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
- Avdhesh Kumar, Manoj Agarwal, Studies on challenges and opportunities for foreign direct investment in the automobile industry in India , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- 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
- 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
- P. Ananthi, A. Chandrabose, The socio-technical opportunities and threats of crowdsensing , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Venkatesh R, A study on women empowerment by enhancing saving capabilities – through self-help groups , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Josephine Theresa S, Graph Neural Network Ensemble with Particle Swarm Optimization for Privacy-Preserving Thermal Comfort Prediction , The Scientific Temper: Vol. 16 No. 12 (2025): The Scientific Temper
- Santosh Kumar Sahu, B. R. Senthil kumar, Y. Aboobucker parvez, Ashish Verma, Assessment of noise levels by using noise prediction modeling , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Deneshkumar V, Jebitha R, Jithu G, Multistate modeling for estimating clinical outcomes of COVID-19 patients , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Neha Verma, Beyond likes & clicks: Empowering role of social media marketing in value creation , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- R. Thiagarajan, S. Prakash Kumar, Performance of public transport appraisal using machine learning , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
<< < 23 24 25 26 27 28 29 30 31 > >>
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
