| Abstract |
The smart cities of future need to have a robust and scalable video surveillance infrastructure. In addition it may also make use of citizen contributed video feeds, images and sound clips for surveillance purposes. Multimedia data from various sources need to be stored in large scalable data stores for compulsory retention period, on-line, off-line analytics and archival. Multimedia feeds related to surveillance are voluminous and varied in nature. Apart from large multimedia files, events detected using video analytics and associated metadata needs to be stored. The underlying data storage infrastructure therefore needs to be designed for mainly continuous streaming writes from video cameras and some variety in terms of I/O sizes, read-write mix, random vs. sequential access. As of now, the video surveillance storage domain is mostly dominated by iSCSI based storage systems. Cloud based storage is also provided by some vendors. Taking in account the need for scalability, reliability and data center cost minimization, it is worth investigating if large scale video surveillance backend can be integrated to the open source cloud based data stores available in the "big data" trend. We developed a multimedia surveillance backend system architecture based on the Sensor Web Enablement framework and cloud based "key-value" stores. Our framework gets data from camera/ edge device simulators, splits media files and metadata and stores those in a segregated way in cloud based data stores hosted on Amazons EC2. We have benchmarked performances of a few cloud based key-value stores under large scale video surveillance workload and demonstrated that those perform satisfactorily, bringing in inherent scalability and reliability of a cloud based storage system to a video surveillance system for a smart safe city. With a case study of the storage of video surveillance system, we show in this paper that with the availability of several cloud based distributed data stores and benchmarking tools, an application's data management needs can be served using hybrid cloud based data stores and selection of such stores can be facilitated using benchmark tools if the application workload characteristics are known. |
