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Cost-aware caching schemes in heterogeneous storage systems

机译:异构存储系统中的成本感知缓存方案

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Modern single- and multi-processor computer systems incorporate, either directly or through a LAN, a number of storage devices with diverse performance characteristics. These storage devices have to deal with workloads with unpredictable burstiness. A storage-aware caching scheme—that partitions the cache among the disks, and aims at balancing the work across the disks—is necessary in this environment. Moreover, maintaining proper size for these partitions is crucial. Adjusting the partition size after each epoch (a certain time interval) assumes that the workload in the subsequent epoch will show similar characteristics as observed in the current epoch. However, in an environment with highly bursty and time-varying workload such an approach seems to be optimistic. Moreover, the existing storage-aware caching schemes assume linear relationship between cache size and hit ratio. But, in practice a (disk) partition may accumulate cache blocks (thus, choke the remaining disks) without increasing the hit ratio significantly. This disk choking phenomenon may degenerate the performance of the disk system. In this paper, we address the issues of continuous repartitioning and disk choking. First, we present a caching scheme that continuously adjusts the partition size forgoing any periodic activity. Later, considering the disk choking issue, we present a repartitioning framework based on the notion of marginal gains. Experimental results show the effectiveness of our approach. We show that our scheme outperforms the existing storage-aware caching schemes.
机译:现代的单处理器和多处理器计算机系统直接或通过LAN合并了许多具有不同性能特征的存储设备。这些存储设备必须以不可预测的突发性处理工作负载。在这种环境中,需要一种可感知存储的缓存方案,该方案可在磁盘之间分配缓存,并旨在平衡磁盘之间的工作。此外,保持这些分区的适当大小至关重要。在每个时期(一定的时间间隔)后调整分区大小均假定后续时期的工作负载将显示与当前时期相似的特征。但是,在工作量极高且时变频繁的环境中,这种方法似乎是乐观的。此外,现有的存储感知缓存方案假定缓存大小与命中率之间呈线性关系。但是,实际上,(磁盘)分区可能会在不显着增加命中率的情况下累积缓存块(因此,使剩余磁盘阻塞)。这种磁盘阻塞现象可能会降低磁盘系统的性能。在本文中,我们解决了连续重新分区和磁盘阻塞的问题。首先,我们提出一种缓存方案,该方案可以不断调整分区大小,以放弃任何周期性活动。后来,考虑到磁盘阻塞问题,我们提出了基于边际收益概念的重新划分框架。实验结果表明了我们方法的有效性。我们证明了我们的方案优于现有的存储感知缓存方案。

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