Optimizing Resource Allocation in Edge Computing Environments using Reinforcement Learning

Thesis Abstract

Abstract


Edge computing has emerged as a promising paradigm for processing data closer to the source, reducing latency and bandwidth usage. Efficient resource allocation in edge computing environments is crucial for optimizing performance and minimizing operational costs. This research focuses on the application of reinforcement learning techniques to address the challenges of resource allocation in edge computing. The study begins with a comprehensive review of edge computing, resource allocation challenges, and existing approaches. A detailed methodology for data analysis, reinforcement learning algorithm selection, and experimentation setup is presented. The implementation phase involves the development of a resource allocation framework, integration of reinforcement learning models, and performance evaluation. The results are analyzed, compared with traditional methods, and visualized to demonstrate the improvements achieved. The thesis concludes with a summary of research contributions, implications, and recommendations for future work in the field of resource allocation in edge computing. This research is expected to provide valuable insights and practical solutions for optimizing resource allocation in edge computing environments using reinforcement learning.