Performance Models and Systems Optimization for Disk-Bound Applications

磁盘阵列性能是数据密集型应用程序的主要瓶颈，在数据密集型应用程序的系统建模、算法设计和性能优化方面仍然存在重大挑战。现有的分析模型没有考虑应用特性、内部磁盘行为和I/O互连网络竞争。PI建议开发一个集成的执行驱动的模拟环境，该环境结合了由应用程序特征驱动的优化方法。与传统的假设对抗性请求序列和前瞻的最坏情况研究不同，该方法捕捉真实应用程序请求序列的可预测性，并有望针对依赖于辅助存储的大数据集的应用程序开发更好的算法。该环境将基于统一而灵活的磁盘阵列访问模型，该模型为算法设计者提供了一个简单的框架，通过在磁盘和内存之间的互连网络上引入竞争来推理磁盘阵列上的算法的复杂性，并考虑磁盘访问中的复杂交互以提高精度，而不是传统的假设单位磁盘块访问成本的模型。

Disk-array performance is the primary bottleneck for data-intensive applications, and significant challenges remain in system modeling, algorithm design, and performance optimization of data-intensive applications. Existing analytical models do not incorporate application characteristics, internal disk behavior, and I/O interconnection network contention. The PIs propose to develop an integrated execution-driven simulation environment that incorporates optimization approach driven by application characterization. Unlike traditional worst-case studies that assume adversarial request sequences and lookahead, the proposed approach captures the predictability of the request sequences of real applications, and is expected to lead to the development of better algorithms for applications with large data sets that depend on secondary storage.The proposed environment will be based on a unified and flexible disk-array access model that provides a simple framework for algorithm designers to reason about the complexity of algorithms on disk arrays by incorporating contention on the interconnection network between disks and memory, and consider the complex interactions within a disk access to enhance accuracy over traditional models that assume a flat unit cost per disk block access.