Coordinated Optimization Of Edge Computing and Unloading for Portable Devices

Abstract

The rapid use of mobile devices, which enable a wide range of applications, results in a massive increase in mobile traffic. Mobile devices must offload computational work to cloud servers to increase resource use due to their resource limitations. The traditional Mobile Cloud Computing (MCC) system has a significant transmission delay, which is unfortunate. Mobile Edge Computation (MEC), which lately promises a significant reduction in latency by moving mobile computing and storage to the network edge, was inspired by the visions of the Internet of Things and 5G communications (i.e., base stations and access points). Finding an effective task assignment using local or remote devices while minimizing energy consumption and latency is the main difficulty of MEC solutions. An enormous increase in mobile traffic results from the high prevalence of mobile devices and their wide range of applications. Mobile devices use cloud servers to offload computational work due to their resource constraints, which increases resource utilization. The traditional Mobile Cloud Computing (MCC) system, however, has a high transmission delay. Recently, Mobile Edge Computation (MEC), which is motivated by the ideas of the Internet of Things and 5G communications, has promised a significant reduction in latency by moving mobile computing and storage to the network edge (i.e., base stations and access points). Finding an effective way to assign jobs to local or remote devices while reducing latency and energy consumption is the main problem facing MEC solutions.