Two-timescale joint service caching and resource allocation for task offloading with edge–cloud cooperation

Task offloading with edge–cloud cooperation has emerged as a pivotal solution for meeting the intricate array of application coupled with dynamically evolving business demand in 6G business scenarios, such as traffic sensing, environmental monitoring, and video surveillance in smart cities. Nonetheless, effectively leveraging heterogeneous edge–cloud network resources for effective task offloading presents substantial challenges. Additionally, the inherent differences in system decision cycles escalate the complexity of the task offloading problem to a new dimension. In this study, we delve into a two-timescale joint service caching and resource allocation optimization for task offloading within edge–cloud cooperation aiming to maximize long-term network performance while adhering to energy constraints. We propose a novel edge–cloud cooperation task offloading scheme that supports both edge–cloud and edge–edge cooperation to effectively balance the edge–cloud and edge–edge loads, promoting the efficient co-utilization of all edge–cloud system resources. Furthermore, we devise an online two-timescale Lyapunov-based joint optimization framework for service caching, task offloading, and computing resource allocation. Our two-timescale decision-making framework can flexibly accommodate the inherent differences in the sensitive decision optimization periods, thereby mitigating the degradation of task offloading performance caused by frequent service caching updates. Finally, theoretical analysis confirms that our proposed algorithm can converge to an approximate optimal solution in polynomial time, and the superiority of our scheme is validated by extensive simulation experiments.