SMART: Cost-Aware Service Migration Path Selection Based on Deep Reinforcement Learning


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With the large-scale commercial use of 5G technology, the era of Mobile Edge Computing with the Internet of Everything as the core is opening. Various computing resources are deployed to the edge of the network near the mobile smart terminal, forming a mobile edge environment for numerous application scenarios. Under this environment, the mobile edge network needs to use the path selection method to obtain one or more service data transmission paths and seamlessly migrates the service data to the most appropriate edge server, to ensure the continuity of edge services and reduce the resource occupation of the mobile edge network. Therefore, this paper proposes a method of Cost-awareServiceMigration Path Selection based on DeepReinforcement Learning (SMART), aiming to jointly optimize communication costs and communication delays under the premise of meeting service requirements. This method transforms the service migration path selection problem in the mobile edge environment into a bi-objective optimization problem under dual constraints, i.e., to find low-latency, low-cost and high-quality service migration paths while satisfying the constraints of computing power resources and transmission time of mobile smart terminals. Then, a DQN is used to construct the corresponding Markov chain decision model according to the problem scenario to find the optimal path for edge service migration. The proposed method learns to select the optimal edge service migration path through the interaction with the environment without obtaining a large amount of historical edge service migration path information in advance. The experimental results onShanghai (Beijing) Telecom mobile communication base station dataset and Shanghai (Beijing) taxi trajectory dataset show that the proposed method can efficiently select low-latency, low-cost, high-quality edge service migration paths in mobile edge environment when vehicles move continuously.It outperforms six typical edge service migration path selection methods, i.e.,Q-learning, A-Star, PLP, PLP/F, PLP/P, and Dijkstra, by at least 15% in all evaluation metrics except computational time.
Edge computing,mobile edge environments,service migration,path selection,deep Q-learning networks
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