5G will serve heterogeneous demands in terms of data-rate, reliability, latency, and efficiency. Mobile operators shall be able to serve all of these requirements using shared network infrastructure's resources. To this end, we propose in this paper a framework for resource orchestration for 5G network slices implementing four Quality of Service pillars. Starting from traffic classification, demands are marked so that they are best served by dedicated logical virtual networks called Network Slices (NSs). To optimally serve multiple NSs over the same physical network, we then implement a new dynamic slicing approach of network resources exploiting Machine Learning (ML). Indeed, as demands change dynamically, a mere recursive optimization leading to progressive convergence towards an optimum slice is not sufficient. Consequently, we need an initial well-informed slicing decision of physical resources from a total available resource pool. Moreover, we formalize both admission control and slice scheduler modules as Knapsack problems. Using our 5G experimental prototype based on OpenAirInterface (OAI), we generate a realistic dataset for evaluating ML based approaches as well as two baselines solutions (i.e. static slicing and uninformed random slicing-decisions). Simulation results show that using regression trees as an ML based approach for both classification and prediction, outperform other alternative solutions in terms of prediction accuracy and throughput.
Slicing is an emergent technology for 5G. It decomposes a single Radio Access Network (RAN) into multiple virtual networks "slices" to meet demands in term of throughput, mobility, latency, reliability, etc. Slicing needs real-time reconfigurations to keep current with demands' dynamics. This results in an increased cost of Operation Expenditures (OPEX). We approached this challenge as an optimization problem of infrastructure's resources. We virtualized and pooled Baseband Units (BBUs) resources on cloud. Dynamic allocation and interconnection with Remote Radio Heads (RRHs) are made possible by leveraging the advents of Network Function Virtualization (NFV) and Software-defined Networking (SDN). We implemented Distributed Base Station (DBS) using open software platform along to a public service orchestration tool for clouds. Our contribution is integrating service selection and deployment with real-time monitoring that allowed auto-control of resources by looping resources' lifecycle. In our experiments, we deployed several slices and we tested two scenarios. First scenario addressed slices' auto-scaling (Infrastructure Scale-Out/Scale-In) when free resources are available in the pool. Second scenario simulated slices' breathing (orchestration of resources) when the pool of resources is exhausted. In first scenario, results show that leveraging cloud elasticity by auto-scaling resources saves costs by providing exactly "what-is-needed" "when-it-isneeded" in term of cloud computing. In second scenario, results show that slices' breathing maximizes the usability by employing our "inhale-and-exhale" heuristic. It is about reusing resources from under-loaded slices in favor of overloaded ones with seamless effect on the user-experience.
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