Load Scheduling in a Cloud Based Massive Video-Storage Environment

Abstract

We propose an architecture for a storage system of surveillance videos. Such systems have to handle massive amounts of incoming video streams and relatively few requests for replay. In such a system load (i.e., Write requests) scheduling is essential to guarantee performance. Large-scale data-storage system (LSDSS) is an emerging hosting facility for video-storage, which has a very high number of writes while most of the videos are never or rarely watched. We discuss the design and implementation of LSDSS and load scheduling in autonomous storage environments called datacenters in LSDSS. A datacenter (DC) is the basic concept in our LSDSS, which has the self-management system to store data efficiently. A LSDSS consists of many DCs organized in a hierarchy fashion, thereby decentralizing load scheduling tasks. Because DC has a simple design, load scheduling is particularly suited for implementation on a real-time video surveillance and allows to make scheduling decisions. We also discuss experimental results that clearly show the advantage of load scheduling over the widely known base load scheduling.