DRL-Based Computing Resource Matching for Task Inference and Block Mining in Blockchain-Assisted Edge Intelligence

Abstract

In a blockchain-assisted edge intelligence system, blockchain can effectively enhance the reliability and trustworthiness of artificial intelligence (AI) task inference. When delegating an AI task from a user device to an edge server, blockchain will broadcast it to the entire network by recording it as a transaction and then conduct the verification process. Computing resource matching is a core issue of blockchain-assisted edge intelligence system, because block mining and task inference will face sharing of computing resources. The prerequisite for achieving optimal system performance is to match the block generation speed in block mining with the number of AI tasks per unit time in the edge intelligence system by computing resource allocation. However, prior studies have neglected the matching issue mentioned above and have not incorporated discussions on the properties of AI tasks, i.e., task inference accuracy. We propose a strategy for task offloading and resource allocation in the blockchain-assisted edge intelligence system. To minimize both the overall delay of task processing and the energy consumption of the user devices, we propose a combined optimization problem that includes task offloading decision, transmission power control, task data size adjustment, and allocation of computing resource on the premise of satisfying the task inference accuracy constraint. To this end, we propose a deep reinforcement learning (DRL) algorithm that incorporates the twin delayed deep deterministic policy gradient (TD3) with a swift numerical approach, significantly lessening the time complexity of the algorithm. To demonstrate the superiority of our scheme, we analyze the complexity and convergence of our algorithm, and then undertake numerous simulation experiments and compare the performance with the other 3 reference schemes in 6 scenarios.