Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Technological evolution of mobile user equipment (TIEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the TIEs is constrained by limited battery capacity and energy consumption of the TIEs. A suitable solution extending the battery life-time of the TIEs is to offload the applications demanding huge processing to a conventional centralized cloud. Nevertheless, this option introduces significant execution delay consisting of delivery of the off loaded applications to the cloud and back plus time of the computation at the cloud. Such a delay is inconvenient and makes the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling it to run the highly demanding applications at the TIE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: 1) decision on computation offloading; 2) allocation of computing resource within the MEC; and 3) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.

I. Introduction

The users’ requirements on data rates and quality of service (QoS) are exponentially increasing. Moreover, technological evolution of smartphones, laptops and tablets enables to emerge new high demanding services and applications. Although new mobile devices are more and more powerful in terms of central processing unit (CPU), even these may not be able to handle the applications requiring huge processing in a short time. Moreover, high battery consumption still poses a significant obstacle restricting the users to fully enjoy highly demanding applications on their own devices. This motivates development of mobile cloud computing (MCC) concept allowing cloud computing for mobile users [1]. In the MCC, a user equipment (UE) may exploit computing and storage resources of powerful distant centralized clouds (CC), which are accessible through a core network (CN) of a mobile operator and the Internet. The MCC brings several advantages [2]; 1) extending battery lifetime by offloading energy consuming computations of the applications to the cloud, 2) enabling sophisticated applications to the mobile users, and 3) providing higher data storage capabilities to the users. Nevertheless, the MCC also imposes huge additional load both on radio and backhaul of mobile networks and introduces high latency since data is sent to powerful farm of servers that are, in terms of network topology, far away from the users.