Intelligent, Adaptive Parallel File Systems

计算率和输入/产出率之间的差距日益扩大，现在是实现各种国家挑战应用程序高性能的主要障碍。对于具有动态、不规则行为和时变的输入/输出需求的新兴多学科应用尤其如此。多个研究小组对应用程序和物理输入/输出模式进行的广泛分析表明，任何单个并行文件策略都不可能为所有输入/输出模式带来高性能。相反，有必要在缓存和预取系统中积极利用输入/输出访问模式知识，以获得峰值硬件输入/输出性能的相当大一部分。本项目将探讨创建(A)自动行为分类技术以识别和分组应用程序资源请求模式的研究问题，(B)灵活的文件系统策略，该策略可以用存储空间来换取增加的输入/输出带宽，并平衡应用程序内部和应用程序之间相互冲突的资源需求，以及(C)一组模糊逻辑决策机制，用于根据应用程序输入/输出请求和输入/输出系统响应选择文件系统策略。

The widening disparity between computation and input/output rates is now a major impediment to achieving high performance for a wide range of national challenge applications. This is especially true for emerging multidisciplinary applications with dynamic, irregular behavior and time varying input/output demands. An extensive analysis of application and physical input/output patterns by multiple research groups has shown that no single parallel file policy is likely to yield high performance for all input/output patterns. Instead, it will be necessary to aggressively exploit input/output access pattern knowledge in caching and prefetching systems to obtain a substantial fraction of peak hardware input/output performance. This project will explore the research issues underlying creation of (a) automatic behavioral classification techniques to identify and group application resource request patterns, (b) flexible file system policies that can trade storage space for increased input/output bandwidth and balance conflicting resource demands within and across applications, and (c) a set of fuzzy logic decision mechanisms for selecting file system policies based on application input/output requests and input/output system responses.