DiscPOP:用于磁盘访问的电源感知缓冲区管理

Xiongzi Ge, D. Feng, D. Du
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引用次数: 4

摘要

在基于磁盘的存储系统的节能缓存缓冲区管理方面进行了大量的研究。如果有大量已知的未来请求,它们中的一些使用贪婪预取技术来人为地增加磁盘空闲间隔。但是,由于没有利用I/O访问模式(顺序/随机)和应用程序模式(计算时间所需的CPU)之间的关系,这可能导致次优解决方案。在cpu密集型应用程序中,与不考虑这种关系的现有高效预取方案相比,通过显式地考虑这种关系,可以减少高达50%的节能,并将电源循环次数增加100%。在本文中,我们通过提出一种基于磁盘特性的功率最优预取(DiscPOP)方案,同时考虑了磁盘功耗、性能保证和磁盘可靠性之间的权衡。具体来说,我们做了两个贡献:(i)建立了一个理论模型来分析磁盘i /O系统中节能缓存缓冲区管理,并将其表述为一个优化问题。我们已经证明了它可以通过整数线性规划(ILP)技术来解决。(ii)我们提出了一种简单的基于分而治之的离线算法贪婪分割(GP),将问题分成几个小问题,并通过ILP求解器分别求解。我们使用跟踪驱动模拟来评估我们提出的方案。结果表明,GP比传统的主动预取节省了29.2%的磁盘能量,减少了20.6%的功率循环。
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DiscPOP: Power-aware buffer management for disk accesses
Much research has been conducted on energy efficient cache buffer management for disk based storage systems. Some of them use greedy prefetching technique to artificially increase disk idle intervals if there are a large number of known future requests. However, this might result in sub-optimal solution by not exploiting the relationship between I/O access pattern (sequential/random) and application pattern (CPU required for computing time). In a CPU-bound application, by explicitly taking into account this relationship it may reduce energy conservation by up to 50% and increase power cycle number by 100% compared to an existing efficient prefetching scheme without this consideration. In this paper, we consider the tradeoff between disk power consumption, performance guarantee and disk reliability all together by proposing a Disk characteristic based Power-Optimal Prefetching (DiscPOP) scheme. Specifically, we make two contributions: (i) A theoretical model is conducted to analyze energy-efficient cache buffer management in disk I/O system and it is formulated as an optimization problem. We have shown it can be solved via an Integer Linear Programming (ILP) technique. (ii) We propose a simple Divide-and-Conquer based offline algorithm named Greedy Partition (GP) to divide the problem into several small ones and solve them separately via an ILP solver. We use trace-driven simulations to evaluate our proposed scheme. The results show GP outperforms the traditional aggressive prefetching by up to 29.2% more disk energy conservation and 20.6% power cycle reduction.
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