Estimation of the Concurrent Capacity of a Streaming Media Server Based on User Behavior Analysis

Qiang Ling *

Department of Automation, University of Science and Technology of China, Hefei 230027, China.

Lixiang Xu

Department of Automation, University of Science and Technology of China, Hefei 230027, China.

Yicheng Zhang

Department of Automation, University of Science and Technology of China, Hefei 230027, China.

Jinfeng Yan

Department of Automation, University of Science and Technology of China, Hefei 230027, China.

*Author to whom correspondence should be addressed.


Abstract

With the fast development of computer and network technology, streaming media has attracted more and more attention. The concurrent capacity is a major performance index, especially for media service providers. In the current literature, the concurrent capacity of a server is usually determined through experiments, which can only be done after building a server and are time-consuming. This paper proposes a method to estimate the concurrent capacity just with the configuration parameters of a server. Due to the fast CPU and high-speed network cards, the bottleneck of the concurrent capacity is the I/O speed, which is determined by both the fast memory and low-speed hard disks in a server. By analyzing the behavior of users, we estimate an upper bound on the percentage of data supplied by the memory, named byte-hit-ratio, under any realistic scheduling policy between the memory and disk for a given memory capacity. Based on the byte-hit-ratio bound, we can obtain an upper bound on the average I/O speed of the server, which is proportional to its concurrent capacity. Our method does not require any actual tests and can guide the design of streaming media servers.

Keywords: Streaming media, concurrent capacity, behavior analysis, byte-hit-ratio


How to Cite

Ling, Qiang, Lixiang Xu, Yicheng Zhang, and Jinfeng Yan. 2013. “Estimation of the Concurrent Capacity of a Streaming Media Server Based on User Behavior Analysis”. Journal of Advances in Mathematics and Computer Science 3 (4):811-21. https://doi.org/10.9734/BJMCS/2013/5152.

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