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
- Sheena Edavalath, Manikandasaran S. Sundaram, Cost-based resource allocation method for efficient allocation of resources in a heterogeneous cloud environment , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- V. Selvi, T. S. Poornappriya, R. Balasubramani, Cloud computing research productivity and collaboration: A scientometric perspective , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- O. Devipriya, K. Kungumaraj, Enhancing cloud efficiency: an intelligent virtual machine selection and migration approach for VM consolidation , 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
- Raja Selvaraj, Manikandasaran S. Sundari, EAM: Enhanced authentication method to ensure the authenticity and integrity of the data in VM migration to the cloud environment , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
- Raja Selvaraj, Manikandasaran S Sundaram, ECM: Enhanced confidentiality method to ensure the secure migration of data in VM to cloud environment , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- V. Baby Deepa, R. Jeya, Dynamic resource allocation with otpimization techniques for qos in cloud computing , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Aruljothi Rajasekaran, Jemima Priyadarsini R., ECDS: Enhanced Cloud Data Security Technique to Protect Data Being Stored in Cloud Infrastructure , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Sheena Edavalath, Manikandasaran S. Sundaram, MARCR: Method of allocating resources based on cost of the resources in a heterogeneous cloud environment , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- P. S. Dheepika, V. Umadevi, An optimized approach for detection and mitigation of DDoS attack cloud using an ensembled deep learning approach , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
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