Performance analysis of a pull-based parallel video server

Abstract

AbstractÐIn conventional video-on-demand systems, video data are stored in a video server for delivery to multiple receivers over a communications network. The video server's hardware limits the maximum storage capacity as well as the maximum number of video sessions that can concurrently be delivered. Clearly, these limits will eventually be exceeded by the growing need for better video quality and larger user population. This paper studies a parallel video server architecture that exploits server parallelism to achieve incremental scalability. First, unlike data partition and replication, the architecture employs data striping at the server level to achieve fine-grain load balancing across multiple servers. Second, a client-pull service model is employed to eliminate the need for interserver synchronization. Third, an admission-scheduling algorithm is proposed to further control the instantaneous load at each server so that linear scalability can be achieved. This paper analyzes the performance of the architecture by deriving bounds for server service delay, client buffer requirement, prefetch delay, and scheduling delay. These performance metrics and design tradeoffs are further evaluated using numerical examples. Our results show that the proposed parallel video server architecture can be linearly scaled up to more concurrent users simply by adding more servers and redistributing the video data among the servers.

Comparison of Data Replication Strategies for Peer-to-Peer Video Streaming

“…In the iterative SLF algorithm, all the video replicas are first sorted in non-increasing order according to w j computed from (2). In each iteration the system selects M video replicas with the largest w j and then distributes these M replicas to M different peers according to two rules: (a) the replica with the largest w j is stored in the peer with the smallest load -computed from …”

“…In the literature many researchers have proposed various approaches to improve the scalability of media servers, such as the use of disk arrays [1], parallel servers [2], and distributed servers [3].…”

Optimal video replication and placement on a cluster of video-on-demand servers

“…By contrast, in a distributed storage VoD cluster, each server has its own disk storage subsystem [2,9,12]. The servers are linked by a backbone network.…”

“…The main advantages of striping are high disk utilization and good load balancing ability [2,9,10]. However, wide data striping can induce high scheduling and extension overhead [4,12]. Replication tends to isolate the servers from each other.…”

“…For cost-effectiveness and scalability, the research interest in cluster-based streaming servers (briefly, VoD cluster) has increased dramatically in the past few years [2,9,10,12,15]. A VoD cluster consists of a number of back-end servers controlled by a dispatcher that makes admission decisions.…”