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Cache Support in a High Performance Fault-Tolerant Distributed Storage System for Cloud and Big Data

机译:针对云和大数据的高性能容错分布式存储系统中的缓存支持

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Due to the trends towards Big Data and Cloud Computing, one would like to provide large storage systems that are accessible by many servers. A shared storage can, however, become a performance bottleneck and a single-point of failure. Distributed storage systems provide a shared storage to the outside world, but internally they consist of a network of servers and disks, thus avoiding the performance bottleneck and single-point of failure problems. We introduce a cache in a distributed storage system. The cache system must be fault tolerant so that no data is lost in case of a hardware failure. This requirement excludes the use of the common write-invalidate cache consistency protocols. The cache is implemented and evaluated in two steps. The first step focuses on design decisions that improve the performance when only one server uses the same file. In the second step we extend the cache with features that focus on the case when more than one server access the same file. The cache improves the throughput significantly compared to having no cache. The two-step evaluation approach makes it possible to quantify how different design decisions affect the performance of different use cases.
机译:由于大数据和云计算的趋势,人们希望提供可由许多服务器访问的大型存储系统。但是,共享存储可能成为性能瓶颈和单点故障。分布式存储系统向外界提供共享存储,但是在内部它们由服务器和磁盘的网络组成,从而避免了性能瓶颈和单点故障问题。我们在分布式存储系统中引入缓存。高速缓存系统必须具有容错能力,以便在发生硬件故障时不会丢失任何数据。该要求不包括使用常见的写入无效缓存一致性协议。缓存的实现和评估分为两个步骤。第一步着重于设计决策,这些决策可以在只有一个服务器使用同一文件时提高性能。在第二步中,我们将扩展缓存功能,使其具有针对多个服务器访问同一文件的情况。与没有高速缓存相比,高速缓存显着提高了吞吐量。通过两步评估方法,可以量化不同的设计决策如何影响不同用例的性能。

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