Multi-index evaluation learning-based computation offloading optimization for power internet of things

Cloud–edge-device collaborative computation offloading can provide flexible and real-time data processing services for massive resource-constrained devices in power internet of things (PIoT). However, the computation offloading optimization in PIoT still faces several challenges such as high computation offloading delay caused by uncertain information, coupling between task offloading and computation resource allocation, and degraded optimization performance due to the lack of multi-index consideration. To address the above challenges, we formulate a joint optimization problem of task offloading and computation resource allocation to minimize the average computation offloading delay. Specifically, a multi-index evaluation learning-based two-stage computation offloading (MINCO) algorithm is proposed to decouple the joint optimization problem into two-stage subproblems and solve them with evaluation and learning of multiple indexes including data flow characteristic, service priority, empirical average computation offloading delay, and empirical arm selection times. Simulation results show that compared with the baseline 1 and baseline 2 algorithms, MINCO improves the average computation offloading delay by 14.67% and 30.71%. Moreover, MINCO can evaluate different service priorities and data flow characteristics to meet different requirements of computation offloading delay.