Efficient Seamless Task Offloading based on Edge-Terminal Collaborative for AIoT Elastic Computing Services
Wang, Jing Peng, Yuhuai Zhang, Xinyu Liu, Lei Mumtaz, Shahid Guizani, Mohsen Dustdar, Schahram
Wang, Jing
Peng, Yuhuai
Zhang, Xinyu
Liu, Lei
Mumtaz, Shahid
Guizani, Mohsen
Dustdar, Schahram
Supervisor
Department
Machine Learning
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
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Research Projects
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Journal Issue
Abstract
Artificial Intelligence of Things (AIoT) utilizes a combination of computing, storage, and networking resources to provide highly reliable and low-latency information services to the industrial production processes. However, with the increasing integration of numerous smart terminals into real-time sensing, autonomous decision-making, and precision manufacturing execution systems, the current task scheduling pattern appears to be insufficient to meet the latency requirements of computationally intensive tasks. To address the above challenge, this paper presents a collaborative edge-terminal task offloading scheme. First, the Task Backlog and Multi-slot Scheduling (TBMS) problem is converted from a long-term offloading problem to a single timeslot scheduling problem by Lyapunov optimization. Then, to simplify the problem, the single timeslot problem is decomposed into three subproblems: the local resource allocation problem, the server resource allocation problem, and the indicator weight selection problem. The two resource allocation problems are proved to be convex, which have been solved by using the Bisection method and the Karush-Kuhn-Tucker (KKT) method, respectively. For the indicator weight selection problem, we proposed the enhanced jumping spider optimization algorithm that integrates the elite opposition-based learning strategy. Extensive experiments show that the proposed algorithm can alleviate the computing pressure of the terminal device. Compared with the traditional methods, the offload system cost is effectively reduced by at least 58.8% and the average execution success rate is increased by at least 6%.
Citation
J. Wang et al., "Efficient Seamless Task Offloading based on Edge-Terminal Collaborative for AIoT Elastic Computing Services," in IEEE Transactions on Services Computing, doi: 10.1109/TSC.2025.3592386
Source
IEEE Transactions on Services Computing
Conference
Keywords
Artificial intelligence of things, Edge-terminal collaboration, Long-term task offloading, Lyapunov optimization, Resource allocation
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Source
Publisher
IEEE