Optimizing Resource Allocation in Edge Computing Networks Through Advanced Optimization Technique

Cloud and edge computing are integrated to form the edge cloud, where the resources are utilized to enhance performance by determining the data source and computation in the cloud resource. The advancement of edge computing has changed by the methods of data processing and it uses the computation closer to the data resource. It is crucial to generate an effective resource allocation for performing the optimization and reducing delays. All these necessary factors are rarely addressed by the existing literature, which impacts the Quality of Service (QoS). The novelty of this framework is to implement an optimal resource allocation strategy for edge computing environments to enhance system efficiency and ensure scalable performance. To accomplish this, an African Bison Optimization (ABO) Algorithm is utilized. By addressing multiple objectives like energy efficiency, total cost, and task delays, the proposed solution is dynamically allocating resources and scheduling tasks. The developed model is useful in managing transmission latency and computational power. Simulation experimentation are analyzed to validate the functionality of the suggested method among other resource management strategies for edge computing systems.