Expanding the quantity of virtual machines utilized within an open-source cloud infrastructure
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2024.15.spl.37Keywords:
Cloud computing, Virtual machine allocation, VM migration, VM deployments, Cloud infrastructure.Dimensions Badge
Issue
Section
License
Copyright (c) 2024 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
As cloud computing continues to evolve, the efficient management and scalability of virtual machines (VMs) have become pivotal for maximizing performance and resource utilization, particularly within open-source cloud infrastructures. This literature review investigates existing approaches and methodologies focused on expanding the number of VMs in open-source cloud environments. Key topics include the impact of VM scaling on resource allocation, load balancing, and energy efficiency, as well as the role of orchestration tools and hypervisor optimization in handling large-scale VM deployments. Furthermore, the review assesses the challenges related to VM density, network latency, and system reliability alongside emerging strategies for enhancing VM elasticity through containerization, microservices, and distributed computing models. This study aims to provide a comprehensive understanding of current trends, innovations, and limitations in VM expansion, offering insights into the future of scalable virtual infrastructures in open-source cloud systems.Abstract
How to Cite
Downloads
Similar Articles
- K. Vani, S. Sujatha, Fault tolerance systems in open source cloud computing environments–A systematic review , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Sabeerath K, Manikandasaran S. Sundaram, BTEDD: Block-level tokens for efficient data deduplication in public cloud infrastructures , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- K. Mohamed Arif Khan, A.R. Mohamed Shanavas, Optimizing IoT application deployment with fog - cloud paradigm: A resource-aware approach , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- A. Anand, A. Nisha Jebaseeli, AI-driven real-time performance optimization and comparison of virtual machines and containers in cloud environments , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Ellakkiya Mathanraj, Ravi N. Reddy, Enhanced principal component gradient round-robin load balancing in cloud computing , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- A. Kalaiselvi, A. Chandrabose, Fuzzy logic-driven scheduling for cloud computing operations: a dynamic and adaptive approach , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- A. Jabeen, A. R. M. Shanavas, Hazard regressive multipoint elitist spiral search optimization for resource efficient task scheduling in cloud computing , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- J. M. Aslam, K. M. Kumar, Enhancing security of cloud using static IP techniques , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- J. M. Aslam, K. M. Kumar, Enhancing cloud data security: User-centric approaches and advanced mechanisms , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- M. Iniyan, A. Banumathi, Brower blowfish nash secured stochastic neural network based disease diagnosis for medical WBAN in cloud environment , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.
Most read articles by the same author(s)
- M. Prabhu, A. Chandrabose, Optimization based energy aware scheduling in wireless sensor networks , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- M. Jayakandan, A. Chandrabose, An ensemble-based approach for sentiment analysis of covid-19 Twitter data using machine learning and deep learning techniques , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- P. Ananthi, A. Chandrabose, Exploring learning-assisted optimization for mobile crowd sensing , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- P. Ananthi, A. Chandrabose, The socio-technical opportunities and threats of crowdsensing , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- M. Prabhu, A. Chandrabose, Improving the resource allocation with enhanced learning in wireless sensor networks , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- A. Kalaiselvi, A. Chandrabose, Fuzzy logic-driven scheduling for cloud computing operations: a dynamic and adaptive approach , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- D. Jayadurga, A. Chandrabose, Distribution of virtual machines with SVM-FFDM approach in cloud computing , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper