Maximize the Long-Term Average Revenue of Network Slice Provider via Admission Control Among Heterogeneous Slices

• Published in: IEEE/ACM transactions on networking Vol. 32; no. 1; pp. 1 - 16
• Main Authors: Dai, Miao, Sun, Gang, Yu, Hongfang, Niyato, Dusit
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 5G/B5G; Admission control; Algorithms; auction mechanism; Bandwidth; Heuristic methods; Knapsack problem; MKP; Network slicing; Quality of experience; Quality of service architectures; Resource management; Revenue; Service level agreements; service prioritization; slice admission control; Sun
• ISSN: 1063-6692; 1558-2566
• DOI: 10.1109/TNET.2023.3297883

Network slicing endows 5G/B5G with differentiated and customized capabilities to cope with the proliferation of diversified services, whereas limited physical network resources may not be able to support all service requests. Slice admission control is regarded as an essential means to ensure service quality and service isolation when the network is under burden. Herein, the scenario where rational tenants coexist with partially competitive network slice providers is adopted. We aim to maximize the long-term average revenue of the network operators through slice admission control, with the feasibility of multidimensional resource requirements, the priority differences among heterogeneous slices, and the admission fairness within each slice taken into account concurrently. We prove the intractability of our problem by a reduction from the Multidimensional Knapsack Problem (MKP), and propose a two-stage algorithm called MPSAC to make a suboptimal solution efficiently. The principle of MPSAC is to split the original problem into two sub-problems; inter-slice decision-making and intra-slice quota allocation, which are solved using a heuristic method and a tailored auction mechanism respectively. Extensive simulations are carried out to demonstrate the efficacy of our algorithm, the results show that the long-term average revenue of ours is at least 9.6% higher than comparisons while maintaining better priority relations and achieving improved fairness performance.