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
- Moyliev Gayrat, Yunuskhodjaev Akhmadkhodja, Saidov Saidamir, Babakhanov Otabek, Mirsultanov Jakhongir, To study references and analysis of an experimental model for skin burns in rats , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- M. Menaha, J. Lavanya, Crop yield prediction in diverse environmental conditions using ensemble learning , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Shiny Bridgette I, Rexlin Jeyakumari S, An optimal fuzzy inventory model for rice farming using lagrangean method , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Rupesh Mandal, Bobby Sharma, Dibyajyoti Chutia , Smart flood monitoring in Guwahati city: A LoRa-based AIoT and edge computing sensor framework , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Sudheer Choudari, K. Rajasekhar, Ch. Sudheer, Comparative study of the foundation model of a 220 kV transmission line tower with different footing steps - Finite element analysis , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Ravi Kumar P, C. Gowri Shankar, Optimizing power converters for enhanced electric vehicle propulsion: A novel research methodology , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Olivia C. Gold, Jayasimman Lawrence, Ensemble of CatBoost and neural networks with hybrid feature selection for enhanced heart disease prediction , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- V Babydeepa, K. Sindhu, A hybrid feature selection and generative adversarial network for lung and uterus cancer prediction with big data , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Renuka Thapliyal, Can Shimla be fitted into the compact city model? , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Punithavathy E, N. Priya, A resilience framework for fault-tolerance in cloud-based microservice applications , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
<< < 4 5 6 7 8 9 10 11 12 13 > >>
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
