一种IA-64架构下的大规模流媒体服务器缓存调度算法

在大规模流媒体服务中,缓存管理是非常关键的问题.特别是随着IA64架构的出现,物理内存的大小可大大得到增加,缓存管理策略正变得越来越重要.目前已经有很多缓存管理算法,其中间隔缓存策略通常被认为是比较有效的一个.但是以往的各种基于间隔的算法大多没有考虑媒体对象的流行程度,致使缓存的利用率受到了影响.通过对媒体对象的流行程度的特点进行研究,并考虑到利用IA64系统中的大内存的思想,提出了一种基于流行程度的间隔缓存策略.同时,为了分析该算法的性能,引入了一个算法的性能分析模型.分析结果显示该算法比传统的间隔缓存策略具有更好的性能.

A Buffer Management Policy in IA-64 Large-Scale Video Streaming Servers

Buffer management is a very critical problem in large-scale video streaming servers. Especially with the appearance of IA-64, the physical memory size has increased up to 16 exabytes, so the buffer management becomes more and more important. There are already many caching policies, among which interval caching policy has been proposed as an effective one. But most of the previous interval-based policies don't consider the popularity of video objects, and the use of huge memories provided by IA-64 systems. The memory utilization is troubled by this. A popularity-based interval caching policy (PIC) is presented to solve the problem. It makes use of the huge memory in IA-64 systems and pays attention to the popularity of video objects. To study the performance of this policy, a static analytical model is given, and large amount of simulations are conducted. The results show that the PIC policy outperforms the traditional interval caching policy.