Blockchain-Secured Task Offloading and Resource Allocation for Cloud-Edge-End Cooperative Networks

Enhanced by blockchain and cloud-edge-end cooperation, an edge computing network is capable to provide IoT (Internet of Things) devices higher task processing performance and better security and privacy guarantee. However, the joint resource management for both the task offloading and the blockchain services was less fully studied by existing works. To this end, in this paper, we focus on the task processing delay and energy consumption optimization problem in a multi-device and multi-base-station cloud-edge-end cooperative network. The task offloading, transmit power allocation, transmission rate allocation, and computing resource allocation are jointly optimized to minimize the long-term average total task processing delay of the tasks of all the devices while keeping the stability of the energy consumption of the devices and guaranteeing that the block mining speed matches the task offloading processes. We transform the optimization problem based on the Lyapunov optimization theory, and then design a hybrid deep reinforcement learning (DRL)-based algorithm. We decompose the problem into multiple sub-problems, and then embed multiple fast numerical methods into the twin delayed deep deterministic policy gradient (TD3) architecture as optimization subroutines to improve the learning performance of the DRL model. We also design a distributed deployment scheme for the algorithm and analyze the algorithm complexity. We demonstrate the superior performance of our algorithm in comparison with 5 reference schemes via extensive experiments in 7 scenarios.