Selective Offloading Network Resource Optimization Approaches in Collaborative Cloud-Edge Computing Networks

Abstract

With the rapid development of Internet of Things (IoT), 5G, and cloud computing, a large number of emerging applications has emerged, such as mobile payment and self-driving cars. Collaborative cloud-edge computing networks (CCECNs) is an effective way to alleviate the limitation of computing capacity by offloading complex tasks from IoT. Thus, this paper mainly proposes selective offloading network resource optimization approaches to address the offloading network resource problems for user requests in CCECNs. Simulation results show that proposed approaches can optimize network resource allocation and can reduce end-to-end (E2E) latency and blocking probability.