具有数据管理语言和策略引擎的可编程缓存

Michael Sevilla, C. Maltzahn, P. Alvaro, Reza Nasirigerdeh, B. Settlemyer, D. Perez, D. Rich, G. Shipman
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引用次数: 5

摘要

我们对ParSplice分子动力学模拟生成的键值活动的分析表明,需要更复杂的缓存管理策略。基线测量显示了清晰的键访问模式和热点,为优化提供了重要的机会。我们使用来自Mantle系统的数据管理语言和策略引擎来动态探索各种技术,从基本算法和启发式到统计模型,微积分和机器学习。虽然Mantle最初是为分布式文件系统设计的,但我们将展示抽象集合如何有效地将问题分解为不同应用程序和存储系统的可管理策略。我们对这个空间的探索产生了一个动态大小的缓存策略,它在使用比默认ParSplice配置少32-66%的内存时不会牺牲任何性能。
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Programmable Caches with a Data Management Language and Policy Engine
Our analysis of the key-value activity generated by the ParSplice molecular dynamics simulation demonstrates the need for more complex cache management strategies. Baseline measurements show clear key access patterns and hot spots that offer significant opportunity for optimization. We use the data management language and policy engine from the Mantle system to dynamically explore a variety of techniques, ranging from basic algorithms and heuristics to statistical models, calculus, and machine learning. While Mantle was originally designed for distributed file systems, we show how the collection of abstractions effectively decomposes the problem into manageable policies for a different application and storage system. Our exploration of this space results in a dynamically sized cache policy that does not sacrifice any performance while using 32-66% less memory than the default ParSplice configuration.
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