A Survey on Mobile Edge Computing: Focusing on Service Adoption and Provision

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.

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Joint Computation Offloading and Data Caching Based on Cooperation of Mobile-Edge-Computing-Enabled Base Stations

Applied Sciences ◽

10.3390/app11135802 ◽

2021 ◽

Vol 11 (13) ◽

pp. 5802

Author(s):

Tian Liu ◽

Wenhao Fan ◽

Fan Wu ◽

Wei Xie ◽

Wen Yuan

Keyword(s):

Cloud Computing ◽

Mobile Cloud Computing ◽

Edge Computing ◽

Base Stations ◽

Computation Offloading ◽

Mobile Cloud ◽

Mobile Edge Computing ◽

Data Caching ◽

Time Costs ◽

And Storage

Mobile terminal applications with high computing complexity and high time delay sensitivity are developing quite fast today, which aggravates the load of mobile cloud computing and storage and further leads to network congestion and service quality decline. Mobile edge computing (MEC) is a way of breaking through the limits of computing and storage resources of mobile cloud and alleviating the load of mobile cloud. Computing time costs and transmission time costs are considered to be the main issues for the mobile cloud when carrying out computing offloading and data caching. Therefore, an efficient resource management strategy, which could minimize the system delay, is proposed in this paper. The new scheme offloads reasonably computing tasks and caches the tasks’ data from the mobile cloud to mobile edge computing-enabled base stations. An intelligence algorithm, genetic algorithm, is being used to solve the global optimization problem which would cause transmission delay and computing resources occupation, and to determine the computing offloading and data caching probability. The simulation of the system using MATLAB is conducted in 8 different scenarios with different parameters. The results show that our new scheme improves the system computing speed and optimizes the user experience in all scenarios, compared with the scheme without data caching and the scheme without computing offloading and data caching.

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Modified reinforcement learning based- caching system for mobile edge computing

Intelligent Decision Technologies ◽

10.3233/idt-190152 ◽

2020 ◽

pp. 1-16

Author(s):

Sarra Mehamel ◽

Samia Bouzefrane ◽

Soumya Banarjee ◽

Mehammed Daoui ◽

Valentina E. Balas

Keyword(s):

Reinforcement Learning ◽

Mobile Networks ◽

Mobile Network ◽

Edge Computing ◽

Mobile Edge Computing ◽

Network Nodes ◽

Caching Strategy ◽

Edge Network ◽

The Right ◽

And Storage

Caching contents at the edge of mobile networks is an efficient mechanism that can alleviate the backhaul links load and reduce the transmission delay. For this purpose, choosing an adequate caching strategy becomes an important issue. Recently, the tremendous growth of Mobile Edge Computing (MEC) empowers the edge network nodes with more computation capabilities and storage capabilities, allowing the execution of resource-intensive tasks within the mobile network edges such as running artificial intelligence (AI) algorithms. Exploiting users context information intelligently makes it possible to design an intelligent context-aware mobile edge caching. To maximize the caching performance, the suitable methodology is to consider both context awareness and intelligence so that the caching strategy is aware of the environment while caching the appropriate content by making the right decision. Inspired by the success of reinforcement learning (RL) that uses agents to deal with decision making problems, we present a modified reinforcement learning (mRL) to cache contents in the network edges. Our proposed solution aims to maximize the cache hit rate and requires a multi awareness of the influencing factors on cache performance. The modified RL differs from other RL algorithms in the learning rate that uses the method of stochastic gradient decent (SGD) beside taking advantage of learning using the optimal caching decision obtained from fuzzy rules.

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Delay-Tolerant Sequential Decision Making for Task Offloading in Mobile Edge Computing Environments

Information ◽

10.3390/info10100312 ◽

2019 ◽

Vol 10 (10) ◽

pp. 312 ◽

Cited By ~ 1

Author(s):

Ibrahim Alghamdi ◽

Christos Anagnostopoulos ◽

Dimitrios P. Pezaros

Keyword(s):

Decision Making ◽

Cloud Computing ◽

Edge Computing ◽

Cloud Services ◽

Mobile Edge Computing ◽

Sequential Decision ◽

Mobile Nodes ◽

Real World Data ◽

Optimal Stopping Theory ◽

Execution Delay

In recent years, there has been a significant increase in the use of mobile devices and their applications. Meanwhile, cloud computing has been considered as the latest generation of computing infrastructure. There has also been a transformation in cloud computing ideas and their implementation so as to meet the demand for the latest applications. mobile edge computing (MEC) is a computing paradigm that provides cloud services near to the users at the edge of the network. Given the movement of mobile nodes between different MEC servers, the main aim would be the connection to the best server and at the right time in terms of the load of the server in order to optimize the quality of service (QoS) of the mobile nodes. We tackle the offloading decision making problem by adopting the principles of optimal stopping theory (OST) to minimize the execution delay in a sequential decision manner. A performance evaluation is provided using real world data sets with baseline deterministic and stochastic offloading models. The results show that our approach significantly minimizes the execution delay for task execution and the results are closer to the optimal solution than other offloading methods.

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Cross-Server Computation Offloading for Multi-Task Mobile Edge Computing

Information ◽

10.3390/info11020096 ◽

2020 ◽

Vol 11 (2) ◽

pp. 96 ◽

Cited By ~ 2

Author(s):

Yongpeng Shi ◽

Yujie Xia ◽

Ya Gao

Keyword(s):

Approximation Algorithm ◽

Mobile Devices ◽

Computational Efficiency ◽

Network Architecture ◽

Edge Computing ◽

Computation Offloading ◽

Mobile Edge Computing ◽

Optimal Solutions ◽

Resource Constrained ◽

And Storage

As an emerging network architecture and technology, mobile edge computing (MEC) can alleviate the tension between the computation-intensive applications and the resource-constrained mobile devices. However, most available studies on computation offloading in MEC assume that the edge severs host various applications and can cope with all kinds of computation tasks, ignoring limited computing resources and storage capacities of the MEC architecture. To make full use of the available resources deployed on the edge servers, in this paper, we study the cross-server computation offloading problem to realize the collaboration among multiple edge servers for multi-task mobile edge computing, and propose a greedy approximation algorithm as our solution to minimize the overall consumed energy. Numerical results validate that our proposed method can not only give near-optimal solutions with much higher computational efficiency, but also scale well with the growing number of mobile devices and tasks.

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An Efficient Computation Offloading Strategy with Mobile Edge Computing for IoT

Micromachines ◽

10.3390/mi12020204 ◽

2021 ◽

Vol 12 (2) ◽

pp. 204

Author(s):

Juan Fang ◽

Jiamei Shi ◽

Shuaibing Lu ◽

Mengyuan Zhang ◽

Zhiyuan Ye

Keyword(s):

Edge Computing ◽

Computation Offloading ◽

Mobile Edge Computing ◽

Efficient Computation ◽

Fine Grained ◽

Delay Sensitive ◽

Starting Point ◽

Computationally Intensive ◽

Execution Delay ◽

The Internet Of Things

With the rapidly development of mobile cloud computing (MCC), the Internet of Things (IoT), and artificial intelligence (AI), user equipment (UEs) are facing explosive growth. In order to effectively solve the problem that UEs may face with insufficient capacity when dealing with computationally intensive and delay sensitive applications, we take Mobile Edge Computing (MEC) of the IoT as the starting point and study the computation offloading strategy of UEs. First, we model the application generated by UEs as a directed acyclic graph (DAG) to achieve fine-grained task offloading scheduling, which makes the parallel processing of tasks possible and speeds up the execution efficiency. Then, we propose a multi-population cooperative elite algorithm (MCE-GA) based on the standard genetic algorithm, which can solve the offloading problem for tasks with dependency in MEC to minimize the execution delay and energy consumption of applications. Experimental results show that MCE-GA has better performance compared to the baseline algorithms. To be specific, the overhead reduction by MCE-GA can be up to 72.4%, 38.6%, and 19.3%, respectively, which proves the effectiveness and reliability of MCE-GA.

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Computation Offloading Game in Multiple UAV-Enabled Mobile Edge Computing Networks

IET Communications ◽

10.1049/iet-com.2020.0131 ◽

2020 ◽

Author(s):

Yanling Ren ◽

Zhibin Xie ◽

Zhenfeng Ding ◽

xiyuan sun ◽

Jie Xia ◽

...

Keyword(s):

Edge Computing ◽

Computation Offloading ◽

Mobile Edge Computing

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Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing

Sensors ◽

10.3390/s21082628 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2628

Author(s):

Mengxing Huang ◽

Qianhao Zhai ◽

Yinjie Chen ◽

Siling Feng ◽

Feng Shu

Keyword(s):

Optimization Algorithm ◽

Edge Computing ◽

Solution Set ◽

Whale Optimization Algorithm ◽

Computation Offloading ◽

Mobile Edge Computing ◽

Multi Objective ◽

Whale Optimization ◽

Artificial Immune System Algorithm

Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective Whale Optimization Algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary Algorithm (GrEA), Cluster-Gradient-based Artificial Immune System Algorithm (CGbAIS), Non-dominated Sorting Genetic Algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions.

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