Hybrid Golden Jackal and Whale Optimization Algorithm (HGJWOA) for Optimizing Resource and Service Allocation in Edge Computing Environments

Edge computing has emerged as a promising paradigm to address the latency and bandwidth constraints of traditional cloud computing by bringing computation and storage closer to the data source. In edge computing environments, efficient resource and service allocation are crucial for optimizing performance and meeting user requirements. This paper proposes a novel approach for optimizing resource and service allocation in edge computing environments using a hybrid algorithm (HGJWOA)combining Golden Jackal Optimization (GJO) and Whale Optimization Algorithm (WOA). The hybridization of GJO and WOA aims to leverage the strengths of both algorithms to achieve superior optimization performance. We develop a framework for resource and service allocation, formulate the optimization problem, and define evaluation metrics. The hybrid algorithm is integrated into the framework and evaluated through extensive simulations. Experimental results demonstrate the effectiveness of the proposed approach in improving resource utilization, reducing latency, and enhancing service delivery in edge computing environments compared to traditional optimization algorithms. Additionally, case studies and real-world deployment scenarios illustrate the practical applicability and benefits of the hybrid algorithm. Overall, the proposed hybrid Golden Jackal and Whale Optimization Algorithm presents a promising solution for optimizing resource and service allocation in edge computing environments, contributing to the advancement of efficient and scalable edge computing systems.