Abstract
The rapid development of 5th generation mobile networks (5G) and Internet of Things (IoT) technologies will generate a large amount of data, the processing and analysis requirements of big data will challenge existing networks and processing platforms. As the most promising technology in 5G networks, edge computing will greatly ease the pressure on network and data processing analysis on the edge. In this paper, we consider the coordination between compute and cache resources between multi-level edge computing nodes (ENs), users under this system can offload computing tasks to ENs to improve quality of service (QoS). We aim to maximize the long-term profit on the edge, while satisfying the low-latency computing of the users, and jointly optimize the edge-side node offloading strategy and resource allocation. However, it is challenging to obtain an optimal strategy in such a dynamic and complex system. Therefore, we use double deep Q-learning (DDQN) to make decisions to solve the complex resource allocation problem on the edge and make edge have certain adaptation and cooperation. Ability to maximize long-term gains while making quick decisions. The simulation results prove the effectiveness of DDQN in maximizing revenue when allocation resources on the edge.
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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Ren, J., Wang, H., Zhang, X. (2020). Adaptive Collaborative Computing in Edge Computing Environment. In: Wang, X., Leung, V.C.M., Li, K., Zhang, H., Hu, X., Liu, Q. (eds) 6GN for Future Wireless Networks. 6GN 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 337. Springer, Cham. https://doi.org/10.1007/978-3-030-63941-9_12
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DOI: https://doi.org/10.1007/978-3-030-63941-9_12
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