Resource-Efficient VM Placement in the Cloud Environment Using Improved Particle Swarm Optimization

Magotra, Bhagyalakshmi; Malhotra, Deepti

doi:10.4018/ijamc.298312

Cited by 6 publications

(2 citation statements)

References 47 publications

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“…PAPSO employs a minimization fitness function to solve the placement of virtual machines that come with nearoptimal solutions. The proposed approach is implemented in CloudSim, where compared to the PABFD using randomized workloads executed on VMs and PMs of different sizes [34].…”

Section: Figure 1 the Architecture Of Eevmpso Systemmentioning

confidence: 99%

An Energy Efficient Particle Swarm Optimization based VM Allocation for Cloud Data Centre: EEVMPSO

Pandey,

Singh

2023

ICST Scal Inf Syst

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Virtual Machine (VM) allocation are the crucial problems because cloud computing enables the rapid growth of data centres and compute centres. Power consumption and network expenses have increased as cloud computing becomes more and more prevalent. System instability may result from repeated requests for computing resources. One of the most important and difficulties facing virtualization technology is finding the best way to stack virtual machines on top of physical machines in cloud data centres. The host must move virtual machines from overloaded to underloaded hosts as part of load balancing, which has an impact on energy consumption. The proposed energy efficient particle swarm optimization algorithm (EEVMPSO) for Virtual Machine allocation to maximize the load balancing. System resources including CPU, storage, and memory are optimized using EEVMPSO. This research article suggests energy-aware virtual machine migration using the Particle Swarm Optimization Algorithm for dynamic VMs placement, energy efficient cloud data centres as a solution to this issue. The experimental result shown in the proposed method, consumption energy in comparison to the PAPSO, KHA, EALBPSO, and RACC-MDT algorithm by 10.86%, 18.22%, 25.8%, and 31.34% respectively, it demonstrated the improvements in the energy service level agreements violation 5.77%, 15.3%, 26.19%, and 30.4%, as well as the average CPU utilization 2.2%, 24%, 22.6%, and 14.6%.

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Section: Figure 1 the Architecture Of Eevmpso Systemmentioning

confidence: 99%

An Energy Efficient Particle Swarm Optimization based VM Allocation for Cloud Data Centre: EEVMPSO

Pandey,

Singh

2023

ICST Scal Inf Syst

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“…When we need to find the optimal individual, we will find a global optimal solution as a set of all other point sets in the population, and then decide to reallocate the new sample to each particle according to each local best and most recent characteristics. Particle Swarm Optimization (PSO) is a search theory based on the space competition between random individuals and populations in nature, thus generating evolutionary search theory [17][18].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Technical Design of Water Pollution Prevention Project Based on Particle Swarm Optimization

2022

WPPCP

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In the water pollution prevention project, particle swarm optimization is a very practical method, which gathers multiple individuals into a group. It can effectively solve traditional problems. This paper mainly introduces the water pollution control technology and control theory and basic steps based on the characteristics of nuclear particle number and diversity, multi species clustering, etc. Then, it summarizes, analyzes and studies the existing literature on its application and shortcomings, and gives suggestions for improvement. It further explains from the design idea to the algorithm flow, and applies this method to practical projects to reduce costs while ensuring accuracy. Next, based on the research of particle swarm optimization algorithm, this paper designs the technical framework of water pollution prevention and control engineering, and conducts simulation tests on the performance of this algorithm. The test results show that in water pollution prevention and control engineering, through the comparative analysis of the performance of particle swarm optimization algorithm, it is found that the particle swarm optimization algorithm has a fast running time and high iteration efficiency, because it has good global optimization ability, And its performance is also very stable and reliable. Therefore, these factors should be fully considered and utilized in the design process to improve the technical level of water pollution treatment so as to achieve the goal of environmental protection in a real sense.

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SR-PSO: server residual efficiency-aware particle swarm optimization for dynamic virtual machine scheduling

Ajmera

Tewari

2023

J Supercomput

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Resource-Efficient VM Placement in the Cloud Environment Using Improved Particle Swarm Optimization

Cited by 6 publications

References 47 publications

An Energy Efficient Particle Swarm Optimization based VM Allocation for Cloud Data Centre: EEVMPSO

An Energy Efficient Particle Swarm Optimization based VM Allocation for Cloud Data Centre: EEVMPSO

Technical Design of Water Pollution Prevention Project Based on Particle Swarm Optimization

SR-PSO: server residual efficiency-aware particle swarm optimization for dynamic virtual machine scheduling

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