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Deep Meta Q-Learning Based Multi-Task Offloading in Edge-Cloud Systems
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AI
DOI：10.1109/TMC.2023.3264901.png)
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Abstract
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Resource-constrained edge devices can not efficiently handle the explosive growth of mobile data and the increasing computational demand of modern-day user applications. Task offloading allows the migration of complex tasks from user devices to the remote edge-cloud servers thereby reducing their computational burden and energy consumption while also improving the efficiency of task processing. However, obtaining the optimal offloading strategy in a multi-task offloading decision-making process is an NP-hard problem. Existing Deep learning techniques with slow learning rates and weak adaptability are not suitable for dynamic multi-user scenarios. In this article, we propose a novel deep meta-reinforcement learning-based approach to the multi-task offloading problem using a combination of first-order meta-learning and deep Q-learning methods. We establish the meta-generalization bounds for the proposed algorithm and demonstrate that it can reduce the time and energy consumption of IoT applications by up to 15%. Through rigorous simulations, we show that our method achieves near-optimal offloading solutions while also being able to adapt to dynamic edge-cloud environments.
Keywords:
Task analysis
Heuristic algorithms
Computational modeling
Servers
Internet of Things
Multitasking
Energy consumption
Deep q-learning
directed acyclic graph
edge-cloud computing
meta-learning
multi-task offloading
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