Efficient Service Allocation Scheduling Algorithms for 5G User Equipments in Slice-in-Slice Networks

In 5G, network slicing is a prominent feature to provide end-to-end Quality of Experience (QoE) for specific user equipment (UE) requirements. A key technology that facilitates the enhancement of network slicing performance is Open-Radio Access Network (ORAN). The support of network slicing is an ongoing technical effort in ORAN. This paves the way for implementing challenging and predictive system control strategies to serve several UEs, leveraging UE specific aggregated data, medium access control (MAC) key performance indicators for optimizing per-UE QoE, service allocation and resource scheduling. In this paper, to improve system performance at a granular level, we propose a novel concept of formation of multiple optimized UE service specific slice-in-slice categories across network slices. There have been no clear state-of-the-art that have addressed the relationships and supportability of UEs of different services across slice-in-slice categories during service allocation in network slicing. In this paper, we propose novel Service Allocation Scheduling (SAS) algorithms for UEs services. Novel multi-objective multi-constraint optimization models are formulated to achieve improved service level guarantees. A graph theoretical optimum service allocation strategy is proposed for UE services to maximize the network slice capacity. A probabilistic system resource prediction algorithm for UE services conditioned on previously occurred services is further proposed. Finally, we compared our proposed SAS with state-of-the-art service scheduling algorithms, where SAS ensures 5.12 and 6.22 times throughput gains.