  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
