Tenant-Oriented Resource Optimization for Cloud Network Slicing with Performance Guarantees

Cloud Network Slicing (CNS), emerging alongside the 5G mobile network, comprises a paradigm shift in the way networks are provisioned, managed, and operated. Fundamentally, CNS fosters the deployment of a multitude of modern applications, e.g., virtual and augmented reality, 4K video streaming, and autonomous vehicles, which require ultra-low latency, high bandwidth consumption, or both. Slicing promotes the realization of such services through the allocation of computing and network resource bundles, which, as CNS mandates, are isolated from the rest of the network. Typically, such resources are arranged into wide geographical areas (e.g., into multiple countries or even continents), which implies that it is possible to allocate from multiple infrastructure providers. This exacerbates the already challenging problem of maximizing resource allocation efficiency, a feature commonly addressed by CNS architectures. In this respect, we study the optimized embedding of slices across multiple domains. Therefore, we account for slices as a collection of computing and network parts. Given specific resource requirements from slice tenants and potentially multiple offers per slice part, we model the problem as a Mixed Integer Linear Program (MILP). We further design two heuristic algorithms in order to mitigate the complex intricacies that would be perceptible in large problem instances. Our evaluation results, based on a simulation environment aligned with the NECOS slicing architecture, indicate that the MILP approach yields better efficiency c ompared t o b oth h euristics, w ith r espect to client expenditure with a fair amount of performance parameters in an adequate execution time. Our main contribution lies in the optimization methods based on the split and combine approach, integrated into the NECOS CNS architecture.