Minimizing Cold Starts in Serverless Environments with Predictive Optimization Approach Using Bi-LSTM and Genetic Algorithms

Khan, Moiz Ahmed Nurul Hasan (2024) Minimizing Cold Starts in Serverless Environments with Predictive Optimization Approach Using Bi-LSTM and Genetic Algorithms. Masters thesis, Dublin, National College of Ireland.

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Abstract

Serverless computing has revolutionized cloud applications because it only focuses on the server as an application service. However, the ”cold start” problem, where functions experience latency that is significantly higher when these functions have been inactive for a while, remains a critical issue when it comes to function performance. Prior work has suggested various solutions, such as static pre-warming techniques and partial runtime environments. Although these methods bring enhancements, they lead to the expenditure of more resources and are unsuitable for varying workloads. These shortcomings are mitigated in the current research by employing Bidirectional Long Short-Term Memory (Bi-LSTM) neural networks and Genetic Algorithms for a predictive optimization strategy. The Bi-LSTM model forecasts future cold start events following prior invocation probabilities, while GA enhances pre-warming strategies during runtime to reduce latency and resource consumption. This approach is different from previous methods in that it provides scalability and performance-optimized solutions, as required by the workload in real time. The solution used AWS Lambda in a serverless framework where performance was assessed relative to accuracy, precision, recall, and F1-score. The results prove that there are substantial enhancements in the field of cold start latency as well as application performance that testify to the reliability of the proposed predictive optimization strategy. The present research makes an innovative contribution by developing a model and using optimization methods that can be easily implemented and are cost-optimized to solve a well-known problem in the context of serverless computing.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Kazmi, Aqeel UNSPECIFIED
Uncontrolled Keywords:	Serverless Computing; Cold Start; Predictive Optimization; BiLSTM; Genetic Algorithms; AWS Lambda
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 Q Science > QA Mathematics > Algebra > Algorithms > Computer algorithms
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Ciara O'Brien
Date Deposited:	03 Jul 2025 11:17
Last Modified:	03 Jul 2025 11:17
URI:	https://norma.ncirl.ie/id/eprint/8024

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