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
- 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
- Anvar Mavlonov , Saidamir Saidov , Jakhongir Mirsultanov, Rano Boboeva , The Features of bone destruction in rabbits with experimental metabolic syndrome , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- Kapil ahuja, Ekta Rani, Soniya Devi, Exploring the dynamic landscape of environmental, social, and governance literature by using bibliometric analysis , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- K. R. R. Prakash, Kishore Kunal, Designing information systems for business administration through human and computer interaction , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Sangeeta ., Jitander S. Sikka, Meenal Malik, Static deformation of a two-phase medium consisting of a rigid boundary elastic layer and an isotropic elastic half-space induced by a very long tensile fault , The Scientific Temper: Vol. 15 No. 02 (2024): 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
- M. Rajalakshmi, V. Sulochana, Enhancing deep learning model performance in air quality classification through probabilistic hyperparameter tuning with tree-structured Parzen estimators , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Neeraj ., Anita Singhrova, Quantum Key Distribution-based Techniques in IoT , 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
- Deepa S, Sripriya T, Radhika M, Jeneetha J. J, Experimental evaluation of artificial intelligence assisted heart disease prediction using deep learning principle , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
<< < 11 12 13 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
