Buffer management and dimensioning for a pull-based parallel video server

Abstract

Recently, there has been a trend toward designing video-on-demand systems using parallel-server architectures. By exploiting server-level parallelism, researchers can break through the performance limit of a single server, while keeping the system cost low by leveraging on commodity hardware platforms. A number of studies have demonstrated the feasibility of building parallel video servers around the client-pull architecture and one can even incorporate data redundancy into the system to sustain server-level failures. However, due to randomness of request arrivals and server processing time, dimensioning the server resource requirement is often difficult. This paper tackles the problem of buffer management and dimensioning for such a pull-based parallel video server. Using a generic buffer-pool model with worst-case analysis, upper bounds on the server buffer requirement are derived for a parallel-server design with multiple disks per server. The obtained bounds are independent of placement policy, video bit-rate, disk-scheduling discipline, and even number of servers in the system, making it applicable to a wide range of server designs. The analytical results also proved that the scalability of this pull-based server design will not be limited by the server buffer requirement.

Multicriteria Resource Brokering in Cloud Computing for Streaming Service

“…Thus network jitter, client download rate, and so forth, which can also affect startup latency, are not considered. According to the studies in [14][15][16][17], single-server streaming systems employ the server-push delivery model while client-pull architecture is more appropriate for multiserver streaming systems. Thus for our streaming service platform, client-pull architecture is assumed.…”

“…Assume is the unwanted positive deviation which numerically exceeds the th goal and let be the unwanted negative deviation which falls short of the th goal. We can express this problem as follows: (17) subject to util IVM + 1 = constraint util IVM (18) util SVM + 2 = constraint util SVM (19) util TVM + 3 = constraint util TVM (20) rt − 4 = constraint rt (21) sl − 5 = constraint sl (22) te + 6 = constraint te (23) ec − 7 = constraint ec (24) pp…”

Evaluation and Analysis of Buffer Requirements for Streamed Video Data in Video on Demand Applications

“…In practice, the ordered delivery of data packets through public domain networks is not guaranteed, which can break the sequential playback of streamed video data [10][11][12]. The performance of such a VOD system is therefore critically dependent upon buffer available with the user [13,14]. The paper concludes by summarizing the limits and bounds for continuous playback versus buffer and real transmission plan [15,16] and poises main challenges for transmission and buffering of streamed video data.…”