Kleio: A Hybrid Memory Page Scheduler with Machine Intelligence
Kleio: A Hybrid Memory Page Scheduler with Machine Intelligence
复制标题
DOI：
10.1145/3307681.3325398
复制
发表时间：
2019-06
期刊：
影响因子：
--
通讯作者：
Thaleia Dimitra Doudali;S. Blagodurov;Abhinav Vishnu;S. Gurumurthi;Ada Gavrilovska
中科院分区：
文献类型：
--
作者：
Thaleia Dimitra Doudali;S. Blagodurov;Abhinav Vishnu;S. Gurumurthi;Ada Gavrilovska
大数据分析对数据中心和EXASCALE计算环境中更多主要记忆容量的需求不断增长，这推动了异质记忆技术的整合。与传统的NUMA系统相比，新技术在访问潜伏期，带宽和容量方面表现出更大的差异。利用这种异质性同时提供应用程序性能提高需要智能数据放置。我们介绍Kleio，这是一个具有机器智能的页面调度程序，用于跨混合内存组件执行的应用程序。克莱奥（Kleio）是一个混合页面调度程序，结合了用于混合记忆的现有的，轻巧的基于历史的数据层级方法，以及基于深层神经网络的新颖智能放置决策。与现有的基于历史的方法相比，我们可以通过使用智能页面调度来实现福利范围，以及选择深度学习算法及其参数，这对此问题空间有效。克莱奥（Kleio）结合了一种新方法，用于优先考虑最高性能提升的页面，同时限制了最终的系统资源开销。我们的绩效评估表明，Kleio平均减少了现有解决方案与具有未来访问模式的Oracle之间的性能差距的80％。 Kleio提供了具有快速有效的神经网络培训和预测准确性水平的混合记忆系统，从而通过有限的资源开销来改善应用程序性能，从而为其在未来系统中的实用集成奠定基础。
The increasing demand of big data analytics for more main memory capacity in datacenters and exascale computing environments is driving the integration of heterogeneous memory technologies. The new technologies exhibit vastly greater differences in access latencies, bandwidth and capacity compared to the traditional NUMA systems. Leveraging this heterogeneity while also delivering application performance enhancements requires intelligent data placement. We present Kleio, a page scheduler with machine intelligence for applications that execute across hybrid memory components. Kleio is a hybrid page scheduler that combines existing, lightweight, history-based data tiering methods for hybrid memory, with novel intelligent placement decisions based on deep neural networks. We contribute new understanding toward the scope of benefits that can be achieved by using intelligent page scheduling in comparison to existing history-based approaches, and towards the choice of the deep learning algorithms and their parameters that are effective for this problem space. Kleio incorporates a new method for prioritizing pages that leads to highest performance boost, while limiting the resulting system resource overheads. Our performance evaluation indicates that Kleio reduces on average 80% of the performance gap between the existing solutions and an oracle with knowledge of future access pattern. Kleio provides hybrid memory systems with fast and effective neural network training and prediction accuracy levels, which bring significant application performance improvements with limited resource overheads, so as to lay the grounds for its practical integration in future systems.