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Parallel pull-based LRU: a request distribution algorithm for clustered Web caches using a DSM for memory mapped networks

机译:基于并行拉式的LRU:使用DSM的内存映射网络的群集Web缓存的请求分配算法

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The SIRAC laboratory has developed SciFS, a distributed shared memory (DSM) that tries to benefit from the high performance and the remote addressing capabilities of the scalable coherent interface (SCI) memory mapped network. We use SciFS for high performance cluster computing and we also experiment with it to build large scale clustered Web caches. We propose Whoops! a clustered Web cache prototype with a new request distribution algorithm, called PPBL (Parallel Pull-Based LRU), especially designed for use with a memory mapped network and a DSM system. Unlike other distribution algorithms the decision is distributed over all nodes thus providing better scalability. We evaluate a first PPBL implementation and discuss scalability issues. Then, we propose a solution to build a scalable implementation of PPBL. We conclude with other improvements that can be achieved to build efficient large scale clustered Web caches using a DSM over memory mapped networks.
机译:SIRAC实验室已经开发了SciFS,一种分布式共享内存(DSM),它试图从可伸缩一致接口(SCI)内存映射网络的高性能和远程寻址功能中受益。我们使用SciFS进行高性能群集计算,并对其进行实验以构建大规模的群集Web缓存。我们建议哎呀!一个具有新请求分配算法的群集Web缓存原型,该算法称为PPBL(基于并行拉的LRU),特别设计用于内存映射网络和DSM系统。与其他分布算法不同,该决策分布在所有节点上,因此提供了更好的可伸缩性。我们评估了第一个PPBL实施方案,并讨论了可伸缩性问题。然后,我们提出一种解决方案,以构建PPBL的可扩展实现。我们以在内存映射网络上使用DSM来构建高效的大规模集群Web缓存所能实现的其他改进作为结束。

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