Enabling Parallel Processing at the Edge for Real-Time Video Analysis Applications
Abstract
In the context of video analysis systems, distributed processing is a widely recognized strategy for handling large volumes of data and reducing the execution time of complex tasks. In this paper, we present a solution, called Prisma, designed to enable parallel processing by implementing video stream-splitting strategies, which generate multiple derived streams from the original video stream. These derived streams can be distributed to multiple clients, allowing each processing instance to handle only a portion of the original stream while abstracting the complexities of network management and video fragmentation.
Keywords:
Edge Computing, Video analysis, Distributed processing
References
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Zaharia, M., Chowdhury, M., Franklin, M. J., Shenker, S., & Stoica, I. (2010). Spark: Cluster computing with working sets. In 2nd USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 10).
Dean, J., & Ghemawat, S. (2008). Mapreduce: simplified data processing on large clusters. Communications of the ACM, 51(1):107–113.
George, A., & Ravindran, A. (2019). Distributed middleware for edge vision systems. In 2019 IEEE 16th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT and AI (HONET-ICT), pages 193–194. IEEE.
Luu, S., Ravindran, A., Pazho, A. D., & Tabkhi, H. (2022). VEI: a multicloud edge gateway for computer vision in IoT. In Proceedings of the 1st Workshop on Middleware for the Edge (Quebec, Quebec City, Canada) (MIDDLEWEDGE ’22), pages 6–11. Association for Computing Machinery, New York, NY, USA.
Meribout, M., Baobaid, A., Khaoua, M. O., Tiwari, V. K., & Pena, J. P. (2022). State of art IoT and edge embedded systems for real-time machine vision applications. IEEE Access, 10:58287–58301.
Neto, O. d. A. R., Chaves, R. C., Nascimento, A. P., & Gomes, R. D. (2024). Middleware para aplicações distribuídas de vídeo com suporte à computação na borda na indústria 4.0. In Brazilian Symposium on Multimedia and the Web (WebMedia), pages 215–222. SBC.
Perafan-Villota, J. C., Mondragon, O. H., & Mayor-Toro, W. M. (2021). Fast and precise: parallel processing of vehicle traffic videos using big data analytics. IEEE Transactions on Intelligent Transportation Systems, 23(8):12064–12073.
Pereira, R., Azambuja, M., Breitman, K., & Endler, M. (2010). An architecture for distributed high performance video processing in the cloud. In 2010 IEEE 3rd International Conference on Cloud Computing, pages 482-489. IEEE.
Singh, T., Rajput, V., Satakshi, Prasad, U., & Kumar, M. (2023). Real-time traffic light violations using distributed streaming. The Journal of Supercomputing, 79(4):7533–7559.
Wagner, R., Matuschek, M., Knaack, P., Zwick, M., & Geiß, M. (2023). Industrialedgeml - end-to-end edge-based computer vision system for industry 5.0. Procedia Computer Science, 217:594–603. 4th International Conference on Industry 4.0 and Smart Manufacturing.
Zaharia, M., Chowdhury, M., Franklin, M. J., Shenker, S., & Stoica, I. (2010). Spark: Cluster computing with working sets. In 2nd USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 10).
Published
2025-05-19
How to Cite
CHAVES, Rafael C.; SOUZA, Lucas M.; RAMOS NETO, Otacílio A.; GOMES, Ruan D..
Enabling Parallel Processing at the Edge for Real-Time Video Analysis Applications. In: WORKSHOP ON EXPERIMENTAL RESEARCH OF THE FUTURE INTERNET (WPEIF), 16. , 2025, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 58-65.
ISSN 2595-2692.
DOI: https://doi.org/10.5753/wpeif.2025.9521.
