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Task placement for crowd recognition in edge-cloud based urban intelligent video systems

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Abstract

Recently, edge-cloud has attracted much attention by its promising prospect in terms of facilitating the benefits of edge and cloud together. It is promising for urban video systems that require efficient and effective processing for their intelligent monitoring drives on various ends, like sky drones and land cameras. For instance, to support crowd recognition for public safety, the tasks to crowd recognition need to be placed into all processing nodes in the video systems for processing effectively. This is a challenging problem to facilitate the edge-cloud orchestrated scenarios. However, the variability of tasks based on their complexities is not considered fully in existing strategies. In this regard, we model and analyse task placement for crowd recognition in edge-cloud intelligent video systems. Then, our strategies are proposed which are referred to Node-Graph based Task Placement (NGTP) and Cluster-Graph based Task Placement (CGTP). Specifically, with the help of data dependencies, NGTP utilises the greedy approach with node graphs in the centralised way for general scenarios. Comparatively, CGTP utilises data dependency and similarity for task placing in the decentralised way for emergency scenarios. The experiments demonstrate the superior and effectiveness performance in forming tasks cost and running time of our proposed approaches.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant Nos. 61702155, 61972128), the Natural Science Foundation of Anhui Province, China (Grant No. 1808085MF176), and the Fundamental Research Funds for the Central Universities, China (PA2021KCPY0050).

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Authors and Affiliations

  1. School of Software, Hefei University of Technology, Hefei, China

    Gaofeng Zhang & Benzhu Xu

  2. School of Computer and Information, Hefei University of Technology, Hefei, China

    Ensheng Liu, Liqiang Xu & Liping Zheng

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  1. Gaofeng Zhang
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Correspondence to Liping Zheng.

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Zhang, G., Xu, B., Liu, E. et al. Task placement for crowd recognition in edge-cloud based urban intelligent video systems. Cluster Comput 25, 249–262 (2022). https://doi.org/10.1007/s10586-021-03392-3

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