Y. Chen, Y. Sun, C. Wang and T. Taleb, "Dynamic Task Allocation and Service Migration in Edge-Cloud IoT System Based on Deep Reinforcement Learning," in IEEE Internet of Things Journal, vol. 9, no. 18, pp. 16742-16757, 15 Sept.15, 2022, doi: 10.1109/JIOT.2022.3164441
Dynamic task allocation and service migration in edge-cloud IoT system based on deep reinforcement learning
|Author:||Chen, Yan1; Sun, Yanjing1; Wang, Chenyang2;|
1School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, 221116 China
2College of Intelligence and Computing, Tianjin University, Tianjin, 300072 China
3Center of Wireless Communications, University of Oulu, Oulu, 90570 Finland
4Department of Computer and In- formation Security, Sejong University, Seoul, 05006 South Korea
|Online Access:||PDF Full Text (PDF, 4.2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022083056749
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2022-08-30
Edge computing extends the ability of cloud computing to the network edge to support diverse resource-sensitive and performance-sensitive IoT applications. However, due to the limited capacity of edge servers (ESs) and the dynamic computing requirements, the system needs to dynamically update the task allocation policy according to real-time system states. Service migration is essential to ensure service continuity when implementing dynamic task allocation. Therefore, this paper investigates the long-term dynamic task allocation and service migration (DTASM) problem in edge-cloud IoT systems where users’ computing requirements and mobility change over time. The DTASM problem is formulated to achieve the long-term performance of minimizing the load forwarded to the cloud while fulfilling the seamless migration constraint and the latency constraint at each time of implementing the DTASM decision. First, the DTASM problem is divided into two sub-problems: the user selection problem on each ES and the system task allocation problem. Then, the DTASM problem is formulated as a Markov Decision Process (MDP) and an approach based on deep reinforcement learning (DRL) is proposed. To tackle the challenge of vast discrete action spaces for DTASM task allocation in the system with a mass of IoT users, a training architecture based on the twin-delayed deep deterministic policy gradient (DDPG) is employed. Meanwhile, each action is divided into a differentiable action for policy training and one mapped action for implementation in the IoT system. Simulation results demonstrate that the proposed DRL-based approach obtains the long-term optimal system performance compared to other benchmarks while satisfying seamless service migration.
IEEE internet of things journal
|Pages:||16742 - 16757|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work is partially supported by the Fundamental Research Funds for the Central Universities (No. 2020ZDPY0304), the Chinese Government Scholarship (NO. 202006420096) awarded by China Scholarship Council, the European Union’s Horizon 2020 Research and Innovation Program under the MonB5G Project under Grant No. 871780, the Academy of Finland 6Genesis project under Grant No. 318927 and IDEA-MILL with grant number 33593.
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.