Transparent Throughput Elasticity for Modern Cloud Storage

Bogdan Nicolae, Pierre Riteau, Zhuo Zhen, K. Keahey
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Abstract

Storage elasticity on the cloud is a crucial feature in the age of data-intensive computing, especially when considering fluctuations of I/O throughput. In this chapter, the authors explore how to transparently boost the I/O bandwidth during peak utilization to deliver high performance without over-provisioning storage resources. The proposal relies on the idea of leveraging short-lived virtual disks of better performance characteristics (and more expensive) to act during peaks as a caching layer for the persistent virtual disks where the application data is stored during runtime. They show how this idea can be achieved efficiently at the block-device level, using a caching mechanism that leverages iterative behavior and learns from past experience. Second, they introduce a corresponding performance and cost prediction methodology. They demonstrate the benefits of our proposal both for micro-benchmarks and for two real-life applications using large-scale experiments. They conclude with a discussion on how these techniques can be generalized for increasingly complex landscape of modern cloud storage.
现代云存储的透明吞吐量弹性
在数据密集型计算时代,云上的存储弹性是一个至关重要的特性,特别是在考虑I/O吞吐量波动时。在本章中,作者探讨了如何在峰值利用率期间透明地提高I/O带宽,从而在不过度分配存储资源的情况下提供高性能。该建议依赖于利用具有更好性能特征(但更昂贵)的短寿命虚拟磁盘的思想,在高峰期间充当持久虚拟磁盘的缓存层,在运行时存储应用程序数据。他们展示了如何在块设备级别有效地实现这一想法,使用利用迭代行为和从过去经验中学习的缓存机制。其次,他们介绍了相应的性能和成本预测方法。它们展示了我们的建议在微基准测试和两个使用大规模实验的实际应用程序中的好处。他们最后讨论了如何将这些技术推广到日益复杂的现代云存储环境中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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