A comparative analysis of virtual machines and containers using queuing models
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2024.15.spl.01Keywords:
Docker container, Virtual machines, Queuing model, Cloud computing.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.
Virtual machines (VMs) and containers are two prevalent technologies in cloud computing, each offering distinct advantages depending on the use case. VMs emulate entire operating systems, including kernels, while containers share the host OS kernel, making them lightweight and resource-efficient. This paper presents a novel method for comparing the performance of VMs and containers using queuing models. The proposed method not only provides a more accurate and flexible comparison but also significantly reduces the time required to calculate and perform performance metrics compared to traditional empirical benchmarking and simulation-based approaches. Through this comparison, the paper highlights the conditions under which containers outperform VMs, particularly in modern, cloud-native environments.Abstract
How to Cite
Downloads
Similar Articles
- Sabana Backer, Prasanth A.P, The influence of attitude on green-cosmetics purchase intention (pi) in central Kerala , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Kamna Kandpal, Piyashi Dutta, P.Sasikala Ravichandran, Examining the relationship between motivation and incentives in the context of maternal health awareness: A study of Asha workers in Uttarakhand , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Sivasankar G. A, Study of hybrid fuel injectors for aircraft engines , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Mohamed Azharudheen A, Vijayalakshmi V, Improvement of data analysis and protection using novel privacy-preserving methods for big data application , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- K. Kalaiselvi, M. Kasthuri, Tuning VGG19 hyperparameters for improved pneumonia classification , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Goutam Mandal, Baibaswata Bhattacharjee, Biosynthesis of ZnO nanoparticles using the young fruit of Borassus flabellifer: Characterization and photocatalytic removal of biohazardous safranin-O dye using solar irradiation , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Akshay J., G. Mahesh Kumar, B. H. Manjunath, Optimizing durability of the thin white topping applying Taguchi method using desirability function , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Somalee Mahapatra, Manoranjan Dash, Subhashis Mohanty, Adoption of artificial intelligence and the internet of things in dental biomedical waste management , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Archana Verma, Role of artificial intelligence in evaluating autism spectrum disorder , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Archana Verma, Application of metaverse technologies and artificial intelligence in smart cities , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
<< < 14 15 16 17 18 19 20 > >>
You may also start an advanced similarity search for this article.
Most read articles by the same author(s)
- 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
- S. Bhuvaneswari, A. Nisha Jebaseeli, Multi-model telecom churn prediction , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper