Advertisement

Cluster Computing

, Volume 22, Supplement 6, pp 13897–13909 | Cite as

AI-based software-defined virtual network function scheduling with delay optimization

  • Dan Liao
  • Yulong Wu
  • Ziyang Wu
  • Zeyuan Zhu
  • Wanting Zhang
  • Gang SunEmail author
  • Victor Chang
Article
First Online:

Abstract

AI-based network function virtualization (NFV) is an emerging technique that separates network control functionality from dedicated hardware middleboxes and is virtualized to reduce capital and operational costs. With the advances of NFV and AI-based software-defined networks, dynamic network service demands can be flexibly and effectively accomplished by connecting multiple virtual network functions (VNFs) running on virtual machines. However, such promising technology also introduces several new research challenges. Due to resource constraints, service providers may have to deploy different service function chains (SFCs) to share the same physical resources. Such sharing inevitably forces the scheduling of the SFCs and resources, which consumes computational time and introduces problems associated with reducing the response delay. In this paper, we address this challenge by developing two dynamic priority methods for queuing AI-based VNFs/services to improve the user experience. We account for both transmission and processing delays in our proposed algorithms and achieve a new processing order (scheduler) for VNFs to minimize the overall scheduling delay. The simulation results indicate that the proposed scheme can promote the performance of AI-based VNFs/services to meet strict latency requirements.

Keywords

Network function virtualization Service function chain Scheduling Delay 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgements

This research was partially supported by National Natural Science Foundation of China (61571098), Fundamental Research Funds for the Central Universities (ZYGX2016J217).

References

  1. 1.
    Li, J., Zhang, Y., Chen, X., et al.: Secure attribute-based data sharing for resource-limited users in cloud computing. Comput. Secur. 72, 1–12 (2018)CrossRefGoogle Scholar
  2. 2.
    Li, P., Li, J., Huang, Z., et al.: Privacy-preserving outsourced classification in cloud computing. Clust. Comput. (2017).  https://doi.org/10.1007/s10586-017-0849-9 CrossRefGoogle Scholar
  3. 3.
    Li, J., Li, J., Chen, X., et al.: Identity-based encryption with outsourced revocation in cloud computing. IEEE Trans. Comput. 64(2), 425–437 (2015)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Li, P., Li, J., Huang, Z., et al.: Multi-key privacy-preserving deep learning in cloud computing. Future Gener. Comput. Syst. 74, 76–85 (2017)CrossRefGoogle Scholar
  5. 5.
    Li, J., Liu, Z., Chen, X., et al.: L-EncDB: a lightweight framework for privacy-preserving data queries in cloud computing. Knowl. Based Syst. 79, 18–26 (2015)CrossRefGoogle Scholar
  6. 6.
    Zhang, Y., Chen, X., Li, J., et al.: Ensuring attribute privacy protection and fast decryption for outsourced data security in mobile cloud computing. Inf. Sci. 379, 42–61 (2017)CrossRefGoogle Scholar
  7. 7.
    Li, J., Li, J., Xie, D., et al.: Secure auditing and deduplicating data in cloud. IEEE Trans. Comput. 65(8), 2386–2396 (2016)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Sun, G., Liao, D., Zhao, D., et al.: Live migration for multiple correlated virtual machines in cloud-based data centers. IEEE Trans. Serv. Comput. 1–14 (2016)Google Scholar
  9. 9.
    Sun, G., Liao, D., Bu, S., et al.: The efficient framework and algorithm for provisioning evolving VDC in federated data centers. Future Gener. Comput. Syst. 73, 79–89 (2017)CrossRefGoogle Scholar
  10. 10.
    Sun, G., Liao, D., Anand, V., et al.: A new technique for efficient live migration of multiple virtual machines. Future Gener. Comput. Syst. 55, 74–86 (2016)CrossRefGoogle Scholar
  11. 11.
    Sun, G., Anand, V., Liao, D., et al.: Power-efficient provisioning for online virtual network requests in cloud-based data centers. IEEE Syst. J. 9(2), 427–441 (2015)CrossRefGoogle Scholar
  12. 12.
    Sun, G., Yu, H., Anand, V., et al.: A cost efficient framework and algorithm for embedding dynamic virtual network requests. Future Gener. Comput. Syst. 29(5), 1265–1277 (2013)CrossRefGoogle Scholar
  13. 13.
    Sun, G., Yu, H., Anand, V., et al.: Optimal provisioning for virtual network request in cloud-based data centers. Photonic Netw. Commun. 24(2), 118–131 (2012)CrossRefGoogle Scholar
  14. 14.
    Sun, G., Yu, H., Li, L., et al.: Exploring online virtual networks mapping with stochastic bandwidth demand in multi-data center. Photonic Netw. Commun. 23(2), 109–122 (2012)CrossRefGoogle Scholar
  15. 15.
    Sun, G., Liao, D., Zhao, D., et al.: Towards provisioning hybrid virtual networks in federated cloud data centers. Future Gener. Comput. Syst. (2017).  https://doi.org/10.1016/j.future.2017.09.065 CrossRefGoogle Scholar
  16. 16.
    Bakkes, S., Spronck, P., Herik, J.: Rapid and reliable adaptation of video game AI. IEEE Trans. Comput. Intell. AI Games 1(2), 93–104 (2009)CrossRefGoogle Scholar
  17. 17.
    Pührer, J.: Towards a simulation-based programming paradigm for AI applications. Comput. Sci. 1–7 (2015)Google Scholar
  18. 18.
    Dietterich, T., Horvitz, E.: Viewpoint rise of concerns about AI: reflections and directions. Commun. ACM 58(10), 38–40 (2015)CrossRefGoogle Scholar
  19. 19.
    Bartoli, G., Marabissi, D., Pucc, R., et al.: AI based network and radio resource management in 5G HetNets. J. Signal Process. Syst. 89(1), 133–143 (2017)CrossRefGoogle Scholar
  20. 20.
    Singhal, S., Daniel, A.: Cluster head selection protocol under node degree, competence level and goodness factor for mobile ad hoc network using AI technique. In: Fourth International Conference on Advanced Computing and Communication Technologies, pp. 415–420, 2014Google Scholar
  21. 21.
    Jukan, A., Chamania, M.: Evolution Towards Smart Optical Networking: Where Artificial Intelligence (AI) Meets the World of Photonics, pp. 1–4, 2017Google Scholar
  22. 22.
    Alemdar, H., Caldwell, N., Leroy, V., et al.: Ternary Neural Networks for Resource-Efficient AI Applications, pp. 1–9, 2017Google Scholar
  23. 23.
    Han, B., Gopalakrishnan, V., Ji, L., et al.: Network function virtualization: challenges and opportunities for innovations. IEEE Commun. Mag. 53(2), 90–97 (2015)CrossRefGoogle Scholar
  24. 24.
    Haque, A., Chandra, S., Khan, L., et al.: Distributed adaptive importance sampling on graphical models using MapReduce. In: IEEE International Conference on Big Data, pp. 597–602, 2014Google Scholar
  25. 25.
    Liu, X., Wang, X., Matwin, S., et al.: Meta-learning for large scale machine learning with MapReduce. In: IEEE International Conference on Big Data, pp. 105–110, 2013Google Scholar
  26. 26.
    Mijumbi, R., Serrat, J., Gorricho, J.L., et al.: Network function virtualization: state-of-the-art and research challenges. IEEE Commun. Surv. Tutor. 18(1), 236–262 (2015)CrossRefGoogle Scholar
  27. 27.
    ETSI GS NFV-PER 001 V1.1.1: Network Functions Virtualisation (NFV): NFV Performance and Portability Best Practices. http://www.etsi.org/deliver/etsi_gs/NFVPER/001_099/001/01.01.01_60/gs_nfv-per001v010101p.pdf
  28. 28.
    ETSI GS NFV-MAN 001 V1.1.1: Network Functions Virtualisation (NFV): Management and Orchestration. http://www.etsi.org/deliver/etsi_gs/NFVMAN/001_099/001/01.01.01_60/gs_nfv-man001v010101p.pdf
  29. 29.
    ETSI GS NFV-INF 004 V1.1.1: Network Functions Virtualisation (NFV): Infrastructure; Hypervisor Domain. http://www.etsi.org/deliver/etsi_gs/NFV-INF/001_099/004/01.01.01_60/gs_nfv-inf004v010101p.pdf
  30. 30.
    Qu, L., Assi, C., Shaban, K.: Delay-aware scheduling and resource optimization with network function virtualization. IEEE Trans. Commun. 64(9), 3746–3758 (2016)CrossRefGoogle Scholar
  31. 31.
    Basta, A., Kellerer, W., Hoffmann, M., et al.: Applying NFV and SDN to LTE mobile core gateways, the functions placement problem. In: The 4th ACM Workshop on All Things Cellular: Operations, Applications and Challenges, pp. 33–38, 2014Google Scholar
  32. 32.
    Luizelli, M.C., Bays, L.R., Buriol, L.S., et al.: Piecing together the NFV provisioning puzzle: efficient placement and chaining of virtual network functions. In: IFIP/IEEE International Symposium on Integrated Network Management, pp. 98–106, 2015Google Scholar
  33. 33.
    Mechtri, M., Ghribi, C., Zeghlache, D.: A scalable algorithm for the placement of service function chains. IEEE Trans. Netw. Serv. Manag. (2016).  https://doi.org/10.1109/TNSM.2016.2598068 CrossRefGoogle Scholar
  34. 34.
    Umeyama, S.: An Eigen decomposition approach to weighted graph matching problems. IEEE Trans. Pattern Anal. Mach. Intell. 10(5), 695–703 (1988)CrossRefGoogle Scholar
  35. 35.
    Chi, P.W., Huang, Y.C., Lei, C.L.: Efficient NFV deployment in data center networks. In: IEEE International Conference on Communications, pp. 5290–5295, 2015Google Scholar
  36. 36.
    Moens, H., Turck, F.D.: VNF-P: a model for efficient placement of virtualized network functions. In: International Conference on Network and Service Management, pp. 418–423, 2014Google Scholar
  37. 37.
    Wang, L., Lu, Z., Wen, X., et al.: Joint optimization of service function chaining and resource allocation in network function virtualization. IEEE Access 4, 8084–8094 (2016)CrossRefGoogle Scholar
  38. 38.
    Mehraghdam, S., Keller, M., Kerl, H.: Specifying and placing chains of virtual network functions. In: IEEE International Conference on Cloud Networking, pp. 7–13, 2014Google Scholar
  39. 39.
    Clayman, S., Maini, E., Galis, A., et al.: The dynamic placement of virtual network functions. In: IEEE Network Operations and Management Symposium (NOMS), pp. 1–9, 2014Google Scholar
  40. 40.
    Kim, S., Han, Y., Park, S.: An energy-aware service function chaining and reconfiguration algorithm in NFV. In: IEEE International Workshops on Foundations and Applications of Self Systems, pp. 54–59, 2016Google Scholar
  41. 41.
    Bruschi, R., Carrega, A., Davoli, F.: A game for energy-aware allocation of virtualized network functions. J. Electr. Comput. Eng. 2016(7), 1–10 (2016)MathSciNetGoogle Scholar
  42. 42.
    Xia, M., Shirazipour, M., Zhang, Y., et al.: Optical service chaining for network function virtualization. IEEE Commun. Mag. 53(4), 152–158 (2015)CrossRefGoogle Scholar
  43. 43.
    Khoury, N.E., Ayoubi, S., Assi, C.: Energy-aware placement and scheduling of network traffic flows with deadlines on virtual network functions. In: IEEE International Conference on Cloud Networking (Cloudnet), pp. 89–94, 2016Google Scholar
  44. 44.
    Kim, S., Park, S., Kim, Y., et al.: VNF-EQ: dynamic placement of virtual network functions for energy efficiency and QoS guarantee in NFV. Clust. Comput. 20(3), 2107–2117 (2017)CrossRefGoogle Scholar
  45. 45.
    Fan, J., Guan, C., Qiao, C., et al.: Guaranteeing Availability for Network Function Virtualization with Geographic Redundancy Deployment (2015). http://hdl.handle.net/10477/41826
  46. 46.
    Guo, T., Wang, N., Moessne, K., et al.: Shared backup network provision for virtual network embedding. IEEE Int. Conf. Commun. 41(4), 1–5 (2011)Google Scholar
  47. 47.
    Kanizo, Y., Rottenstreich, O., Segall, I., et al.: Optimizing virtual backup allocation for middleboxes. In: IEEE International Conference on Network Protocols, pp. 1–10, 2016Google Scholar
  48. 48.
    Kim, H., Yoon, S., Jeon, H., et al.: Service platform and monitoring architecture for network function virtualization (NFV). Clust. Comput. 19(4), 1835–1841 (2016)CrossRefGoogle Scholar
  49. 49.
    Kang, Y., Choi, W., Kim, B., et al.: On tradeoff between the two compromise factors in assigning tasks on a cluster computing. Clust. Comput. 17(3), 861–870 (2014)CrossRefGoogle Scholar
  50. 50.
    Noh, K.: A study on the position of CDO for improving competitiveness based big data in cluster computing environment. Clust. Comput. 19(3), 1659–1669 (2016)CrossRefGoogle Scholar
  51. 51.
    Mijumbi, R., Serrat, J., Gorricho, J.L., et al.: Design and evaluation of algorithms for mapping and scheduling of virtual network functions. In: IEEE Conference on Network Softwarization (NetSoft), pp. 1–9, 2015Google Scholar
  52. 52.
    Chang, V.: Towards data analysis for weather cloud computing. Knowl. Based Syst. 127, 29–45 (2017)CrossRefGoogle Scholar
  53. 53.
    Sun, G., Chang, V., Yang, G., et al.: The cost-efficient deployment of replica servers in virtual content distribution networks for data fusion. Inf. Sci. (2017, in press)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Lab of Optical Fiber Sensing and Communications (Ministry of Education)University of Electronic Science and Technology of ChinaChengduChina
  2. 2.Center for Cyber SecurityUniversity of Electronic Science and Technology of ChinaChengduChina
  3. 3.Xi’an Jiaotong Liverpool UniversitySuzhouChina

Personalised recommendations

Cite article

Buy options