Cited by 53Cloud Computing and Resource ManagementEnergy Efficiency in ComputingMetaheuristic Optimization Algorithms Research

Energy-Aware Container Consolidation Based on PSO in Cloud Data Centers

Shi, Tao, Ma, Hui, Chen, Gang·2018·2018 IEEE Congress on Evolutionary Computation (CEC)

Abstract

In the last few years, the container-based Cloud computing paradigm has gradually emerged as a flexible approach for energy efficient resource utilization. Cloud providers aim to optimize resource utilization and energy consumption while performing container consolidation, which involves virtual machine (VM) selection and VM placement. Most of the related works focus either on VM selection or VM placement, neglecting the interplay of the two optimization problems. This paper proposes a Two-stage Multi-type Particle Swarm Optimization approach, named TMPSO, to energy-aware container consolidation in Cloud data centers. Our experimental evaluation based on benchmark datasets demonstrates that our proposed algorithm shows further energy saving comparing with some existing approaches.

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Crossref · 10.1109/cec.2018.8477708

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Authors
Shi, T., Ma, H., & Chen, G.
Affiliations
Victoria University of Wellington (NZ)
Venue
2018 IEEE Congress on Evolutionary Computation (CEC)
Publisher
IEEE
Published
2018-07
Pages
1-8
OpenAlex
W2897536843
Cited by
53
References
25
Citations by year
2026: 3 · 2025: 7 · 2024: 7 · 2023: 10 · 2022: 5 · 2021: 4 · 2020: 14 · 2019: 3
FWCI
4.12
Type
conference paper
Language
en
Open access
closed
Locations
IEEE Congress on Evolutionary Computation (CEC) (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
  • Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
  • Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
Keywords
container consolidation, energy-aware resource allocation, cloud data centers, particle swarm optimization, virtual machine placement, virtual machine selection, energy efficiency

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