Method towards collaborative cloud and edge computing via RBC for joint communication and computation resource allocation

With the extensive adoption of cloud and edge computing in intelligent manufacturing systems driven by the Industrial Internet of Things (IIoT) and Artificial Intelligence, enhancing the efficiency of cloud-edge collaboration under constrained communication and computational resources has emerged as a prominent research focus. We develop the GRALB model, which is based on Role-Based Collaboration (RBC) in cooperative services, to comprehensively manage the offloading strategy of terminal user tasks between edge nodes and the cloud to solve the joint communication and computing resource allocation problem in intelligent manufacturing systems. First, we jointly model the end-to-end latency and energy consumption based on the physical scenario of cloud-edge collaboration. Then, we extend the GRA model based on E-CARGO and propose the GRALB model with load balancing, which formally models the original joint communication and computing resource allocation problem as an equivalent cooperative service model and provides a proof of algorithm convergence. Finally, we design an x-ILP solution to support the verification and integrated application of the proposed model. Simulation results further confirm our theoretical analysis and show that the proposed collaborative cloud and edge computing solution significantly improves the overall system performance.