Enhancing User Code Efficiency in Edge Computing Applications through Machine Learning-Driven Optimization

This research proposal addresses the critical challenge of code efficiency optimization in an edge computing environment that is limited by computational resources, dynamically changing network conditions, and real-time processing. Traditional optimization methods are usually not effective for these resource-constrained and dynamic environments; thus, inefficiency can modify the performance and energy consumption accordingly. In this regard, the following research proposes a DQN-based optimization framework drawing on the specifics of edge computing applications. The key objective of the research will be the design, implementation, and validation of the reinforcement learning framework which will dynamically optimize code execution through intelligent management of the underlying resources and adaptation to changing conditions in real time. This will be done in various steps: a thorough literature review, formulation of the problem, and then the development that involves the DQN model. The framework will also be tested on a simulated environment designed to emulate the complexities of edge computing and later deployed real-world for validation. These would be envisaged to achieve significant reductions in execution time, energy consumption, and efficient usage of resources-all very crucial for efficiency and scalability in edge computing systems. This will also contribute to the academic understanding of how reinforcement learning could be applied to performance optimization in distributed computing environments. This work will bridge the gap in theoretical models and practical applications to further extend the edge computing area and act as the basis for future innovations of machine learning for system optimization.