Example of Degrading Network Slicing System in Two-Service Retrial Queueing System

Moskaleva, Faina; Lisovskaya, Ekaterina; Lapshenkova, Lyubov; Shorgin, Sergey; Gaidamaka, Yuliya

doi:10.1007/978-3-030-92507-9_23

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…Network slice service planning mechanisms eMBB, mMTC, and URLLC [4,11,12] Mobile virtual network operators (MVNOs) [13] Traffic type: guaranteed bit rate (GBR), non-GBR, streaming, elastic [14] Resource capacity planning mechanisms Weights for services according to bit-rate or latency requirements [12,[15][16][17][18][19][20][21] Service-level agreement (SLA) [22] Slice priorities [18,[23][24][25] Auction model [26][27][28] Isolation to guarantee the minimum and/or maximum bit rate [16,18,[29][30][31][32][33] QoS degradation [34] Three equally weighed criteria: (i) ratio of the average throughput to average delay of one user/slice; (ii) average network throughput subject to average delay constraints; (iii) barrier function [35] Three equally weighed criteria: (i) co-occurrence rate; (ii) maximum deviation; (iii) the time interval within which the deviation is possible [36]…”

Section: Mechanismmentioning

confidence: 99%

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

Kochetkova,

Leonteva,

Ghebrial

et al. 2023

Future Internet

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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.

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