PSACCF: Prioritized Online Slice Admission Control Considering Fairness in 5G/B5G Networks

• Published in: IEEE transactions on network science and engineering Vol. 9; no. 6; pp. 4101 - 4114
• Main Authors: Dai, Miao, Luo, Long, Ren, Jing, Yu, Hongfang, Sun, Gang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.11.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 5G mobile communication; 5G/B5G; Admission control; Algorithms; fairness; Heuristic methods; Multiple objective analysis; network slice; online slice admission control; Optimization; priority; Quality of experience; Resource allocation; Resource management; Resource utilization; Sensors; Service level agreements; Signal to noise ratio
• ISSN: 2327-4697; 2334-329X
• DOI: 10.1109/TNSE.2022.3195862

5G/B5G is intended to support various services with the assistance of network slices, and each slice requires adequate resources to provide the prenegotiated service quality. The Slice Admission Control (SAC) algorithm is a necessity for Slice Providers (SPs) to guarantee the Quality of Service (QoS) and Quality of Experience (QoE) of each admitted request. In such circumstances, concerns regarding the priority of services and the fairness of resource allocation are meaningful topics. However, these two issues do not receive sufficient research attention simultaneously in the literature. Here, we study the SAC problem in 5G/B5G networks, aiming to enhance the fairness degree while satisfying the necessary priority requirements. We first reinterpret priority as a higher Cumulative Service Acceptance Ratio (CSAR) and adopt the uniformity of adjacent CSAR gaps to reflect fairness. The SAC problem is formulated as a nonlinear and nonconvex multiobjective optimization problem. Thus, we propose a heuristic algorithm called Prioritized Slice Admission Control Considering Fairness (PSACCF) to solve it. This approach introduces the resource efficiency of services to amend priority violations and then promotes fairness by setting the target CSARs for each service type and improving their actual CSARs. Numerous simulations are conducted to compare the performance of PSACCF with that of Moderate High Priority First (MHPF) and Aggressive High Priority First (AHPF). The results show that PSACCF can achieve at least a 33.6% improvement in fairness degree and a higher minimum average resource utilization while maintaining a nearly identical priority indicator to that of the compared methods.