The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Artificial neural network-based virtual machine allocation in cloud computing
Abstract
The expeditious development in information technology visualizes that facilities for computing are one of the prime utilities of life after the other main essential utilities i.e., water, gas, and electricity. With the help of virtualization, cloud computing helps in transparently sharing of data and services between the cloud users and provides access to more than thousands of computers as a single instance. The virtual machine allocation is a challenging task in virtualization which is regulated as a crucial part of the virtual machine (VM) migration. It is done to discover the best assignment of VMs on physical machines (PMs) because it has explicit effects on resource utilization, energy efficiency, the performance of running applications, etc. So, there is a need to design an effective method for the VM placement problem. The research paper presents a virtual machine allocation technique implementing Enhanced-Modified Best Fit Decreasing (E-MBFD) Algorithm. The outcome of the proposed technique is the cross-validation of allocated virtual machines on physical machines using Artificial Neural Network (ANN). Also, the proposed technique is implemented with the merits of finding the false allocations which happen due to inefficient utilization of resources and it helps in re-allocation of these VMs. The empirical result shows that the E-MBFD technique outperforms in terms of minimized power consumption and a lesser number of service-level agreement (SLA) violations in contrast with the conventional technique.
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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 [131]
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Energy efficiency in cloud computing data centers: a survey on software technologies
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Metadata
- Authors
- Shalu, & Singh, D.
- Affiliations
- Department of Computer Science & Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonepat 131039 Haryana, India
- Venue
- Journal of Discrete Mathematical Sciences and Cryptography
- Publisher
- Taru Publications
- Published
- 2021-04-27
- Volume
- 24
- Issue
- 6
- Pages
- 1739-1750
- ISSN
- 0972-0529 (ISSN-L)
- 2169-0065
- OpenAlex
- W3157266456
- Cited by
- 28
- References
- 16
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 4 · 2023: 7 · 2022: 7 · 2021: 3
- FWCI
- 4.75
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Discrete Mathematical Sciences and Cryptography (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, cloud computing, virtualization, artificial neural networks, resource allocation, energy efficiency, power consumption, service level agreement violation, virtual machine migration
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