Abstract
Video analytics has gradually increased in recent years. The intelligent CCTV cameras in public places, you-tube videos, etc. generate an enormous amount of video data. Generally, video analytics required more time as it contains several processes like encoding, decoding, etc. There are several existing approaches are evolved in improving the efficiency of video analytics but performance delay and loss of data still existing challenges. With our analysis, we strongly state VM migration will be an effective solution to overcome this delay and performance issues. In this paper, we propose NACT based map reducing mechanism (NACT-Map) for processing the real-time streaming videos. The NACT (Novel Awaiting Computation Time) enables the prediction of VM allocation and automatic migration. The scheduling and allocating of the optimal resource are done by task monitor who utilizes the Task manager (TM) system. The NACT based VM migration and MapReduce technique with Hadoop simplifies the process and minimizes the execution time. The splitting of video into chunks of frames speedup the process. Further efficiency is improved by the Map Reduce technique which uses video and its related content for clusters. The performance of our proposed system is executed in the cloudsim with a large dataset contains two real-time videos. Further, the result is compared with the existing methodologies such as distributed video decoding mechanism with extended FFmpeg and VideoRecordReader (VDMFF) (Yoon et al. in Distributed video decoding on Hadoop. IEICE Trans Inf Syst E101-D(1):2933–2941, 2018) and distributed Video Analytics Framework for Intelligent Video Surveillance (SIAT) (Uddin et al. in SIAT: a distributed video analytics framework for intelligent video surveillance. Symmetry 11:911, 2019). The obtained result shows our proposed NACT_Map consumes minimum Task processing time \(({\text{p}}_{{{\text{tix}}}} )\) and about 90% of efficiency in overall system performance is increased.
Similar content being viewed by others
References
Mind Blowing Youtube Facts, Figures and Statistics. Available at: https://merchdope.com/youtubestats/ (accessed 5 Nov 2018)
Video Streaming Now Makes Up 58% of Internet Usage Worldwide. Available at: http://digg.com/2018/streaming-video-worldwide (accessed 5 Nov 2018)
Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. In: OSDI, 2004
Yoon, I., Yi, S., Oh, C., Jung, H., Yi, Y.: Distributed video decoding on Hadoop. IEICE Trans. Inf. Syst. E101-D(1), 2933–2941 (2018)
Uddin, M., Alam, A., AnhTu, N., Islam, M., Lee, Y.-K.: SIAT: A distributed video analytics framework for intelligent video surveillance. Symmetry 11, 911 (2019). https://doi.org/10.3390/sym11070911www.mdpi.com/journal/symmetry
Tan, H., Chen, L.: An approach for fast and parallel video processing on Apache Hadoop clusters. In: IEEE International Conference on Multimedia and Expo (ICME) (2014). https://doi.org/10.1109/ICME.2014.6890135
Zhao, X., Ma, H., Zhang, H.: HVPI: extending Hadoop to support video analytics applications. In: Proceedings of the IEEE—2015 8th International Conference on Cloud Computing, pp. 789–796 (2015)
Abdullah, T., Anjum, A., Fahim Tariq, M., Baltaci, Y., Antonopoulos, N.: Traffic monitoring using videoanalytics in cloud. In: Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, pp. 39–48 (2014)
Suma, V.: Towards sustainable industrialization using big data and internet of things. J. ISMAC 1(1), 24–37 (2019)
Raj, J.S., Smys, S.: Virtual structure for sustainable wireless networks in cloud services and enterprise information system. J ISMAC 1(3), 188–205 (2019)
Hameed, M., Khalid, H., Azam, F.: Big data: mathematical topology video data analytics using superimposed learning. In: Proceedings of the IEEE—2015 International Conference on Machine Learning, pp. 133–140 (2015)
Muniswamaiah, M., Agerwala, T., Tappert, C.: Big data in cloud computing review and opportunities. Int J Comput Sci Inf Tech 11(4), 43–57 (2019)
Pereira, R., Azambuja, M., Breitman, K., Endler, M.: An architecture for distributed high performance video processing in the cloud. In: Proceedings of the Cloud Computing (CLOUD), 2010 IEEE 3rd International Conference on, pp. 482–489, IEEE (2010)
Uddin, M.A., Akhond, M.R., Lee, Y.-K.: Dynamic scene recognition using spatiotemporal based DLTP on spark. IEEE Access 6, 66123–66133 (2018)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Nirmalan, R., Gokulakrishnan, K. An intelligent surveillance video analytics framework using NACT-Hadoop/MapReduce on cloud services. Distrib Parallel Databases 39, 873–889 (2021). https://doi.org/10.1007/s10619-020-07320-z
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10619-020-07320-z