Mobile cloud computing (MCC) integrates cloud computing (CC) into mobile networks, prolonging the battery life of the mobile users (MUs). However, this mode may cause significant execution delay. To address the delay issue, a new mode known as mobile edge computing (MEC) has been proposed. MEC provides computing and storage service for the edge of network, which enables MUs to execute applications efficiently and meet the delay requirements. In this paper, we present a comprehensive survey of the MEC research from the perspective of service adoption and provision. We first describe the overview of MEC, including the definition, architecture, and service of MEC. After that we review the existing MUs-oriented service adoption of MEC, i.e., offloading. More specifically, the study on offloading is divided into two key taxonomies: computation offloading and data offloading. In addition, each of them is further divided into single MU offloading scheme and multi-MU offloading scheme. Then we survey edge server- (ES-) oriented service provision, including technical indicators, ES placement, and resource allocation. In addition, other issues like applications on MEC and open issues are investigated. Finally, we conclude the paper.
In recent years, with the continuous development of cloud computing (CC), big data, mobile network, software defined network (SDN), and upgrading of intelligent mobile terminals [
However, cloud is usually located far away from the MUs, which leads to high network delay for data transmission between MUs and cloud. In order to solve the issue of network delay, a new paradigm named mobile edge computing (MEC) has been proposed. The MEC can be seen as a specific case of the MCC.
A number of surveys on MEC have been published recently [
Different from the existing surveys, we provide a comprehensive survey of the state-of-the-art MEC research, focusing on service adoption and provision. More specifically, MEC is mainly composed of two parts, including MU and edge server (ES). On the one hand, we surveyed MUs-oriented service adoption, such as computation offloading and data offloading. On the other hand, ES-oriented service provision, including ES deployment and resource allocation, is investigated.
The summary of abbreviations in this paper is shown in Table
Summary of abbreviations.
Access Point(s) | AP(s) |
Base Stations | BSs |
Cloud Computing | CC |
Deep Supervised Learning | DSL |
Dynamic Voltage Scaling | DVS |
Edge Computing | EC |
Edge Server(s) | ES(s) |
Efficient Computation Offloading | ECO |
End-to-End | E2E |
Energy-Efficient Computation Offloading | EECO |
European Telecommunications Standards Institute | ETSI |
Fiber-Wireless | FiWi |
Fog Computing | FC |
Full Duplex | FD |
Green-energy Aware Avatar Placement | GAP |
Incentive-compatible Auction Mechanism | ICAM |
Integer Linear Programming | ILP |
Internet of Things | IoT |
Intrusion Detection System | IDS |
Iterative Improvement | Π |
Markov Decision Process | MDP |
Mixed Integer Linear Programming | MILP |
Mobile Cloud Computing | MCC |
Mobile Data Traffic | MDT |
Mobile Data Traffic Offloading | MDTO |
Mobile Edge Computing | MEC |
Mobile Edge Internet of Things | MEIoT |
Mobile Edge Computing-wireless Power Transfer | MEC-WPT |
Multiple Knapsack Problem | MKP |
Multi-objective Optimization Problem | MOOP |
Multi-user Computation Offloading | MUCO |
Multi-user Data Offloading | MUDO |
Mobile User(s) | MU(s) |
Physical Resource Block | PRB |
Profit Maximization Avatar Placement | PRIMAL |
Quality of Experience | QoE |
Quality of Service | QoS |
Radio Access Networks | RANs |
Radio Access Points | RAPs |
Software Defined Networking | SDN |
Stochastic Reward Net | SRN |
Two-Phase Optimization | TPO |
Vehicular Ad Hoc Networks | VANET |
Unmanned Aerial Vehicles | UAVs |
Vehicular Cyber-physical Systems | VCPSs |
Virtual Machine | VM |
Wireless Access Point(s) | WAP(s) |
In this section, we introduce the definition and architecture of MEC in Section
Summary of literatures on different modes.
Item | Related work | Key points |
---|---|---|
MEC | Definition [ | [ |
Architecture [ | [ | |
Different network types [ | [ | |
| ||
Comparison of several modes | [ | [ |
| ||
MCC | [ | [ |
| ||
FC | [ | [ |
| ||
Cloudlet | [ | [ |
MEC is a new network paradigm that provides information technology services and CC capabilities within the mobile access network of MUs and has become a technology. European Telecommunications Standards Institute (ETSI) proposed standardization of MEC in 2014 [
In this paper, we mainly focus on the MEC and present a comprehensive survey from the perspective of service of MEC. More specially, we mainly review the service adoption of MU’s and service provision of ES’. MUs in this paper can be mobile phones, tablets, and other intelligent devices. In fact, it is not very critical whether MUs have computing capability or not. The main issue of MUs is offloading to ESs. Meanwhile, the ES placement and resource allocation of ES is very important.
As shown in Figure
Mobile edge computing (MEC) architecture.
In order to better understand of MEC, the authors in [
In this section, we introduce the existing surveys on conceptual comparison in Section
Comparison of EC implementations: fog computing, cloudlet, and MEC are shown in [
In this section, similar terms, such as mobile cloud computing (MCC), fog computing (FC), and cloudlets, are introduced and compared.
As shown in Figure
Mobile cloud computing (MCC) architecture.
In this section, we review the research on offloading. A summary of literatures on MUs-oriented service adoption are shown in Table
Summary of literatures on MUs-oriented service adoption.
Work area | Related Work | Key Points |
---|---|---|
Single MUCO | Cloudlet based single MUCO [ | [ |
| ||
Multi-MUCO | Cloudlet based multi-MUCO [ | [ |
| ||
Single MUDO | [ | [ |
| ||
Multi MUDO | [ | [ |
In [
We review computation offloading in Section
Computation offloading was originally studied in MCC. The offloading problem model in MEC is similar to MCC, but the main difference is the destination of offloading. It is commonly assumed that the implementation of computation offloading relies on a network architecture with the public cloud, while the offloading destination of MEC is ES. In addition, the offloading goals are basically the same which is to minimize total energy consumption or overall task execution time, or both of them. Furthermore, MEC computation offloading is divided into single mobile user computation offloading (single MUCO) and multiple mobile user computation offloading (Multi-MUCO).
In [
In [
Similarly, DO also can be divvied into single MU data offloading (Single MUDO) and multi-MU data offloading (Multi-MUDO).
In this section, we mainly review the ES-oriented service provision. In Section
Summary of literatures on ES-oriented service provision.
Work Area | Related work | Key Points |
---|---|---|
Technical indicators | Expenditure [ | [ |
| ||
Cloudlet placement | WLAN [ | [ |
WMAN | [ | |
| ||
Resource scheduling | ES placement | [ |
Cloudlets deployments[ | [ | |
VM migration | [ |
In this section, expenditure is introduced in Section
Different from cloud, the resources of ES are limited. It is critical to minimize the cost of ES and meet the requirements of user tasks. And therefore, the expenditure indictor is highlighted. In [
Load balancing is an important indicator for evaluating the MEC system. Jia et al. [
In this section, we mainly review ES placement issue. In base station based MEC system, the ES are assumed to have been placed and are placed in the same location as the base station. So we mainly study the placement of edge servers (i.e., cloudlets) in the cloudlet based MEC system. We mainly review the case of cloudlet placement in VLAN and WLAN. We review the cloudlet placement in WLAN in Section
In [
Figure
Cloudlet placements in WMAN.
Cloudlets are particularly suited for wireless metropolitan area network (WMAN). And the cloudlet placement problem in WMAN consisting of many WAPs is very promising. There are two classical optimization problems which are closely related to this placement problem, namely, the cache placement [
In this section, we divide the issue of resource scheduling into two subsections.
Resource allocation and computation offloading are usually jointly considered in base station based MEC system. And thus we do not repeat introduce the literatures here. More information can be find in Section
In this section, applications on MEC are reviewed. A summary of literatures on applications of MEC is shown in Table
Summary of literatures on applications of MEC.
item | Related work | Key points |
---|---|---|
Applications | [ | [ |
[ | ||
[ | ||
[ | ||
[ | ||
[ | ||
[ |
MEC not only remarkably reduces the cost of network operation and improving QoS of MUs by pushing computation resources closer to the network edges, but also provides a scalable IoT architecture for time-sensitive applications. In [
In this section, the MEC challenges are introduced. In Section
MEC is a very novel and promising research area. Although we have introduced many studies in this paper, there still exist open research issues. From the MU’s point of view, it is necessary to further design an efficient algorithm for multiple MUs to select a cloudlet that satisfies different service requirements. From the perspective of ES, MUs and cloudlet need to be jointly optimized for cloudlet placement issues. On the one hand, considering the limited resources of cloudlet, on the other hand, there are tremendous amounts of MUs in WMAN, so it is critical to study low-complexity cloudlet placement and scheduling algorithms. In addition, considering the dynamic changes of the MU’s request and the energy consumption of cloudlet, multicloudlet collaboration method and VM migration algorithm need to be further studied. Service recommendations [
The main purpose of [
To the best of our knowledge, many tools like Matlab, JAVA, and Python can be used for simulation for CC. In addition, CloudSim [
With the development of mobile network and 5G, MEC has become a promising field in these years. It not only meets the user’s more business needs, improves the QoS and QoE of MU, but also brings business benefits to service providers. In this paper, we present a comprehensive survey of MEC from the perspective of service adoption and provision. We firstly describe the overview of MEC. After that we review the existing MUs-oriented service adoption of MEC. And then we survey ES-oriented service provision. Moreover, other issues like applications on MEC, open issues are investigated. We highlight that more researches should focus on services of MEC.
The authors declare that they have no conflicts of interest.
This work is supported by the Natural Science Foundation of Fujian Province under Grant no. 2018J05106, National Science Foundation of China under Grant no. 61702277, Quanzhou Science and Technology Project under Grant no. 2015Z115, and the Scientific Research Foundation of Huaqiao University under Grant no. 14BS316. China Scholarship Council (CSC) awarded Kai Peng one year’s research abroad at the University of British Columbia. The authors would like to thank Tao Lin for collecting the material for writing.