Proactive Data Placement for Surveillance Video Processing in Heterogeneous Cluster

Haitao Zhang, Bin Xu, Jin Yan, Lujie Liu, Huadong Ma

Abstract: Large-scale surveillance video analytic is a kind of typical big data application. The Spark framework combined with Hadoop Distributed File System (HDFS) is a promising solution for the efficient surveillance video processing. However, the current HDFS distributes data to multiple nodes according to the disk space availability, and this data placement mode will lead to the serious skew of the video task completion time and the performance degradation of the distributed video processing in the heterogeneous Spark cluster. In this paper, we firstly design a distributed surveillance video processing platform architecture which supports the seamless integration with the standard video surveillance system. Our platform uses the Spark computing framework over the data stored in HDFS. Then, we propose a novel proactive video data placement strategy to schedule the input video data into the appropriate cluster node adaptively. Our strategy adopts a novel Computing Time Prediction Model (CTPM) which can accurately estimate the execution time of the video processing task by incorporating the several important video task features. In our strategy, an Initial Data Placement Algorithm (IDPA) is used to place the data needed by the video processing jobs on the appropriate cluster node in the process of the initial data loading, and then a Data Rebalance Algorithm (DRA) is further used to schedule the data for the workload balancing during the process of the job execution. Finally, we build a distributed surveillance video processing system according to the proposed platform architecture and conduct the extensive experiments. The experimental results verify the accuracy of CTPM and show that our system can reduce the overhead of the data transferring and improve the job execution efficiency compared with the current widely used methods.