Advertisement

A Network Edge Monitoring Approach for Real-Time Data Streaming Applications

  • Salman Taherizadeh
  • Ian Taylor
  • Andrew Jones
  • Zhiming Zhao
  • Vlado StankovskiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10382)

Abstract

Renting very high bandwidth or special connection links is neither affordable nor economical for service providers. As a consequence, ensuring data streaming systems to be able to guarantee desired service quality experienced by the users has been a challenging issue due to real-time changes in the network performance of the Internet communications. This paper presents a network monitoring approach that is broadly applicable in the adaptation of real-time services running on network edge computing platforms. The approach identifies runtime variations in the network quality of links between application servers and end-users. It is shown that by identifying critical conditions, it is possible to continuously adapt the deployed service for optimal performance. Adaptation possibilities include reconfiguration by dynamically changing paths between clients and servers, vertical scaling such as re-allocation of bandwidth to specific links, horizontal scaling of application servers, and even live-migration of application components from one edge server to another to improve the application performance.

Keywords

Edge computing Network monitoring Real-time data streaming 

Notes

Acknowledgements

This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 643963 (SWITCH project: Software Workbench for Interactive, Time Critical and Highly self-adaptive cloud applications).

References

  1. 1.
    Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L.: Edge computing - vision and challenges. Technical report MIST-TR, Wayne State University (2016)Google Scholar
  2. 2.
    Zhu, J., Chan, D., Prabhu, M., Natarajan, P., Hu, H., Bonomi, F.: Improving web sites performance using edge servers in fog computing architecture. In: IEEE 7th International Symposium on Service Oriented System Engineering (SOSE), pp. 320–323 (2013)Google Scholar
  3. 3.
    Shojafar, M., Cordeschi, N., Baccarelli, E.: Energy-efficient adaptive resource management for real-time vehicular cloud services. IEEE Trans. Cloud Comput. PP(99), 1–14 (2016)Google Scholar
  4. 4.
    Stojmenovic, I., Wen, S.: The fog computing paradigm - scenarios and security issues. In: Conference on Computer Science and Information Systems (FedCSIS) (2014)Google Scholar
  5. 5.
    Chen, K.T., Chang, Y.C., Hsu, H.J., Chen, D.Y., Huang, C.Y., Hsu, C.H.: On the quality of service of cloud gaming systems. IEEE Trans. Multimedia 16(2), 480–495 (2014)CrossRefGoogle Scholar
  6. 6.
    Jutila, M.: An adaptive edge router enabling internet of things. IEEE Internet Things J. 3(6), 1061–1069 (2016)CrossRefGoogle Scholar
  7. 7.
    Cervino, A.J.: Contribution to multiuser videoconferencing systems based on cloud computing. Doctoral thesis, Technical University of Madrid (2012)Google Scholar
  8. 8.
    Clayman, S., Galis, A., Mamatas, L.: Monitoring virtual networks with lattice. In: Proceedings of 2010 IEEE/IFIP Network Operations and Management Symposium Workshops (NOMS Wksps), Osaka, pp. 239–246. IEEE (2010)Google Scholar
  9. 9.
    Fatema, K., Emeakaroha, V.C., Healy, P.D., Morrison, J.P., Lynn, T.: A survey of cloud monitoring tools: taxonomy, capabilities and objectives. J. Parallel Distrib. Comput. 74(10), 2918–2933 (2014)CrossRefGoogle Scholar
  10. 10.
    Taherizadeh, S., Jones, A.C., Taylor, I., Zhao, Z., Martin, P., Stankovski, V.: Runtime network-level monitoring framework in the adaptation of distributed time-critical cloud applications. In: Proceedings of the 22nd International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 2016), Las Vegas, 6 pp. ACM (2016)Google Scholar
  11. 11.
    Alhamazani, K., Ranjan, R., Mitra, K., Rabhi, F., Jayaraman, P.P., Ullah-Khan, S., Guabtni, A., Bhatnagar, V.: An overview of the commercial cloud monitoring tools: research dimensions, design issues, and state-of-the-art. Computing 97(4), 357–377 (2015)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Nadjaran-Toosi, A., Calheiros, R.N., Buyya, R.: Interconnected cloud computing environments: challenges, taxonomy, and survey. ACM Comput. Surv. (CSUR) 47(1), 1–47 (2014)CrossRefGoogle Scholar
  13. 13.
    Perkins, C., Westerlund, M., Ott, J.: Web Real-Time Communication (WebRTC) media transport and use of RTP. IETF active internet draft (2012)Google Scholar
  14. 14.
    Trihinas, D., Pallis, G., Dikaiakos, M.D.: JCatascopia - monitoring elastically adaptive applications in the cloud. In: 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (2014)Google Scholar
  15. 15.
    Sookhak, M., Gani, A., Talebian, H., Akhunzada, A., Khan, S.U., Buyya, R., Zomaya, A.Y.: Remote data auditing in cloud computing environments: a survey, taxonomy, and open issues. ACM Comput. Surv. (CSUR) 47(4), 1–34 (2015)CrossRefGoogle Scholar
  16. 16.
    Al-Jubouri, B., Gabrys, B.: Multicriteria approaches for predictive model generation: a comparative experimental study. In: 2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM), pp. 64–71. IEEE (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Salman Taherizadeh
    • 1
    • 4
  • Ian Taylor
    • 2
  • Andrew Jones
    • 2
  • Zhiming Zhao
    • 3
  • Vlado Stankovski
    • 4
    Email author
  1. 1.Faculty of Computer and Information ScienceUniversity of LjubljanaLjubljanaSlovenia
  2. 2.School of Computer Science and InformaticsCardiff UniversityCardiffWales
  3. 3.Informatics InstituteUniversity of AmsterdamAmsterdamThe Netherlands
  4. 4.Faculty of Civil and Geodetic EngineeringUniversity of LjubljanaLjubljanaSlovenia

Personalised recommendations