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Assessment of linux’ data path implementations for download and streaming (2007)
| Venue: | International Journal of Software Engineering and Knowledge Engineering |
| Citations: | 1 - 0 self |
BibTeX
@ARTICLE{Dalseng07assessmentof,
author = {Tom Anders Dalseng and Carsten Griwodz},
title = {Assessment of linux’ data path implementations for download and streaming},
journal = {International Journal of Software Engineering and Knowledge Engineering},
year = {2007}
}
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Abstract
Distributed multimedia streaming systems are increasingly popular due to technological advances, and numerous streaming services are available today. On servers or proxy caches, there is a huge scaling challenge in supporting thousands of concurrent users that request delivery of high-rate, time-dependent data like audio and video, because this requires transfers of large amounts of data through several sub-systems within a streaming node. Unnecessary copy operations in the data path can therefore contribute significantly to the resource consumption of streaming operations. Despite previous research, off-the-shelf operating systems have only limited support for data paths that have been optimized for streaming. Additionally, system call overhead has grown with newer operating systems editions, adding to the cost of data movement. Frequently, it is argued that these issues can be ignored because of the continuing growth of CPU speeds. However, such an argument fails to take problems of modern streaming systems into account. The dissipation of heat generated by disks and high-end CPUs is a major problem of data centers, which would be alleviated if less power-hungry CPUs could be used. The power budget of mobile devices, which are increasingly used for streaming as well, is
Keyphrases
linux data path implementation data path streaming node unnecessary copy operation power-hungry cpu power budget previous research several sub-systems major problem time-dependent data system edition cpu speed system call overhead data movement mobile device huge scaling challenge concurrent user proxy cache limited support large amount resource consumption technological advance data center available today high-end cpu numerous streaming service
