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

Ajmera, Kashav; Tewari, Tribhuwan Kumar

doi:10.1007/s11227-023-05270-8

Cited by 7 publications

(1 citation statement)

References 31 publications

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“…PSO has the advantages of simplicity, high accuracy, and fast convergence, and has good advantages in solving practical problems. The main process of the PSO algorithm is shown in Figure 4 [21].…”

Section: Design Of Improved Svr-pso Algorithm For Multi-objective Opt...mentioning

confidence: 99%

Optimization Design of Indoor Environmental Ventilation in Buildings Based on Improved SVR-PSO Model

Han,

Zhang,

Yin

et al. 2024

Sustainability

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As the growth of society and the continuous upgrading of people’s living standards increase, people’s requirements for indoor environment are also increasing. To optimize the ventilation methods inside buildings, a numerical simulation method was used to construct numerical simulations of airflow organization and aerosol diffusion, and based on this model, better ventilation methods were determined. To optimize the determined better ventilation method, a multi-constraint optimization model was constructed using infection probability, thermal comfort, energy utilization coefficient, and velocity non-uniformity coefficient. The ventilation method was optimized through multi-objective constraints. To solve the optimization model, an optimized particle swarm algorithm was studied and designed. The results showed that under the “air rain” flow field, the maximum values of aerosol particles at the human body, bed surface, and outlet were 171, 769, and 19,973, respectively, while the minimum values were 4, 169, and 2197, respectively. The “air rain” flow field is a better ventilation method. The maximum and minimum values of the original non-uniformity coefficient were 0.44 and 0.08, respectively. After optimization by the particle swarm optimization algorithm, the maximum and minimum predicted non-uniformity coefficients were 0.457 and 0.08, respectively. The original value and predicted value are very close. The numerical model and algorithm constructed by the research institute are effective. The algorithm designed by the research institute can provide technical support for multi-objective optimization of indoor ventilation methods.

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Section: Design Of Improved Svr-pso Algorithm For Multi-objective Opt...mentioning

confidence: 99%

Optimization Design of Indoor Environmental Ventilation in Buildings Based on Improved SVR-PSO Model

Han,

Zhang,

Yin

et al. 2024

Sustainability

View full text Add to dashboard Cite

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Resource Management in Edge Clouds: Latency-Aware Approaches for Big Data Analysis

Pozveh,

Shahhoseini,

Khabareh

2024

Studies in Big Data

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NS-OWACC: nature-inspired strategies for optimizing workload allocation in cloud computing

Deng,

Nauman,

Zhang

2024

Computing

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

Cited by 7 publications

References 31 publications

Optimization Design of Indoor Environmental Ventilation in Buildings Based on Improved SVR-PSO Model

Optimization Design of Indoor Environmental Ventilation in Buildings Based on Improved SVR-PSO Model

Resource Management in Edge Clouds: Latency-Aware Approaches for Big Data Analysis

NS-OWACC: nature-inspired strategies for optimizing workload allocation in cloud computing

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