Network Slicing for Edge-Cloud Orchestrated Networks via Online Convex Optimization

In this paper, we study a network slicing framework for edge-cloud orchestrated networks. The framework integrates multi-access edge computing (MEC), allowing devices to offload computation-intensive tasks to Radio Access Networks (RAN) at the edge, with the flexibility to process tasks in the cloud based on resource availability and cost. In order to make workload distribution and resource allocation decisions for the edge and cloud resources, an Online Convex Optimization (OCO) approach is tailored. This approach leverages predictions to make the resource allocation and workload distribution decisions. The algorithm aims to optimize the long-term system cost while satisfying the Quality of Service (QoS) constraints, particularly in terms of minimizing delays. The proposed model encompasses various costs, including the costs for edge and cloud computing resources, communication resources, delay violations, and slice reconfiguration. Through simulations, we demonstrate the efficacy of the algorithm in reducing the costs associated with network slicing.