5G and beyond networks are expected to support flows with varied Quality-of-Service (QoS) requirements under unpredictable traffic conditions. Consequently, designing policies ensuring optimal system utilization in such networks is challenging. Given this, we formulate a long-term time-averaged scheduling problem that minimizes a weighted function of packets dropped by the 5G wireless base station. We then present two policies for this problem. The first is a delay-guaranteed near-optimal policy, and the second is a delay-guaranteed sub-optimal policy that provides flow isolation. We perform extensive simulations to understand the performance of these policies. Further, we study these policies in the presence of a closed-loop flow rate-control mechanism.

Network slicing is a 5G paradigm that enables the creation of on-demand logical networks over shared physical infrastructure. In this paper, we present a framework that allows users to make advance slice reservations with the End-to-End Orchestrator (EEO). Our reservation mechanism enables the EEO to make admission decisions instantly upon request arrival, providing guarantees as to when the request can be enabled. We then proceed to address a relevant revenue maximization problem through an optimal solution, which has factorial time complexity. We also propose a low-complexity algorithm that can efficiently allocate resources for the online version of the problem. We conduct evaluations that demonstrate how the reservation mechanism can potentially improve EEO’s revenue. Additionally, we conduct a study on scenarios where the arrival rates of slice requests exhibit a positive correlation with reservation discounts provided by EEO.