Technical Report: Consistency Assurance for Edge-Assisted Multi-Party Computing and Transport-Intensive Applications

In the rapidly evolving digital era, we have witnessed the rise of computation-intensive and transmission-intensive applications like multi-party real-time video communication, remote medical surgeries, and online education. Cloud-edge collaborative resource scheduling has been pivotal in enhancing computational efficiency and reducing end-to-end latency for such applications. However, providing services while ensuring system consistency presents a new challenge in multiparty settings. This study focuses on efficient resource scheduling within cloud-edge collaborative environments, especially addressing system consistency. We propose an innovative real-time consistency assurance model based on latency differentiation aimed at optimizing real-time consistency issues in cloud-edge settings while ensuring data accuracy and timeliness. This problem falls within integer programming, which can transform into a convex optimization issue, efficiently solvable via convex optimization methods. The solution effectively addresses consistency in resource scheduling, significantly improving overall system performance and reliability. Experimental results demonstrate that our method is suitable for computation-intensive and transmission-intensive tasks, substantially enhancing the system's consistency performance and providing robust technical support for the design and optimization of practical cloud-edge collaborative systems.