Skip to main content
SpringerLink
Log in

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

  • Published:
Cluster Computing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  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)

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  7. Li, J., Li, J., Xie, D., et al.: Secure auditing and deduplicating data in cloud. IEEE Trans. Comput. 65(8), 2386–2396 (2016)

    Article  MathSciNet  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  17. Pührer, J.: Towards a simulation-based programming paradigm for AI applications. Comput. Sci. 1–7 (2015)

  18. Dietterich, T., Horvitz, E.: Viewpoint rise of concerns about AI: reflections and directions. Commun. ACM 58(10), 38–40 (2015)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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, 2014

  21. Jukan, A., Chamania, M.: Evolution Towards Smart Optical Networking: Where Artificial Intelligence (AI) Meets the World of Photonics, pp. 1–4, 2017

  22. Alemdar, H., Caldwell, N., Leroy, V., et al.: Ternary Neural Networks for Resource-Efficient AI Applications, pp. 1–9, 2017

  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)

    Article  Google Scholar 

  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, 2014

  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, 2013

  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)

    Article  Google Scholar 

  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. 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. 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. Qu, L., Assi, C., Shaban, K.: Delay-aware scheduling and resource optimization with network function virtualization. IEEE Trans. Commun. 64(9), 3746–3758 (2016)

    Article  Google Scholar 

  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, 2014

  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, 2015

  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

    Article  Google Scholar 

  34. Umeyama, S.: An Eigen decomposition approach to weighted graph matching problems. IEEE Trans. Pattern Anal. Mach. Intell. 10(5), 695–703 (1988)

    Article  Google Scholar 

  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, 2015

  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, 2014

  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)

    Article  Google Scholar 

  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, 2014

  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, 2014

  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, 2016

  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)

    MathSciNet  Google Scholar 

  42. Xia, M., Shirazipour, M., Zhang, Y., et al.: Optical service chaining for network function virtualization. IEEE Commun. Mag. 53(4), 152–158 (2015)

    Article  Google Scholar 

  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, 2016

  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)

    Article  Google Scholar 

  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. 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. Kanizo, Y., Rottenstreich, O., Segall, I., et al.: Optimizing virtual backup allocation for middleboxes. In: IEEE International Conference on Network Protocols, pp. 1–10, 2016

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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, 2015

  52. Chang, V.: Towards data analysis for weather cloud computing. Knowl. Based Syst. 127, 29–45 (2017)

    Article  Google Scholar 

  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)

Download references

Acknowledgements

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

Author information

Authors and Affiliations

  1. Key Lab of Optical Fiber Sensing and Communications (Ministry of Education), University of Electronic Science and Technology of China, Chengdu, China

    Dan Liao, Yulong Wu, Ziyang Wu, Zeyuan Zhu, Wanting Zhang & Gang Sun

  2. Center for Cyber Security, University of Electronic Science and Technology of China, Chengdu, China

    Gang Sun

  3. Xi’an Jiaotong Liverpool University, Suzhou, China

    Victor Chang

Authors
  1. Dan Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yulong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ziyang Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zeyuan Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wanting Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gang Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Victor Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gang Sun.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liao, D., Wu, Y., Wu, Z. et al. AI-based software-defined virtual network function scheduling with delay optimization. Cluster Comput 22 (Suppl 6), 13897–13909 (2019). https://doi.org/10.1007/s10586-018-2124-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-2124-0

Keywords

Search

Navigation