Interleaving Workloads with Performance Guarantees on Storage Clusters

本研究的重点是设计和实现一个轻量级、但又通用的中间件框架，该框架可为存储工作负载与各种需求交织在一起的问题提供有效且可扩展的解决方案。该框架使用简单且非侵入性的工作负载统计信息收集，例如工作负载直方图和时间依赖度量值，以提供对系统工作负载特征及其对系统指标的影响的准确预测。该框架准确、快速地将存储群集中存在并交互的复杂流程映射到可靠的分配决策中。该框架的核心是能够预先评估资源分配策略对系统指标的影响，从而能够在系统资源的多种可能分配中导航，并选择最符合系统目标的资源。这项研究有可能彻底改变存储系统中的自主资源管理，并提供方法来满足相互冲突的目标，例如发现性能与其他指标(包括成本、能源消耗、可靠性和可用性)之间的权衡和依赖关系。该项目使并行和分布式系统的研究生课程能够得到增强，具有新兴范式的方面，如数据密集型、云和绿色计算，并促进了直接参与的多名学生的教育。

This research focuses on the design and implementation of a lightweight, yet, versatile middleware framework that provides effective and scalable solutions to the problem of interleaving storage workloads with a wide spectrum of demands. The framework uses simple and non-intrusive collection of workload statistics such as workload histograms and measures of temporal dependence to provide accurate forecasting of system workload characteristics and their impact on system metrics. The framework maps accurately and swiftly complex processes that exist and interact in storage clusters into robust allocation decisions. Central to the framework is its ability to estimate beforehand the effect of resource allocation policies on system metrics, which enables navigating through multiple possible allocations of system resources and selecting the on that best meets system targets.This research has the potential to revolutionize autonomic resource management in storage systems and provide methodologies to meet conflicting targets such as discovering trade-offs and dependencies between performance and other metrics including cost, energy consumption, reliability, and availability.This project enables enhancement of graduate courses on parallel and distributed systems with aspects of emerging paradigms such as data intensive, cloud, and green computing, as well as advances the education of the multiple students directly involved.