GBRM: a graph embedding and blockchain-based resource management framework for 5G MEC

In the 5G scenario of the convergence of information technology (IT) and communication technology (CT), multi-operators collaborate to form edge computing, which makes the problem of resource optimization more complicated than ever. Users may access resources deployed by various MEC's operators to achieve ultra-low latency. However, traditional resource management methods consider only a single operator failure to handle profit allocation and privacy security issues among different operators. To address this problem, we proposed a resource management framework named GBRM based on graph embedding and blockchain. Specifically, we use the Stackelberg game model to solve MEC servers' cache-offloading problem; non-indexed content sharing by Deepwalk graph embedding between MECs ensures the privacy of different operators' content. Consortium blockchain assists in the trusted profit allocation of services across various operators. Experiments show in the virtual network scenario that our work performance is significantly better than the RandomSelect and the LocalIndex method in global latency and close to the global index's ideal situation. Multi-operators collaborate to form edge computing, which makes the problem of resource optimization more complicated than ever.