Joint QoS and energy-efficient resource allocation and scheduling in 5G Network Slicing

Abstract

Network Slicing (NS) is fast evolving as a prominent enabler for providing tailored services in the Fifth Generation of cellular networks (5G). Network Slices are virtualized network entities formed over physical substrates, deployed for the customized application use cases. A Network Slice needs to exhibit end to end capabilities and meet Quality of Service (QoS) specifications and Service Level Agreements (SLAs). To provide end-to-end traffic management capabilities in the network slice, firstly, traffic flows are categorized into different priority traffic classes, and their severity levels are assessed. Priorities can be applied across cellular and IP based systems. Machine Learning (ML) algorithms are employed on QoS profile attributes in establishing traffic priorities in slices. Secondly, we propose a novel algorithm for NS Resource Partitioning and User Allocation. We put forward an online virtual backbone based solution for resource allocation and priority class-based packet scheduling. This joint QoS and energy efficiency driven approach is built on top of established traffic classes and dynamic power savings techniques. Finally, through Cognitive Cycles (CC), we devise better network re-configuration to obtain more energy savings. Traffic classifier modules are implemented using Jupyter notebook and Python API. Scheduling and resource allocation modules in networks slices are emulated in Mininet, Flowvisor, and Beacon and POX controllers. The simulation results reveal the reduced node consumption is achieved through the evolutionary CC algorithm, and it outperforms other standard approaches by at least 23%. Similarly, for the traffic priority prediction, from the results, we could infer Gradient Boosting and Random Forest Regressors exhibit superior accuracy with the root mean square deviation of 2.2% and 1.2% respectively when compared to other standard ML algorithms.