A survey on the computation offloading approaches in mobile edge computing: A machine learning-based perspective

Shakarami, Ali; Ghobaei‐Arani, Mostafa; Shahidinejad, Ali

doi:10.1016/j.comnet.2020.107496

Cited by 241 publications

(137 citation statements)

References 59 publications

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“…en, the DAG is depth-firstly traversed with starting from the exit task, and all tasks' priorities can be obtained by (11) and (12)fd12. ese tasks are arranged according to the corresponding priorities in the descending order, and the scheduling sequence of tasks can be defined as follows:…”

Section: Priority Computationmentioning

confidence: 99%

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DRL-Based Edge Computing Model to Offload the FIFA World Cup Traffic

Che

2020

Mobile Information Systems

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In recent years, the volume of global video traffic has been increasing rapidly and it is considerably significant to offload the traffic during the process of video transmission and improve the experience of users. In this paper, we propose a novel traffic offloading strategy to provide a feasible and efficient reference for the following 2022 FIFA World Cup held in Qatar. At first, we present the system framework based on the Mobile Edge Computing (MEC) paradigm, which supports transferring the FIFA World Cup traffic to the mobile edge servers. Then, the Deep Reinforcement Learning (DRL) is used to provide the traffic scheduling method and minimize the scheduling time of application programs. Meanwhile, the task scheduling operation is regarded as the process of Markov decision, and the proximal policy optimization method is used to train the Deep Neural Network in the DRL. For the proposed traffic offloading strategy, we do the simulation based on two real datasets, and the experimental results show that it has smaller scheduling time, higher bandwidth utilization, and better experience of user than two baselines.

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Section: Priority Computationmentioning

confidence: 99%

“…Nevertheless, at the era of mobile Internet, the ability of edge computing cannot be handled with the billions of mobile devices. At the right time, the Mobile Edge Computing (MEC) [11][12][13] has been regarded as the relatively appropriate alternative solution.…”

Section: Introductionmentioning

confidence: 99%

DRL-Based Edge Computing Model to Offload the FIFA World Cup Traffic

Che

2020

Mobile Information Systems

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“…Optimizing computation offloading is typically formulated as a Mixed Integer Non-Linear Programming (MINLP) problem, which is challenging to solve. The optimizing approaches of computation offloading in mobile edge computing are separated into several perspectives: game-theoretic methodology [ 9 ], machine learning methodology [ 10 ], queuing theory, and linear programming concerning other algorithms. Researchers are trying to solve computation offloading problems using various methods.…”

Section: Introductionmentioning

confidence: 99%

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

Huang

Zhai

Chen

et al. 2021

Sensors

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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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“…Instead, it needs some new techniques or/and networking paradigms as the auxiliary tools to help complete the effective evaluation and analysis of physical training. As everyone knows, Mobile Edge Computing (MEC) [4,5] is a new technique, and its purpose is to effectively solve problems such as latency and network load. In addition, MEC can put some complex tasks at the edge computing servers for computing, in order to save response time and thus guarantee the real-time evaluation and analysis.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Analysis of Traditional Physical Training by Using Mobile Edge Computing and Software-Defined Networking

Pan

Wang

Jing-sheng

2021

Mobile Information Systems

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The body health plays an important metric in people’s everyday life, and it directly determines whether people have the ability to preferably contribute to the society. In fact, the physical training is a universal sport to enhance the body health. Therefore, the evaluation and analysis of physical training become particularly significant. With the rapid development and emerging of new techniques and networking paradigms, the traditional offline physical training evaluation and analysis cannot be performed well. Instead, this paper uses Mobile Edge Computing (MEC) and Software-Defined Networking (SDN) to implement the evaluation and analysis of physical training, shortened for MSPT, where MEC is the new computing technique and SDN is the new networking paradigm. The proposed MSPT includes two parts. At first, the physical training data from different mobile devices are migrated into the edge server for computing according to the current condition, in which the game theory is used to complete the task scheduling. Then, SDN is responsible for the global scheduling in the centralized control manner, in which the multigranularity scheduling strategy is used to handle the traffic between the SDN controller and edge computing server. The experiments are driven by OMNet, including three aspects of evaluation, i.e., task offloading of MEC, traffic scheduling of SDN, and performance analysis of physical training, and the results show that the proposed MSPT has better performance than the corresponding baselines.

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A survey on the computation offloading approaches in mobile edge computing: A machine learning-based perspective

Cited by 241 publications

References 59 publications

DRL-Based Edge Computing Model to Offload the FIFA World Cup Traffic

DRL-Based Edge Computing Model to Offload the FIFA World Cup Traffic

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

Evaluation and Analysis of Traditional Physical Training by Using Mobile Edge Computing and Software-Defined Networking

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