A novel virtual machine placement algorithm using RF element in cloud infrastructure

Farzaneh, Seyed Milad; Fatemi, Omid

doi:10.1007/s11227-021-03863-9

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…Farzaneh and Fatemi's [ 18 ] work focused on the critical optimization challenge in cloud architecture that is VM placement (VMP). The solution impacts prices, energy, and performance.…”

Section: Literature Surveysmentioning

confidence: 99%

An Energy-Efficient Strategy and Secure VM Placement Algorithm in Cloud Computing

Srivastava

Tiwari

Srivastava

et al. 2022

Computational Intelligence and Neuroscience

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One of the important and challenging tasks in cloud computing is to obtain the usefulness of cloud by implementing several specifications for our needs, to meet the present growing demands, and to minimize energy consumption as much as possible and ensure proper utilization of computing resources. An excellent mapping scheme has been derived which maps virtual machines (VMs) to physical machines (PMs), which is also known as virtual machine (VM) placement, and this needs to be implemented. The tremendous diversity of computing resources, tasks, and virtualization processes in the cloud causes the consolidation method to be more complex, tedious, and problematic. An algorithm for reducing energy use and resource allocation is proposed for implementation in this article. This algorithm was developed with the help of a Cloud System Model, which enables mapping between VMs and PMs and among tasks of VMs. The methodology used in this algorithm also supports lowering the number of PMs that are in an active state and optimizes the total time taken to process a set of tasks (also known as makespan time). Using the CloudSim Simulator tool, we evaluated and assessed the energy consumption and makespan time. The results are compiled and then compared graphically with respect to other existing energy-efficient VM placement algorithms.

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“…Farzaneh and Fatemi's [ 18 ] work focused on the critical optimization challenge in cloud architecture that is VM placement (VMP). The solution impacts prices, energy, and performance.…”

Section: Literature Surveysmentioning

confidence: 99%

An Energy-Efficient Strategy and Secure VM Placement Algorithm in Cloud Computing

Srivastava

Tiwari

Srivastava

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Virtualization forms an essential technology of the cloud, based on which it creates Virtual Machines (VM) over the active servers to perform tasks as requested by different clients [3,4]. The number of cloud clients has been rapidly growing to avail the wide range of day-to-day cloud services due to which there has been a drastic need for more VM to fulfill the demands of its customers.…”

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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Encryption with access policy and cloud data selection for secure and energy-efficient cloud computing

Reddy¹,

Rao

Kumar

et al. 2023

Multimed Tools Appl

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A novel virtual machine placement algorithm using RF element in cloud infrastructure

Cited by 3 publications

References 28 publications

An Energy-Efficient Strategy and Secure VM Placement Algorithm in Cloud Computing

An Energy-Efficient Strategy and Secure VM Placement Algorithm in Cloud Computing

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

Encryption with access policy and cloud data selection for secure and energy-efficient cloud computing

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