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
- N. Saranya, M. Kalpana Devi, A. Mythili, Summia P. H, Data science and machine learning methods for detecting credit card fraud , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Finney D. Shadrach, Harsshini S, Darshini R, Grapevine leaf species and disease detection using DNN , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Pavani Guntaka, M. Changal Raju, Mopuri Obulesu, A numerical study of unsteady MHD free convection flow with heat and mass transfer across an inclined porous plate, taking hall current and dufour effects by FDM , The Scientific Temper: Vol. 16 No. 06 (2025): The Scientific Temper
- Adedotun Adedayo F, Odusanya Oluwaseun A, Adesina Olumide S, Adeyiga J. A, Okagbue, Hilary I, Oyewole O, Prediction of automobile insurance fraud claims using machine learning , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Gurpreet S. Saund, Kulandai Samy, Eco-critical dystopia and anthropocentrism in Margaret Atwood’s Oryx and Crake , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Ayalew Ali, Sitotaw Wodajo, Taye Teshoma, The link between corporate governance and earnings management of insurance companies in Ethiopia , The Scientific Temper: Vol. 16 No. 07 (2025): 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
- Mantsha Rayeen, Roshni Sengupta, Sanjay Chaudhary, Short-term changes in lens vault post implantable collamer lens surgery in myopic patients , The Scientific Temper: Vol. 16 No. 07 (2025): The Scientific Temper
- Ravikiran K, Neerav Nishant, M Sreedhar, N.Kavitha, Mathur N Kathiravan, Geetha A, Deep learning methods and integrated digital image processing techniques for detecting and evaluating wheat stripe rust disease , The Scientific Temper: Vol. 14 No. 03 (2023): 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
<< < 16 17 18 19 20 21 22 23 24 25 > >>
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
