Abstract
The rapid growth of video surveillance systems has brought the trend of analyzing video objects characteristics for subsequent semantic applications. However, the complexity of extracting object features from surveillance video is substantial due to resource consumption in video transmission and computation in a large-scale distributed environment. Video processing jobs should be adequately assigned to distributed processing servers, without violating the capacity requirement in processing video flows. To resolve this issue, we discuss fundamental design principles for the task scheduling in large-scale video processing systems. We present the architecture and methods of distributing jobs in a resource pool, with considerations on important factors such as the prediction of video flow traffic, the processing workload and the heuristic assignment decision. Proposed methods can be selectively implemented in practical systems with emphasis on satisfying different system requirements.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Michalopoulos, P.G., Jacobson, R.D., Anderson, C.A., et al.: Automatic incident detection through video image processing. Ann. N. Y. Acad. Sci. 1078(1), 15–25 (2015)
Wang, X.: Intelligent multi-camera video surveillance: a review. Pattern Recogn. Lett. 34(1), 3–19 (2013)
Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)
Verma, A., Cherkasova, L., Campbell, R.H.: ARIA: automatic resource inference and allocation for mapreduce environments. In: International Conference on Autonomic Computing, Icac 2011, Karlsruhe, Germany, pp. 249–256, June 2011
Cheng, D., Rao, J., Guo, Y., et al.: Improving MapReduce performance in heterogeneous environments with adaptive task tuning. In: The International MIDDLEWARE Conference, pp. 97–108 (2014)
Chen, T.P., Haussecker, H., Bovyrin, A., et al.: Computer vision workload analysis: case study of video surveillance systems. Intel Technol. J. 9(2), 109–118 (2005)
Jokhio, F., Ashraf, A., Lafond, S., et al.: Prediction-based dynamic resource allocation for video transcoding in cloud computing. In: Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, pp. 254–261 (2013)
Chen, J., Mahindra, R., Khojastepour, M.A., et al.: A scheduling framework for adaptive video delivery over cellular networks. In: International Conference on Mobile Computing & Networking, pp. 389–400 (2016)
Uhlig, R., Neiger, G., Rodgers, D., et al.: Intel virtualization technology. Computer 38(5), 48–56 (2005)
Padala, P., Shin, K.G., Zhu, X., et al.: Adaptive control of virtualized resources in utility computing environments. ACM SIGOPS Operating Syst. Rev. 41(3), 289–302 (2007)
Gross, D., Harris, C.M.: Fundamentals of Queueing Theory. Wiley, New York (2008)
Dai, J., Zhao, Y., Liu, Y., et al.: Cloud-assisted analysis for energy efficiency in intelligent video systems. J. Supercomputing 70(3), 1345–1364 (2014)
Acknowledgments
This work was supported in part by the National Science Foundation of China under Grants 61300028, in part by the Project of the Ministry of Public Security under Grant 2014JSYJB009.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Dai, J., Wang, X. (2016). Effective Task Scheduling for Large-Scale Video Processing. In: Wang, G., Ray, I., Alcaraz Calero, J., Thampi, S. (eds) Security, Privacy and Anonymity in Computation, Communication and Storage. SpaCCS 2016. Lecture Notes in Computer Science(), vol 10067. Springer, Cham. https://doi.org/10.1007/978-3-319-49145-5_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-49145-5_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-49144-8
Online ISBN: 978-3-319-49145-5
eBook Packages: Computer ScienceComputer Science (R0)