Controllable Queuing System with Elastic Traffic and Signals for Resource Capacity Planning in 5G Network Slicing

Irina Kochetkova (), Kseniia Leonteva, Ibram Ghebrial, Anastasiya Vlaskina, Sofia Burtseva, Anna Kushchazli and Konstantin Samouylov ()
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Irina Kochetkova: Institute of Computer Science and Telecommunications, RUDN University, 6 Miklukho-Maklaya St., 117198 Moscow, Russia
Kseniia Leonteva: Institute of Computer Science and Telecommunications, RUDN University, 6 Miklukho-Maklaya St., 117198 Moscow, Russia
Ibram Ghebrial: Institute of Computer Science and Telecommunications, RUDN University, 6 Miklukho-Maklaya St., 117198 Moscow, Russia
Anastasiya Vlaskina: Institute of Computer Science and Telecommunications, RUDN University, 6 Miklukho-Maklaya St., 117198 Moscow, Russia
Sofia Burtseva: Institute of Computer Science and Telecommunications, RUDN University, 6 Miklukho-Maklaya St., 117198 Moscow, Russia
Anna Kushchazli: Institute of Computer Science and Telecommunications, RUDN University, 6 Miklukho-Maklaya St., 117198 Moscow, Russia
Konstantin Samouylov: Institute of Computer Science and Telecommunications, RUDN University, 6 Miklukho-Maklaya St., 117198 Moscow, Russia

Future Internet, 2023, vol. 16, issue 1, 1-23

Abstract: Fifth-generation (5G) networks provide network slicing capabilities, enabling the deployment of multiple logically isolated network slices on a single infrastructure platform to meet specific requirements of users. This paper focuses on modeling and analyzing resource capacity planning and reallocation for network slicing, specifically between two providers transmitting elastic traffic, such during as web browsing. A controller determines the need for resource reallocation and plans new resource capacity accordingly. A Markov decision process is employed in a controllable queuing system to find the optimal resource capacity for each provider. The reward function incorporates three network slicing principles: maximum matching for equal resource partitioning, maximum share of signals resulting in resource reallocation, and maximum resource utilization. To efficiently compute the optimal resource capacity planning policy, we developed an iterative algorithm that begins with maximum resource utilization as the starting point. Through numerical demonstrations, we show the optimal policy and metrics of resource reallocation for two services: web browsing and bulk data transfer. The results highlight fast convergence within three iterations and the effectiveness of the balanced three-principle approach in resource capacity planning for 5G network slicing.

Keywords: 5G; network slicing; capacity planning; resource reallocation; controller; elastic traffic; Markov decision process (MDP); queuing system; signal; continuous-time Markov chain (CTMC) (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
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