Recent advances of computing, caching, and communication (3C) can have significant impacts on mobile social networks (MSNs). MSNs can leverage these new paradigms to provide a new mechanism for users to share resources (e.g., information, computation-based services). In this paper, we exploit the intrinsic nature of social networks, i.e., the trust formed through social relationships among users, to enable users to share resources under the framework of 3C. Specifically, we consider the mobile edge computing (MEC), in-network caching and device-to-device (D2D) communications. When considering the trust-based MSNs with MEC, caching and D2D, we apply a novel big data deep reinforcement learning (DRL) approach to automatically make a decision for optimally allocating the network resources. The decision is made purely through observing the network's states, rather than any handcrafted or explicit control rules, which makes it adaptive to variable network conditions. Google TensorFlow is used to implement the proposed deep Q-learning approach. Simulation results with different network parameters are presented to show the effectiveness of the proposed scheme.

Additional Metadata
Keywords caching, deep reinforcement learning, mobile edge computing, Mobile social networks
Persistent URL dx.doi.org/10.1109/GLOCOM.2018.8647548
Conference 2018 IEEE Global Communications Conference, GLOBECOM 2018
Citation
He, Y. (Ying), Liang, C. (Chengchao), Yu, F.R, & Leung, V.C.M. (Victor C.M.). (2019). Integrated Computing, Caching, and Communication for Trust-Based Social Networks: A Big Data DRL Approach. In 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings. doi:10.1109/GLOCOM.2018.8647548