Dynamic Service Placement in Edge Computing A Comparative Evaluation of Nature-Inspired Algorithms

This project focuses on dynamic service placement within edge computing environments, utilizing nature-inspired algorithms to enhance service offloading and computational efficiency. The system supports both administrators and users, with functionalities such as registration, login, and simulation results. It offers seamless management of services, including the ability to upload application details, select appropriate algorithms, and run simulations to view results, alongside generating detailed reports. The system architecture includes a front-end developed using HTML, CSS, and ReactJS, while the back-end is powered by Java with Spring Boot and MySQL database for storing user and application data. Key optimization techniques evaluated in this project include Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Reinforcement Learning-based methods. This work aims to provide insights into selecting the most suitable placement algorithm to ensure efficient resource utilization, minimize latency, and satisfy Quality of Service (QoS) requirements.

Keywords: Edge Computing, Dynamic Service Placement, Nature-Inspired Algorithms, Optimization, Particle Swarm Optimization, Genetic Algorithm, Reinforcement Learning, QoS, Spring Boot, ReactJS, MySQL