Personal and Ubiquitous Computing

, Volume 20, Issue 5, pp 795–808 | Cite as

QoS4IVSaaS: a QoS management framework for intelligent video surveillance as a service

  • Weishan Zhang
  • Pengcheng Duan
  • Xiaodan Xie
  • Feng Xia
  • Qinghua Lu
  • Xin Liu
  • Jiehan Zhou
Original Article

Abstract

Quality of service (QoS) is critical for real-time intelligent video surveillance as a service (IVSaaS) platform, which is both computation intensive and data intensive by nature. However, there is scarce work on a QoS framework for IVSaaS platform. In this paper, we propose QoS for intelligent video surveillance as a service, a QoS framework to make computing resources highly available. In the framework, multiple metrics such as throughput, loads of CPU/GPU, memory and IO are taken into account with different time series models to enhance the adaptivity of different video services. A model selection algorithm is proposed to choose the model that achieves the best performance under various error indicators. At the same time, a resource abnormality detection algorithm is designed to detect anomalies when a service is underperformed. Evaluation results show that the proposed QoS framework can successfully ensure QoS by dynamically scheduling computing resources.

Keywords

Anomaly detection Stream processing QoS CPU-GPU collaboration 

References

  1. 1.
    Alamri A, Hossain MS, Almogren A, Hassan MM, Alnafjan K, Zakariah M, Seyam L, Alghamdi A (2015) Qos-adaptive service configuration framework for cloud-assisted video surveillance systems. Multimed Tools Appl 74:1–16Google Scholar
  2. 2.
    Atrey PK, Cavallaro A, Kankanhalli M (2013) Intelligent multimedia surveillance. Springer, BerlinCrossRefGoogle Scholar
  3. 3.
    Bass L, Clements P, Kazman R (2012) Software architecture in practice. Addison-Wesley, BostonGoogle Scholar
  4. 4.
    Diamos GF, Yalamanchili S (2008) Harmony: an execution model and runtime for heterogeneous many core systems. In: Proceedings of the 17th international symposium on high performance distributed computing, ACM, pp 197–200Google Scholar
  5. 5.
    Gao F (2013) Vsaas model on dragon-lab. Int J Multimed Ubiquitous Eng 8(4):293–302Google Scholar
  6. 6.
    Hassan M, Hossain MA, Al-Qurishi M (2014) Cloud-based mobile iptv terminal for video surveillance. In: Advanced Communication Technology (ICACT), 2014 16th International conference on, IEEE, pp 876–880Google Scholar
  7. 7.
    Hassan MM, Hossain MA, Abdullah-Al-Wadud M, Al-Mudaihesh T, Alyahya S, Alghamdi A (2015) A scalable and elastic cloud-assisted publish/subscribe model for iptv video surveillance system. Clust Comput 18(4):1539–1548CrossRefGoogle Scholar
  8. 8.
    Huang T (2014) Surveillance video: the biggest big data. Comput Now 7(2):82–91Google Scholar
  9. 9.
    Kessler C, Dastgeer U, Thibault S, Namyst R, Richards A, Dolinsky U, Benkner S, Träff JL, Pllana S (2012) Programmability and performance portability aspects of heterogeneous multi-/manycore systems. In: IEEE design, automation and test in Europe conference and exhibition (DATE), pp 1403–1408Google Scholar
  10. 10.
    Kim J, Kim H, Lee JH, Lee J (2011) Achieving a single compute device image in opencl for multiple gpus. ACM SIGPLAN Notices 46(8):277–288CrossRefGoogle Scholar
  11. 11.
    Limna T, Tandayya P (2012) Design for a flexible video surveillance as a service. In: IEEE 5th International congress on image and signal processing (CISP), pp 197–201Google Scholar
  12. 12.
    Limna T, Tandayya P (2014) A flexible and scalable component-based system architecture for video surveillance as a service, running on infrastructure as a service. Multimed Tools Appl 73:1–27Google Scholar
  13. 13.
    Limna T, Tandayya P (2015) Video surveillance as a service cost estimation and pricing model. In: IEEE 12th International joint conference on computer science and software engineering (JCSSE), pp 174–179Google Scholar
  14. 14.
    Linderman MD, Collins JD, Wang H, Meng TH (2008) Merge: a programming model for heterogeneous multi-core systems. In: ACM SIGOPS operating systems review, ACM, vol 42, pp 287–296Google Scholar
  15. 15.
    Luk CK, Hong S, Kim H (2009) Qilin: exploiting parallelism on heterogeneous multiprocessors with adaptive mapping. In: 42nd Annual IEEE/ACM International symposium on microarchitecture MICRO-42, IEEE, pp 45–55Google Scholar
  16. 16.
    Maas M, Reames P, Morlan J, Asanović K, Joseph AD, Kubiatowicz J (2012) Gpus as an opportunity for offloading garbage collection. In: ACM SIGPLAN notices, ACM, vol 47, pp 25–36Google Scholar
  17. 17.
    Prati A, Vezzani R, Fornaciari M, Cucchiara R (2013) Intelligent video surveillance as a service. Intelligent multimedia surveillance. Springer, Berlin, pp 1–16CrossRefGoogle Scholar
  18. 18.
    Sharma CM, Kumar H (2014) Architectural framework for implementing visual surveillance as a service. In: IEEE International conference on computing for sustainable global development (INDIACom), pp 296–301Google Scholar
  19. 19.
    Song B, Tian Y, Zhou B (2014) Design and evaluation of remote video surveillance system on private cloud. In: IEEE International symposium on biometrics and security technologies (ISBAST), pp 256–262Google Scholar
  20. 20.
    Sun Y, Song H, Jara AJ, Bie R (2016) Internet of things and big data analytics for smart and connected communities. IEEE Access 4:766–773CrossRefGoogle Scholar
  21. 21.
    Veldema R, Philippsen M (2011) Iterative data-parallel mark&sweep on a gpu. In: ACM SIGPLAN notices, ACM, vol 46, pp 1–10Google Scholar
  22. 22.
    Veldema R, Philippsen M (2012) Parallel memory defragmentation on a gpu. In: Proceedings of the 2012 ACM SIGPLAN workshop on memory systems performance and correctness, ACM, pp 38–47Google Scholar
  23. 23.
    Zhang W, Xu L, Duan P, Gong W, Lu Q, Yang S (2015) A video cloud platform combing online and offline cloud computing technologies. Pers Ubiquitous Comput 19(7):1099–1110CrossRefGoogle Scholar
  24. 24.
    Zivkovic Z (2004) Improved adaptive gaussian mixture model for background subtraction. In: IEEE proceedings of the 17th International conference on pattern recognition (ICPR), vol 2, pp 28–31Google Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • Weishan Zhang
    • 1
  • Pengcheng Duan
    • 1
  • Xiaodan Xie
    • 2
  • Feng Xia
    • 3
  • Qinghua Lu
    • 1
  • Xin Liu
    • 1
  • Jiehan Zhou
    • 4
  1. 1.School of Computer and Communication EngineeringChina University of PetroleumQingdaoChina
  2. 2.Science and Technology on Optical Radiation LaboratoryBeijingChina
  3. 3.Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of SoftwareDalian University of TechnologyDalianChina
  4. 4.Faculty of Information Technology and Electrical EngineeringUniversity of OuluOuluFinland

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