Integration of Machine Learning with MEC for Intelligent Applications

Abstract

∗In recent years, telecom operators and large companies are eager to obtain value from the edge of the network, and the priority of cloud computing has been transferred from the center to the edge. In addition, with the comprehensive deployment of 5G base station (BS), the number of 5G users has been largely increased. For 5G users, they expect to have a better experience of high bandwidth and low latency. Thus, the Mobile Edge Computing (MEC) came into being. MEC brings the capability from the center to the edge of the mobile network. Requests and data of User equipment (UE) has been underlined in MEC. These requests and data will be analyzed and disposed at the edge without being uploaded to the cloud center, which diminishes the latency efficiently. Besides, with the help of machine learning, MEC can show a better performance. This paper is aimed at studying superiorities of MEC itself and integration of machine learning with MEC, and intelligent applications they will bring. This paper first discusses the concept and architecture of MEC, then the advantages of MEC are listed. Next, the improvements of integration of machine learning with MEC and the intelligent applications which employ these technologies will be introduced. Finally, the deficiencies and future research trend of MEC will be discussed. After that, conclusion can be drought that MEC augment the performance of speed, security and privacy, energy saving and reliability. Furthermore, integration of machine learning with MEC can provide better resource management and offloading decision.