Multi-Objective Algorithms for Virtual Machine Selection and Placement in Cloud Data Center

Alhammadi, Akram S. A.; Vasanthi, V.

doi:10.1109/icoten52080.2021.9493496

Cited by 6 publications

(2 citation statements)

References 25 publications

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“…It has been observed that energy consumption has been one of the main factors that has affected the progress of the cloud as it not only increases the operational costs of the data centers but also causes high carbon emissions in the environment [16]. Similarly, many other factors such as SLA violations, resource wastage, QoS, network traffic, etc.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Reinforcement Learning for Virtual Machines Placement in Cloud Computing

Bhatt,

Singhal

2024

IJACSA

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The rapid demand for cloud services has provoked cloud providers to efficiently resolve the problem of Virtual Machines Placement in the cloud. This paper presents a VM Placement using Reinforcement Learning that aims to provide optimal resource and energy management for cloud data centers. Reinforcement Learning provides better decision-making as it solves the complexity of VM Placement problem caused due to tradeoff among the objectives and hence is useful for mapping requested VM on the minimum number of Physical Machines. An enhanced Tournament-based selection strategy along with Roulette Wheel sampling has been applied to ensure that the optimization goes through balanced exploration and exploitation, thereby giving better solution quality. Two heuristics have been used for the ordering of VM, considering the impact of CPU and memory utilizations over the VM placement. Moreover, the concept of the Pareto approximate set has been considered to ensure that both objectives are prioritized according to the perspective of the users. The proposed technique has been implemented on MATLAB 2020b. Simulation analysis showed that the VMRL performed preferably well and has shown improvement of 17%, 20% and 18% in terms of energy consumption, resource utilization and fragmentation respectively in comparison to other multi-objective algorithms.

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Section: Introductionmentioning

confidence: 99%

Multi-Objective Reinforcement Learning for Virtual Machines Placement in Cloud Computing

Bhatt,

Singhal

2024

IJACSA

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“…This approach requires improvisation to introduce more prioritized attributes for applications and experiments. A new metaheuristic has been proposed in the literature [15], which functions to minimize the VM cost and energy consumption. The mechanism focuses on solving the VM problem but does not hold any approach for effective load balancing.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Metaheuristic Technique for Optimization of Virtual Machine Placement in Cloud

Chayan,

Sunita

2023

IJFIS

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The massive growth in cloud users and applications has caused a tremendous increase in the number of virtual machine (VM) requests. Consequently, VM placement has become an important factor in the management of cloud datacenters and their resources. In this context, a new hybrid mechanism named simulated annealing-intelligent water drop cycle algorithm (SA-IWDCA) has been proposed that improves the computational efforts and convergence rate of the water cycle algorithm (WCA) to efficiently place VM in a heterogeneous cloud. WCA performs hybridization in two levels using preselection and memetic algorithms. In level one, an intelligent water drop (IWD) is invoked to generate and feed the initial VM solution to the WCA. At the second level, simulated annealing (SA) is used to improve the VM solution quality using neighborhood search and targeted approaches of temperature scheduling and acceptance probability. The proposed approach incorporates the energy consumption and resource management of datacenters, resulting in a minimum number of active servers and reduced resource dissipation. Extensive simulations and analysis using MATLAB showed that the SA-IWDCA performed significantly well and has shown an improvement of 25% in energy consumption and 20% in resource utilization as compared to other heuristic and metaheuristic approaches.

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MECpVmS: an SLA aware energy-efficient virtual machine selection policy for green cloud computing

et al. 2022

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Multi-Objective Algorithms for Virtual Machine Selection and Placement in Cloud Data Center

Cited by 6 publications

References 25 publications

Multi-Objective Reinforcement Learning for Virtual Machines Placement in Cloud Computing

Multi-Objective Reinforcement Learning for Virtual Machines Placement in Cloud Computing

Hybrid Metaheuristic Technique for Optimization of Virtual Machine Placement in Cloud

MECpVmS: an SLA aware energy-efficient virtual machine selection policy for green cloud computing

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