The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
An imperialist competitive algorithm for virtual machine placement in cloud computing
Abstract
Cloud computing, the recently emerged revolution in IT industry, is empowered by virtualisation technology. In this paradigm, the user’s applications run over some virtual machines (VMs). The process of selecting proper physical machines to host these virtual machines is called virtual machine placement. It plays an important role on resource utilisation and power efficiency of cloud computing environment. In this paper, we propose an imperialist competitive-based algorithm for the virtual machine placement problem called ICA-VMPLC. The base optimisation algorithm is chosen to be ICA because of its ease in neighbourhood movement, good convergence rate and suitable terminology. The proposed algorithm investigates search space in a unique manner to efficiently obtain optimal placement solution that simultaneously minimises power consumption and total resource wastage. Its final solution performance is compared with several existing methods such as grouping genetic and ant colony-based algorithms as well as bin packing heuristic. The simulation results show that the proposed method is superior to other tested algorithms in terms of power consumption, resource wastage, CPU usage efficiency and memory usage efficiency.
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“The techniques include EAGLE algorithm [139], Imperialist competitive algorithm [140], Krill herd algorithm [141].”
From Energy efficiency in cloud computing data centers: a survey on software technologies (2023) · cites this paper as [140]
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Self-citation
Research works printed but not linked: 5
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Cited by
Sorted by citations
Energy efficiency in cloud computing data centers: a survey on software technologies
Katal, A., Dahiya, S., & Choudhury, T. (2023) · Cluster Computing · doi:10.1007/s10586-022-03713-0 · Cited by 516
“The techniques include EAGLE algorithm [139], Imperialist competitive algorithm [140], Krill herd algorithm [141].”
MethodOptimizing Virtual Machine placement in IaaS data centers: Taxonomy, review and open issues
Talebian, H., Gani, A., Sookhak, M., Abdelatif, A. A., Yousafzai, A., Vasilakos, A. V., & Yu, F. R. (2020) · Cluster Computing · doi:10.1007/s10586-019-02954-w · Cited by 72
A multi-objective krill herd algorithm for virtual machine placement in cloud computing
Baalamurugan, K. M., & Vijay Bhanu, S. (2020) · The Journal of Supercomputing · doi:10.1007/s11227-018-2516-1 · Cited by 26
Metadata
- Authors
- Jamali, S., Malektaji, S., & Analoui, M.
- Affiliations
- University of Mohaghegh Ardabili (IR)
- Iran University of Science and Technology (IR)
- Corresponding author
- Jamali, Shahram
- Venue
- Journal of Experimental & Theoretical Artificial Intelligence
- Publisher
- Informa UK Limited
- Published
- 2016-07-26
- Volume
- 29
- Issue
- 3
- Pages
- 575-596
- ISSN
- 0952-813X (ISSN-L)
- 1362-3079
- OpenAlex
- W2500359574
- Cited by
- 14
- References
- 27
- Citations by year
- 2023: 2 · 2022: 1 · 2021: 1 · 2020: 4 · 2019: 4 · 2018: 1 · 2017: 1
- FWCI
- 4.46
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Experimental & Theoretical Artificial Intelligence (publishedVersion)
- Funders
- University of Technology Sydney
- Topics
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Keywords
- virtual machine placement, imperialist competitive algorithm, cloud computing, resource waste, resource utilization, metaheuristic optimization
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