Computing Offloading and Resource Optimization of Adaptive Data Block Size in Edge Environments

With the rapid growth of smart mobile devices, the limited resources and computing performance of mobile devices might increase the delay of processing tasks, resulting in greater energy and cost consumption by terminal devices. This study aimed to focus on the balance of delay and energy consumption in the mobile edge environment, considering the impact of computationally intensive network interference on system performance under blockchain and proposed a joint optimization algorithm for computing offloading and resource allocation of adaptive data block size in the edge environment. First, a computing offloading model and a communication model were established, and the problem model was optimized on that basis. Second, the incentive mechanism of a virtual currency based on blockchain was introduced, and a coordinate descent method was used to formulate the offloading decision of adaptive data block size. Then, a subchannel allocation was based on the improved Hungarian algorithm and the greedy algorithm while satisfying the user delay constraint. Finally, energy minimization problems were converted into power minimization problems, then into a convex optimization problem, and finally into the user’s optimal transmit power. Experimental results showed that compared with similar computing offloading algorithms, the proposed offloading decision could reduce the energy consumption of the system while satisfying different time delay requirements of the users and improving the system performance.