Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
120 tokens/sec
GPT-4o
10 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
3 tokens/sec
DeepSeek R1 via Azure Pro
55 tokens/sec
2000 character limit reached

Robust Resource Sharing in Network Slicing via Hypothesis Testing (2404.18254v1)

Published 28 Apr 2024 in cs.NI

Abstract: In network slicing, the network operator needs to satisfy the service level agreements of multiple slices at the same time and on the same physical infrastructure. To do so with reduced provisioned resources, the operator may consider resource sharing mechanisms. However, each slice then becomes susceptible to traffic surges in other slices which degrades performance isolation. To maintain both high efficiency and high isolation, we propose the introduction of hypothesis testing in resource sharing. Our approach comprises two phases. In the trial phase, the operator obtains a stochastic model for each slice that describes its normal behavior, provisions resources and then signs the service level agreements. In the regular phase, whenever there is resource contention, hypothesis testing is conducted to check which slices follow their normal behavior. Slices that fail the test are excluded from resource sharing to protect the well-behaved ones. We test our approach on a mobile traffic dataset. Results show that our approach fortifies the service level agreements against unexpected traffic patterns and achieves high efficiency via resource sharing. Overall, our approach provides an appealing tradeoff between efficiency and isolation.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (26)
  1. C. Marquez, M. Gramaglia, M. Fiore, A. Banchs, and X. Costa-Pérez, “Resource sharing efficiency in network slicing,” IEEE Trans. Netw. Service Manag., vol. 16, no. 3, pp. 909–923, 2019.
  2. P. Nikolaidis, A. Zoulkarni, and J. Baras, “Resource efficiency vs performance isolation tradeoff in network slicing,” in 2023 IEEE WiOpt, 2023, pp. 33–40.
  3. C. Sexton, N. Marchetti, and L. A. DaSilva, “On provisioning slices and overbooking resources in service tailored networks of the future,” IEEE/ACM Trans. Ntw., vol. 28, no. 5, pp. 2106–2119, 2020.
  4. C. Gutterman, E. Grinshpun, S. Sharma, and G. Zussman, “Ran resource usage prediction for a 5g slice broker,” in ACM Mobihoc ’19, New York, NY, USA, 2019, p. 231–240.
  5. S. Alcalá-Marín, A. Bazco-Nogueras, A. Banchs, and M. Fiore, “kansaas: Combining deep learning and optimization for practical overbooking of network slices,” in ACM MobiHoc ’23, 2023, p. 51–60.
  6. A. Banchs, G. de Veciana, V. Sciancalepore, and X. Costa-Perez, “Resource allocation for network slicing in mobile networks,” IEEE Access, vol. 8, pp. 214 696–214 706, 2020.
  7. V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,” ACM Comput. Surv., vol. 41, no. 3, jul 2009.
  8. ——, “Anomaly detection for discrete sequences: A survey,” IEEE Trans. Knowl. Data Eng., vol. 24, no. 5, pp. 823–839, 2012.
  9. S. Wang, J. F. Balarezo, S. Kandeepan, A. Al-Hourani, K. G. Chavez, and B. Rubinstein, “Machine learning in network anomaly detection: A survey,” IEEE Access, vol. 9, pp. 152 379–152 396, 2021.
  10. M. Gupta, J. Gao, C. C. Aggarwal, and J. Han, “Outlier detection for temporal data: A survey,” IEEE Trans. Knowl. Data Eng., vol. 26, no. 9, pp. 2250–2267, 2014.
  11. V. V. Veeravalli and T. Banerjee, “Chapter 6 - quickest change detection,” in Academic Press Library in Signal Processing: Volume 3.   Elsevier, 2014, vol. 3, pp. 209–255.
  12. W. Wang, Q. Chen, T. Liu, X. He, and L. Tang, “A distributed online learning approach to detect anomalies for virtualized network slicing,” in 2021 GLOBECOM, 2021, pp. 1–6.
  13. A. Chawla, A.-M. Bosneag, and A. Dalgkitsis, “Graph-based interpretable anomaly detection framework for network slice management in beyond 5g networks,” in 2023 IEEE/IFIP NOMS, 2023, pp. 1–6.
  14. A. Kumar and V. L. Thing, “Malicious lateral movement in 5g core with network slicing and its detection,” in 2023 ITNAC.   Los Alamitos, CA, USA: IEEE Computer Society, dec 2023, pp. 110–117.
  15. S. A. Baset, L. Wang, and C. Tang, “Towards an understanding of oversubscription in cloud,” in USENIX Hot-ICE ’12, 2012.
  16. F. Caglar and A. Gokhale, “ioverbook: Intelligent resource-overbooking to support soft real-time applications in the cloud,” in 2014 IEEE CLOUD, 2014, pp. 538–545.
  17. D. Shue, M. J. Freedman, and A. Shaikh, “Performance isolation and fairness for Multi-Tenant cloud storage,” in 2012 OSDI.   Hollywood, CA: USENIX Association, Oct. 2012, pp. 349–362.
  18. 3GPP, “LTE; E-UTRA; Physical layer procedures,” 3GPP, TS 36.213, 02 2015, version 12.4.0.
  19. P. Nikolaidis, A. Zoulkarni, and J. S. Baras, “Data-driven bandwidth adaptation for radio access network slices,” arXiv:2311.17347, 2023.
  20. V. Moulos, “A hoeffding inequality for finite state markov chains and its applications to markovian bandits,” in 2020 ISIT, 2020, pp. 2777–2782.
  21. J. Fan, B. Jiang, and Q. Sun, “Hoeffding’s inequality for general markov chains and its applications to statistical learning,” J. Mach. Learning Research, vol. 22, no. 139, pp. 1–35, 2021.
  22. M. J. Neely, “Stochastic network optimization with application to communication and queueing systems,” Synthesis Lectures on Communication Networks, vol. 3, no. 1, pp. 1–211, 2010.
  23. Google-OR-tools, “The knapsack problem,” 2023. [Online]. Available: https://developers.google.com/optimization/pack/knapsack
  24. P. F. Pérez, C. Fiandrino, and J. Widmer, “Characterizing and modeling mobile networks user traffic at millisecond level,” in WiNTECH ’23.   New York, NY, USA: ACM, 2023, p. 64–71.
  25. R. Falkenberg and C. Wietfeld, “Falcon: An accurate real-time monitor for client-based mobile network data analytics,” in 2019 GLOBECOM, 2019, pp. 1–7.
  26. G. Attanasio, C. Fiandrino, M. Fiore, J. Widmer, N. Ludant, B. Bloessl, K. Kousias, Özgü Alay, L. Jacquot, and R. Stanica, “In-depth study of rnti management in mobile networks: Allocation strategies and implications on data trace analysis,” Computer Networks, vol. 219, p. 109428, 2022.

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-up Questions

We haven't generated follow-up questions for this paper yet.

X Twitter Logo Streamline Icon: https://streamlinehq.com