Optimizing Resource Allocation to Edge Devices in Edge Computing: Exploring Multi-objectives Optimization Techniques

In the burgeoning domain of edge computing, efficient resource allocation to edge devices plays a pivotal role in enhancing system performance and user experience. This paper delves into the intricacies of optimizing resource allocation for edge devices by exploring multi-objective optimization techniques. We specifically focus on three crucial goals: minimizing distance, maximizing processing capacity, and maximizing the probability of success for each edge device. Through extensive experimentation and analysis, we demonstrate the effectiveness of employing the Non-dominated Sorting Genetic Algorithm II(NSGA-II) in achieving optimal solutions for these objectives. Our findings showcase that the NSGA-II algorithm outperforms other methods, providing a compelling demonstration of its efficacy in addressing the challenges associated with multi-objective resource allocation in edge computing environments. This paper not only contributes valuable insights into the realm of edge computing but also serves as a practical guide for researchers and practitioners seeking to optimize resource allocation in similar contexts.