Tools
Tools
Kallepalli, Sumanth Varma (2024) Machine Learning-Based Improved Cold Start Latency Prediction Framework in Serverless Computing. Masters thesis, Dublin, National College of Ireland.
Preview |
PDF (Master of Science)
Download (1MB) | Preview |
Preview |
PDF (Configuration Manual)
Download (782kB) | Preview |
Abstract
In the contemporary world of cloud computing, serverless computing is the game-changer paradigm that enables on-demand, scalable, and cost-effective ways to deploy applications. One such critical problem related to performance and user experience in serverless computing is cold start latency. This paper introduces a machine learning framework for the prediction and improvement of cold start latency with the objective of enhancing serverless computing efficiency. We develop predictive models for cold start anticipation and duration prediction using machine learning techniques based on historical data analysis, together with real-time metrics. Machine-learning-based regression, decision trees, and neural networks have to be developed addressing patterns and correlating traditional techniques that are incapable of doing this task. We show that the built predictive models are able to forecast cold starts accurately, thus enabling strategies for resource allocation and optimization in a proactive manner. This not only helps in making applications more responsive but also gives a fillip to efficiency in resource utilization an important factor in reining costs for the service provider. Also, we design the framework to adapt and scale to the dynamic nature of serverless environments. Through integration with cloud infrastructure, it seamlessly augments the current state-of-the-art in offerings for serverless. This research fills an important gap in performance through effective means for the practical elevation of deployment and execution of serverless applications. This machine learning approach therefore represents a substantial step forward in the optimization of serverless computing for more effective and efficient cloud services.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Gupta, Shaguna UNSPECIFIED |
| Uncontrolled Keywords: | Cloud Computing; Machine Learning; Cost Efficiency; Cold Start Latency |
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Cloud computing T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > Computer networks > Internet of things Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning |
| Divisions: | School of Computing > Master of Science in Cloud Computing |
| Depositing User: | Ciara O'Brien |
| Date Deposited: | 03 Jul 2025 11:10 |
| Last Modified: | 03 Jul 2025 11:10 |
| URI: | https://norma.ncirl.ie/id/eprint/8023 |
Actions (login required)
 |
View Item |