GRVMP: A Greedy Randomized Algorithm for Virtual Machine Placement in Cloud Data Centers
Open accessAbstract
Cloud computing efficiency greatly depends on the efficiency of the virtual machines (VMs) placement strategy used. However, VM placement has remained one of the major challenging issues in cloud computing mainly because of the heterogeneity in both virtual and physical machines (PMs), the multidimensionality of the resources, and the increasing scale of the cloud data centers (CDCs). An inefficiency in VM placement strategy has a significant influence on the quality of service provided, the amount of energy consumed, and the running costs of the CDCs. To address these issues, in this article, we propose a greedy randomized VM placement (GRVMP) algorithm in a large-scale CDC with heterogeneous and multidimensional resources. GRVMP inspires the “power of two choices” model and places VMs on the more power-efficient PMs to jointly optimize CDC energy usage and resource utilization. The performance of GRVMP is evaluated using synthetic and real-world production scenarios (Amazon EC2) with several performance matrices. The results of the experiment confirm that GRVMP jointly optimizes power usage and the overall wastage of resource utilization. The results also show that GRVMP significantly outperforms the baseline schemes in terms of the performance metrics used.
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GRVMP and EER-VMP ignored memory resource waste, restricting their usefulness in practical settings.
“Furthermore, certain models-like GRVMP [46] and EER-VMP [47]-ignored memory resource waste, therefore restricting their usefulness in practical settings.”
From Carbon-Aware, Energy-Efficient, and SLA-Compliant Virtual Machine Placement in Cloud Data Centers Using Deep Q-Networks and Agglomerative Clustering (2025) · cites this paper as [46]
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- Authors
- Azizi, S., Shojafar, M., Abawajy, J., & Buyya, R.
- Affiliations
- University of Kurdistan (IR)
- University of Surrey (GB)
- Deakin University (AU)
- The University of Melbourne (AU)
- Venue
- IEEE Systems Journal
- Published
- 2021-06
- Volume
- 15
- Issue
- 2
- Pages
- 2571-2582
- ISSN
- 1932-8184 (ISSN-L)
- 1937-9234
- 2373-7816
- OpenAlex
- W3040329870
- Cited by
- 80
- References
- 47
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 18 · 2023: 15 · 2022: 14 · 2021: 13 · 2020: 1
- FWCI
- 10.11
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- IEEE Systems Journal (publishedVersion)
- Open Research (University of Surrey) (submittedVersion, cc-by)
- Figshare (submittedVersion, cc-by)
- Funders
- Australian Research Council Discovery Project
- Marie Curie Fellowship
- European Commission (MSCA-IF-GF-2019-839255)
- 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 › Computer Networks and Communications › Distributed and Parallel Computing Systems
- Keywords
- virtual machine placement, cloud data centers, power of two choices, energy efficiency, resource utilization, heterogeneous resources, Amazon EC2
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