The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Optimal VM Placement Approach Using Fuzzy Reinforcement Learning for Cloud Data Centers
Abstract
Ineffective resource management in cloud data centers leads to high energy usage and cost to maintain the resources. A Virtual machine (VM) placement is a part of the VM consolidation problem where each VM should be mapped to the available host. An optimal VM placement is an effective way to enhance both resource usage and energy efficiency. However, minimizing the energy consumption without affecting SLA violation and performance of the application is quite challenging. This paper proposes a fuzzy based State-Action-Reward-State-Action (SARSA) reinforcement learning algorithm to address the VM placement problem. The integration of fuzzy controller with Reinforcement learning (RL) algorithm is capable of optimizing the reallocation of maximal number of VMs into a minimal number of hosts that reduce energy usage and wastage of resource as well. The proposed work is capable of handling the fluctuating workload situations and delivers proper placement of VMs (initial or re-mapping) while ensuring the desired Quality-of-Service (QoS) demands of users to meet the Service Level Agreement (SLA). The experimental results exhibit reduced consumption of energy and less resource wastage as compared with other VM placement algorithms.
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“For the suitable placement, machine-learning approaches can be used for the placement of virtual machines. Reinforcement Learning [130], Artificial Neural Network [131], and Fuzzy reinforcement learning [132] are some of them.”
From Energy efficiency in cloud computing data centers: a survey on software technologies (2023) · cites this paper as [132]
Citations
Cites
Assessing ICT global emissions footprint: Trends to 2040 & recommendations
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Qi, L., Chen, Y., Yuan, Y., et al. (2019) · World Wide Web · doi:10.1007/s11280-019-00684-y · Cited by 196
Energy-Aware VM Consolidation in Cloud Data Centers Using Utilization Prediction Model
Farahnakian, F., Pahikkala, T., Liljeberg, P., Plosila, J., Hieu, N. T., & Tenhunen, H. (2016) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2016.2617374 · Cited by 185
Energy-Efficient Virtual Machines Consolidation in Cloud Data Centers Using Reinforcement Learning
Farahnakian, F., Liljeberg, P., & Plosila, J. (2014) · Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) · doi:10.1109/pdp.2014.109 · Cited by 167
Improving Packing Algorithms for Server Consolidation.
Ajiro, Y., & Tanaka, A. (2007) · Int. CMG Conference · Cited by 153
Energy-aware VM placement algorithms for the OpenStack Neat consolidation framework
Moges, F. F., & Abebe, S. L. (2019) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-019-0126-y · Cited by 97
Ranjbari, M., & Torkestani, J. A. (2017) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2017.10.009 · Cited by 93
Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge Evolution
Jamshidi, P., Sharifloo, A. M., Pahl, C., Metzger, A., & Estrada, G. G. (2015) · International Conference on Cloud and Autonomic Computing (ICCAC) · doi:10.1109/iccac.2015.35 · Cited by 74
Enhanced First-Fit Decreasing Algorithm for Energy-Aware Job Scheduling in Cloud
Alahmadi, A. M., Alnowiser, A., Zhu, M., Che, D., & Ghodous, P. (2014) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci.2014.97 · Cited by 60
A reinforcement learning approach for dynamic selection of virtual machines in cloud data centres
Duggan, M., Kieran, F., Duggan, J., et al. (2016) · International Conference on Innovative Computing Technology · doi:10.1109/intech.2016.7845053 · Cited by 31
Research works printed but not linked: 1
- 2016, Cloudsim
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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
“Reinforcement Learning [130], Artificial Neural Network [131], and Fuzzy reinforcement learning [132] are some of them.”
Metadata
- Authors
- Jumnal, A., & Dilip Kumar, S. M.
- Venue
- 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV)
- Publisher
- IEEE
- Published
- 2021-02-04
- Pages
- 29-35
- OpenAlex
- W3147611418
- Cited by
- 9
- References
- 17
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 2 · 2022: 3
- FWCI
- 1.45
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) (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 › Computer Networks and Communications › Software-Defined Networks and 5G
- Keywords
- virtual machine placement, fuzzy reinforcement learning, SARSA algorithm, cloud data centers, energy efficiency, VM consolidation, resource management, service level agreements, quality of service, workload fluctuation
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