Dynamic Cloud Resource Scheduling in Virtualized 5G Mobile Systems

In virtualized networks, network functions are delivered as software running on generic hardware allowing service providers to dynamically allocate resources based on traffic and service demands. Network Function Virtualization (NFV) is becoming a key enabler and consequently a hot research topic. Dynamic scaling of resources in NFV is a highly important challenge towards its implementation in real-life networks. In this paper, we propose a method to predict the required resources in the appropriate time to sustain true elasticity in NFV. The capacity of different Virtualized Network Functions (VNFs) would increase/decrease in a way that the CPU utilization is maximized while the overall cost is minimized. In this paper, we present two strategies to predict the day-ahead CPU utilization. The first strategy is an offline scheduling method that helps managing elasticity in virtualized networks by predicting normal days events. The second one is an online scheduling approach that predicts the day-ahead CPU utilization during sudden peaks due to some unusual circumstances. In this paper, we also present new promising results that show the correlation between the control and data planes. Finally, we propose a hybrid algorithm that uses both strategies to efficiently handle elasticity in virtualized networks. The obtained results are encouraging and are all based on real-life data of mobile operator networks.