Predictive Dynamic Scaling Multi-Slice-in-Slice-Connected Users for 5G System Resource Scheduling

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.

A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches

“…We have learned that network functionality is abstracted from its hardware and software components [4]. This abstraction leads to the formation of slices, and each slice is given a set of functions, services, and system resources, making it an independent, dynamically created logical entity that can support UEs with multiple needs [92]. However, each slice has been forced to carry out dynamic management due to increased demands.…”

“…There has been a lot of research that discusses improving resource scheduling and network slicing processes in Open RAN. Recent research by [92] provided a new algorithm in 5G's slices called dynamic scaling multi-slice-in-slice-connected user equipment services for system resource optimized scheduling (DMUSO) algorithm. Like its name, the DMUSO algorithm uses a concept where, in an UE, slices are connected, so this concept is called multi-slice-in-slice connected UEs.…”

“…Results showed that DMUSO achieves efficient and optimized system resource scheduling, with significant performance gains from 4.4 times to 7.7 times compared to other methods and algorithms. Further research related to DMUSO is still open, and [92] still has to analyze the effect of varying user equipment mobility on resource scheduling across slices in the future.…”