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
- Abhishek Dwivedi, Nikhat Raza Khan, Reconfiguration of Automated Manufacturing Systems Using Gated Graph Neural Networks , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
- Vinodini R, Ritha W, Sasitharan Nagapan, The green inventory model for sustainable environment that includes degrading products and backordering with integration of environmental cost , The Scientific Temper: Vol. 16 No. 08 (2025): The Scientific Temper
- Bayelign Abebe Zelalem, Ayalew Ali Abebe, Dividend policy and banks’ performance: Assessing the relevance versus irrelevance theory , The Scientific Temper: Vol. 16 No. 05 (2025): The Scientific Temper
- U. Johns Praveena, J. Merline Vinotha, Bilevel Fractional/Quadratic Green Transshipment Problem by Implementing AI traffic control system with Multi Choice Parameters Under Fuzzy Environment , The Scientific Temper: Vol. 16 No. 11 (2025): The Scientific Temper
- P Janavarthini, I Antonitte Vinoline, Sustainable fuzzy inventory for concurrent fabrication and material depletion modeling with random substandard items , The Scientific Temper: Vol. 16 No. 04 (2025): The Scientific Temper
- Temesgen A. Asfaw, Batch size impact on enset leaf disease detection , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- Pravin P. Adivarekar1, Amarnath Prabhakaran A, Sukhwinder Sharma, Divya P, Muniyandy Elangovan, Ravi Rastogi, Automated machine learning and neural architecture optimization , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Bhavika Bhagyesh Lad, Sonam Mansukhani, Applying the risk-need-responsivity model in juvenile offender treatment: A conceptual framework , The Scientific Temper: Vol. 15 No. spl-2 (2024): The Scientific Temper
- M. Monika, J. Merline Vinotha, A Sustainable Vendor–Buyer Supply Chain Framework Integrating Energy Storage Systems and Green Investments with Incentive Policies under Demand Uncertainty , The Scientific Temper: Vol. 17 No. 02 (2026): The Scientific Temper
- Sanjeev Kumar, Saurabh Charaya, Rachna Mehta, Multi-Metric Evaluation Framework for Machine Learning-Based Load Prediction in e-Governance Systems , The Scientific Temper: Vol. 17 No. 01 (2026): The Scientific Temper
<< < 2 3 4 5 6 7 8 9 10 11 > >>
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
