficulties, concerns, and needs that cloud data centres and cloud organisations must grasp, as well as some of the factors and case studies that influence green cloud usage.

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Section: Vm Placementmentioning

confidence: 99%

Energy efficiency in cloud computing data centers: a survey on software technologies

2022

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“…The VM migration is executed while the servers are overloaded during the VM placement. Therefore, efficient VM 44 migration is performed using the DJ‐HA. The suggested VM migration is aimed to minimize the makespan and energy taken while performing the task in the cloud sector.…”

Section: Optimal Vm Migration In a Cloud Environment By A Hybrid Heur...mentioning

confidence: 99%

Hybrid meta‐heuristic algorithm for optimal virtual machine placement and migration in cloud computing

Henry

Anbuananth

Kalarani

2022

Concurrency and Computation

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Data sharing in cloud computing happens with multiple participants to freely distribute the group data, which focuses on advancing the effectiveness of work in cooperative backgrounds and has attained widespread benefits. The main intent of this article is to accomplish a virtual machines (VMs) placement and migration model using a hybrid meta-heuristic concept. A new meta-heuristic algorithm named DJ-HA is developed for optimal VM placement and migration to reduce the count of active servers, and minimization of makespan, and energy consumption with a faster convergence rate in a cloud background. Then, the VM migration is done based on the multi-objective function concerning energy consumption and makespan using the same hybrid DJ-HA.From the result analysis, the energy consumption of the DJ-HA is correspondingly secured at 4.3%, 3.5%, 31%, and 33% more advanced than PSO, GWO, DHOA, and JA, at the 100th iteration for Experiment 1. Accordingly, the cost function of the suggested DJ-HA is secured at 88.8%, 89.4%, 33.3%, and 50% increased than PSO, GWO, DHOA, and JA at the 100th iteration for Experiment 4. Hence, it is proved that the suggested VM migration using DJ-HA is enriched than the other conventional algorithms.

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“…The minimization of migration costs is also discussed in a recent work focusing on network function virtualization in [31]. Other works considered service component placement optimization using many different objectives and/or criteria such as dimensioning [32], job completion time [33], VM consolidation [34] [35], and energy saving [36] [37]. More recently, sophisticated auto-scaling solutions emerged [38], [39].…”

Section: B Related Workmentioning

confidence: 99%

BDMaaS+: Business-Driven and Simulation-Based Optimization of IT Services in the Hybrid Cloud

Cerroni

Grabarnik

Poltronieri

et al. 2022

IEEE Trans. Netw. Serv. Manage.

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The maturity of heterogeneous and hybrid public Cloud environments enables service providers to deploy there their complex IT services trusting these large and complex infrastructures. At the same time, evaluating the impact of changes at service configuration before and at the runtime is still a very challenging and difficult task. Moreover, a comprehensive performance evaluation of IT service configurations should not be limited just to costs for IT resource acquisition, but also include risk related elements such as Service Level Agreement (SLA) violation penalties and other intangibles. To support IT service providers in this difficult task, we developed Business-Driven Management as a Service Plus (BDMaaS+), a novel decision support tool that can evaluate IT service configuration through simulation with realistic service and network models. By allowing service providers to define expanded operational parameters, BDMaaS+ also enables what-if scenario analysis, thereby opening interesting possibilities at the planning level. Experimental results, collected from our thorough evaluations, demonstrate how a service provider can leverage BDMaaS+ to explore the potential of high-level business SLA changes and data center additions.

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An energy-efficient VM placement method for cloud data centers using a hybrid genetic algorithm

Cited by 14 publications

References 24 publications

Energy efficiency in cloud computing data centers: a survey on software technologies

Energy efficiency in cloud computing data centers: a survey on software technologies

Hybrid meta‐heuristic algorithm for optimal virtual machine placement and migration in cloud computing

BDMaaS+: Business-Driven and Simulation-Based Optimization of IT Services in the Hybrid Cloud

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