University of Oulu

Y. Chen, Y. Sun, B. Yang and T. Taleb, "Deep Reinforcement Learning-based Joint Caching and Computing Edge Service Placement for Sensing-Data-Driven IIoT Applications," ICC 2022 - IEEE International Conference on Communications, Seoul, Korea, Republic of, 2022, pp. 4287-4292, doi: 10.1109/ICC45855.2022.9838832

Deep reinforcement learning-based joint caching and computing edge service placement for sensing-data-driven IIoT applications

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Author: Chen, Yan1; Sun, Yanjing1; Yang, Bin2;
Organizations: 1School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
2School of Computer and Information Engineering, Chuzhou University, Chuzhou, China
3Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.3 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2023-05-11


Edge computing (EC) is a promising technology to support a variety of performance-sensitive intelligent applications, especially in the Industrial Internet of Things (IIoT). The sensing-data-driven applications whose task processing requires sensing data from various sensors are typical applications in IIoT systems. The placement of caching and computing edge service functions for such applications is vital to ensure system performance and resource utilization in EC-enabled IIoT systems. Therefore, this paper investigates the joint caching and computing edge service placement (JCCESP) for multiple sensing-data-driven IIoT applications in an EC-enabled IIoT system. The JCCESP problem is formulated as a Markov Decision Process (MDP). Then, a deep reinforcement learning (DRL)-based approach is proposed to address the challenges like limited prior knowledge and the heterogeneity of such IIoT systems. Under such an approach, the policy network of the DRL agent is constructed based on an encoder-decoder model to tackle various applications requiring different numbers of service functions. A REINFORCE-based method is further employed to train the policy network. Simulation results indicate that the performances achieved by our proposed approach can converge after training and are significantly superior to benchmarks.

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Series: IEEE International Conference on Communications
ISSN: 1550-3607
ISSN-E: 1938-1883
ISSN-L: 1550-3607
ISBN: 978-1-5386-8347-7
ISBN Print: 978-1-5386-8348-4
Pages: 4287 - 4292
DOI: 10.1109/icc45855.2022.9838832
Host publication: ICC 2022 - IEEE International Conference on Communications
Conference: IEEE International Conference on Communications
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
113 Computer and information sciences
Funding: This work is supported by the National Natural Science Foundation of China (No. 62071472), the Program for ‘Industrial IoT and Emergency Collaboration’ Innovative Research Team in CUMT (No. 2020ZY002), the Fundamental Research Funds for the Central Universities (No. 2020ZDPY0304), the Chinese Government Scholarship (CSC202006420096), the Academy of Finland Projects: 6Genesis (No. 318927) and IDEA-MILL (No. 335936).
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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