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


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.


Anomaly detection Stream processing QoS CPU-GPU collaboration 


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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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