Performance Models and Systems Optimization for Disk-Bound Applications

阵列性能是数据密集型应用的主要瓶颈，在数据密集型应用的系统建模、算法设计和性能优化方面仍然存在重大挑战。现有的分析模型不包括应用程序的特点，内部磁盘的行为，和I/O互连网络争用。PI建议开发一个集成的执行驱动的仿真环境，其中包括应用程序特性驱动的优化方法。与传统的假设对抗性请求序列和前瞻性的最坏情况研究不同，所提出的方法捕获了真实的应用程序的请求序列的可预测性，预计将为依赖二级存储的大型数据集应用程序开发更好的算法。拟议的环境将基于统一和灵活的磁盘，阵列访问模型，它为算法设计者提供了一个简单的框架，通过在磁盘和存储器之间的互连网络上引入争用，并考虑磁盘访问内的复杂交互，以提高传统模型的准确性，传统模型假定每个磁盘块访问的单位成本是固定的。

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.