QoS-AWARE SERVICE MIGRATION METHOD FOR IoV SYSTEM BASED ON CVX DEEP REINFORCEMENT LEARNING

Abstract: a qos-aware service migration method in an iov system based on cvx deep reinforcement learning is provided. firstly, the optimization problem of service migration and resource allocation is decoupled into two sub-problems; then, a new actor-critic-based asynchronous-update deep reinforcement learning method is designed to explore the optimal service migration, where a delayed-update actor makes decisions of service migration and a one-step-update critic evaluates the decisions to guide the direction of the policy update; and on this basis, an optimal resource allocation method for an mec server based on convex optimization is proposed to achieve the optimal resource allocation. according to the qos-aware service migration method, extensive experiments are conducted based on a real-world testbed and a dataset of city vehicle trajectories, thereby verifying the feasibility and effectiveness of semir. compared with benchmark methods, the semir has the best performance of service migration under various scenarios.

Inventor(s): Zheyi CHEN, Sijin HUANG, Jia WEN, Qingnan JIANG

CPC Classification: H04W36/30 (WIRELESS COMMUNICATION NETWORKS (broadcast communication ; communication systems using wireless links for non-selective communication, e.g. wireless extensions ))

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