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
- Amala Deepa V., T. Lucia Agnes Beena, Enhancing data imputation in complex datasets using Lagrange polynomial interpolation and hot-deck fusion , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- Ganga Gudi, Mallamma V Reddy, Hanumanthappa M, Enhancing Kannada text-to-speech and braille conversion with deep learning for the visually impaired , The Scientific Temper: Vol. 16 No. Spl-1 (2025): The Scientific Temper
- Sirajum Munira Priety, Farhan Bin Manjur, AI Driven Approach in Smart Manufacturing in Bangladesh , The Scientific Temper: Vol. 16 No. 10 (2025): The Scientific Temper
- Deepesh Bhardwaj, Niyati Chaudhary, Green Premium: Assessing the Influence of Sustainability Features on Real Estate Market Value in Delhi NCR , The Scientific Temper: Vol. 17 No. 01 (2026): The Scientific Temper
- Chandrasekaran M, Rajesh P K, Optimization of cost to customer of power train in commercial vehicle using knapsack dynamic programming influenced by vehicle IoT data , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- V. Parimala, D. Ganeshkumar, Solar energy-driven water distillation with nanoparticle integration for enhanced efficiency, sustainability, and potable water production in arid regions , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- Amanda Quist Okronipa, Isaac Asampana, Jones Yeboah Nyame, Exploring e-learning system loyalty: The role of system quality and satisfaction , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Mohiyuddeen Hafzal, Gayathri B.J., M. Meghana Shet, Shaping the future: Education and skill development for Viksit Bharat@2047 , The Scientific Temper: Vol. 15 No. spl-2 (2024): The Scientific Temper
- Josephine Theresa S, A Framework for Environment Thermal Comfort Prediction Model , The Scientific Temper: Vol. 16 No. 12 (2025): The Scientific Temper
- Amresh Kumar Singh, Manjit Singh Chhetri, Pushyamitra Mishra, Toughness and Ductile Brittle Transition Temperature of Different Mineral Filler Reinforced TPOs Composites , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
<< < 20 21 22 23 24 25 26 27 28 29 > >>
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

