Joint computation offloading and task caching for multi-user and multi-task MEC systems: reinforcement learning-based algorithms

Computation offloading at mobile edge computing (MEC) servers can mitigate the resource limitation and reduce the communication latency for mobile devices. Thereby, in this study, we proposed an offloading model for a multi-user MEC system with multi-task. In addition, a new caching concept is intro...

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Bibliographic Details
Published in:Wireless networks 2021-04, Vol.27 (3), p.2023-2038
Main Authors: Elgendy, Ibrahim A., Zhang, Wei-Zhe, He, Hui, Gupta, Brij B., Abd El-Latif, Ahmed A.
Format: Article
Language:English
Subjects:
Algorithms
Caching
Communications Engineering
Computation offloading
Computer Communication Networks
Edge computing
Electrical Engineering
Electronic devices
Engineering
IT in Business
Machine learning
Mobile communication systems
Mobile computing
Networks
Wireless networks
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Summary:Computation offloading at mobile edge computing (MEC) servers can mitigate the resource limitation and reduce the communication latency for mobile devices. Thereby, in this study, we proposed an offloading model for a multi-user MEC system with multi-task. In addition, a new caching concept is introduced for the computation tasks, where the application program and related code for the completed tasks are cached at the edge server. Furthermore, an efficient model of task offloading and caching integration is formulated as a nonlinear problem whose goal is to reduce the total overhead of time and energy. However, solving these types of problems is computationally prohibitive, especially for large-scale of mobile users. Thus, an equivalent form of reinforcement learning is created where the state spaces are defined based on all possible solutions and the actions are defined on the basis of movement between the different states. Afterwards, two effective Q-learning and Deep-Q-Network-based algorithms are proposed to derive the near-optimal solution for this problem. Finally, experimental evaluations verify that our proposed model can substantially minimize the mobile devices’ overhead by deploying computation offloading and task caching strategy reasonably.
ISSN:1022-0038
1572-8196
DOI:10.1007/s11276-021-02554-w