Cited by 52Cloud Computing and Resource ManagementEnergy Efficiency in ComputingMetaheuristic Optimization Algorithms Research

A whale optimization system for energy-efficient container placement in data centers

Al-Moalmi, Ammar, Luo, Juan, Salah, Ahmad, Li, Kenli, Yin, Luxiu·2021·Expert Systems with Applications

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Crossref · 10.1016/j.eswa.2020.113719

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59 of 59 references verified0 citation errors · 0 not checkable

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  • Al-Moalmi et al. [50] proposed an algorithm based on Whale Optimization Algorithm to solve container placement and VM placement in CaaS environment.

    “Apart from this, Al-Moalmi et al. [50] have addressed the issues related to the placement of container and VM in Container as a Service (CaaS) environment by considering the optimization of the power consumption and resource utilization. They proposed an algorithm based on the Whale Optimization Algorithm (WOA) to solve the two problems that are container placement and VM placement.”

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Authors
Al-Moalmi, A., Luo, J., Salah, A., Li, K., & Yin, L.
Affiliations
Hunan University (CN)
Corresponding author
Luo, Juan
Venue
Expert Systems with Applications
Publisher
Elsevier BV
Published
2021-02
Volume
164
Pages
113719
ISSN
0957-4174 (ISSN-L)
OpenAlex
W3045685641
Cited by
52
References
59
Citations by year
2026: 2 · 2025: 9 · 2024: 11 · 2023: 14 · 2022: 10 · 2021: 6
FWCI
7.06
Type
journal article
Language
en
Open access
closed
Locations
Expert Systems with Applications (publishedVersion)
Funders
  • National Defense Basic Scientific Research Program of China
  • National Natural Science Foundation of China
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 placement, virtual machine placement, whale optimization algorithm, container as a service, data center energy efficiency, power consumption minimization, resource utilization, heterogeneous hardware, multi-objective optimization

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