Task Offloading Scheduling in Mobile Edge Computing Networks

Wang, Zhonglun; Li, Peifeng; Shen, Shuai; Yang, Kun

doi:10.1016/j.procs.2021.03.041

Cited by 15 publications

(10 citation statements)

References 14 publications

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“…is is a distinctive feature of the incomplete observation system and is a key point for the offloading model to meet the conditions for asynchronous decision-making [25,26].…”

Section: Systematic Action Transitions and Delayed Rewardsmentioning

confidence: 99%

Multiuser Computing Offload Algorithm Based on Mobile Edge Computing in the Internet of Things Environment

Chu

Leng

2022

Wireless Communications and Mobile Computing

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As traditional cloud computing is not efficient enough to support large-scale computational task execution in IoT environments, a task offloading and resource allocation algorithm for mobile edge computing (MEC) is proposed in this paper. First, a multiuser computation offloading model is constructed, including a communication model and computation offloading model, which is transformed into the minimization of users’ time delay and energy consumption (i.e., total system overhead) in the MEC system. Then, the task offloading model is formulated into a Markov decision process, and an offloading strategy based on a deep Q network (DQN) is designed to dynamically make fine tunings on the offloading proportion of each user so as to realize a low-cost MEC system. The proposed algorithm is analyzed based on the constructed simulation platform. The simulation results show that when the number of user terminals is 40, the average delay of the proposed algorithm does not exceed 0.9 s, and the average energy consumption tends to 65 J, which is better than the comparison method. Therefore, the proposed algorithm has certain application prospects.

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“…is is a distinctive feature of the incomplete observation system and is a key point for the offloading model to meet the conditions for asynchronous decision-making [25,26].…”

Section: Systematic Action Transitions and Delayed Rewardsmentioning

confidence: 99%

Multiuser Computing Offload Algorithm Based on Mobile Edge Computing in the Internet of Things Environment

Chu

Leng

2022

Wireless Communications and Mobile Computing

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“…In addition, the author also concludes that there is a one-to-one mapping relationship between the transmission energy allocated to each channel and the number of CPU cycles allocated to relevant users and proposes a license control strategy. Its core idea is to allow the tasks to be transmitted to the edge processing and the disallowed tasks to be processed locally, which can not only ensure that the edge server is not overloaded but also ensure the stability of the task queue of the edge device [ 11 , 12 ]. The definition of an educational resource allocation problem is that the edge equipment performs the educational resource allocation process, while the definition of a centralized educational resource allocation problem is that the server performs the process.…”

Section: Literature Reviewmentioning

confidence: 99%

Resource Allocation Strategy of the Educational Resource Base for MEC Multiserver Heuristic Joint Task

Luo

2022

Computational Intelligence and Neuroscience

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This paper analyzes the application of MEC multiserver heuristic joint task in resource allocation of the educational resource database. After constructing the scenario of educational resource database, a mathematical model is constructed from the dimensions of local execution strategy, unloading execution, and given educational resource allocation, in order to optimize the optimal allocation of educational resources through MEC. The results show that the DOOA scheme has good performance in terms of calculation cost and timeout rate. Compared with other benchmark schemes, the DQN-based unloading scheme has better performance, can effectively balance the load, and is better than the random unloading scheme and the SNR-based unloading scheme in terms of delay and calculation cost. The results show that the total hits of all category 1 users' content requests account for the proportion of the total content requests. The images have a small downward trend at the 15000 and 30000 time slots and then continue to rise. This shows that the proposed scheme can automatically adjust the caching strategy to adapt to the changes of content popularity, which proves that the agent can correctly perceive the changing trend of content popularity when the popularity of network content is unknown and improve the caching strategy accordingly to improve the cache hit rate. Therefore, the allocation of educational resources based on the MEC multiserver heuristic joint task is more reasonable and can achieve the optimal solution.

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“…It can be easily implemented in practice. Zakaryia studied a task scheduling method using mobile edge computing (MEC) with multiple base stations (BS), each consisting of a MEC server, that guides multiple latencysensitive user equipment (UE) in computing [9,10]. Sheng et al [11] also have worked on computing offloading techniques in mobile edge computing.…”

Section: Literature Reviewmentioning

confidence: 99%

Intrinsic Profit Maximization of the Offloading Tasks for Mobile Edge Computing with Fixed Memory Capacities and Low Latency Constraints Using Ant Colony Optimization

Tunga¹,

Kar²,

Giri³

2022

MMEP

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Artificial intelligence and the Internet of Things (IoT) have resulted in more computationally demanding and time-sensitive applications. Given the limited processing power of current mobile computers, there is a need for on-demand computing resources with minimal latency. Edge computing has already made a significant contribution to mobile networks, enabling the distribution, scaling, and faster access of computational resources at network margins closer to users, especially in power-constrained mobile devices. Offloading tasks efficiently on the Mobile Edge Computing Server (MECS) is an important part of our proposed method. We propose a method of offloading multiple tasks for Mobile Edge Computing servers that require fixed memory capacities and low latency. We calculate the optimum cumulative intrinsic profit of the number of offloaded tasks efficiently using the Ant Colony Optimization (ACO) model, which is flexible and versatile in the context of real-time applications.

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Task Offloading Scheduling in Mobile Edge Computing Networks

Cited by 15 publications

References 14 publications

Multiuser Computing Offload Algorithm Based on Mobile Edge Computing in the Internet of Things Environment

Multiuser Computing Offload Algorithm Based on Mobile Edge Computing in the Internet of Things Environment

Resource Allocation Strategy of the Educational Resource Base for MEC Multiserver Heuristic Joint Task

Intrinsic Profit Maximization of the Offloading Tasks for Mobile Edge Computing with Fixed Memory Capacities and Low Latency Constraints Using Ant Colony Optimization

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