Predictive Dynamic Scaling Multi-Slice-in-Slice-Connected Users for 5G System Resource Scheduling (2106.15229v1)

Abstract: Network slicing is an effective 5G concept for improved resource utilization and service scalability tailored to users (UEs) requirements. According to the standardization, 5G system should support UEs through specification of its heterogenous requirements such as, high data rates, traffic density, latency, reliability, UE density, system efficiency and service availability. These requirements are specified as Service Level Agreements (SLAs) between UEs and network operators resulting in increased interest to develop novel challenging mechanisms for improved interference-free resource efficient performances. An emerging concept that enables such demanding SLA assurances is Open-Radio Access Network (O-RAN). In this paper, we study a novel resource scheduling problem for UE services conditioned on other services in network slicing. To improve system performance, we propose that UEs are connected across network slices to serve several applications. UEs of similar service classes (defined by SLAs) are grouped to form optimized slice-in-slice category(s) within network slice(s).We propose novel Predictive Dynamic Scaling Multi-UE service specific System Resource Optimized Scheduling (DMUSO) algorithm(s). Multiobjective multi-constraint optimization problems (MOP) are formed to learn the dynamic system resource allocation and throughput for UE services conditioned on new services entering the network slice. An epsilon-constraint line search algorithm is presented to estimate UE service bandwidth. Using the theoretical models, DMUSO forms optimal slice-in-slice categories and estimates the maximum dynamic additional slice-in-slice categories, throughput served across network slices. Finally, compared to state-of-the-art literatures, DMUSO guarantees UEs SLAs with 4.4 and 7.5 times performance gains