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的，但多功能的中间件框架，提供有效的和可扩展的解决方案，交错存储工作负载的问题与广泛的需求。该框架使用简单的和非侵入性的收集工作量统计数据，如工作量直方图和时间依赖性的措施，以提供准确的预测系统工作量的特点和它们对系统指标的影响。该框架将存储集群中存在和交互的复杂进程准确、快速地映射到稳健的分配决策中。该框架的核心是它能够预先估计资源分配策略对系统度量的影响，该研究有可能彻底改变存储系统中的自主资源管理，并提供满足冲突目标的方法，如发现交易，性能和其他指标（包括成本、能耗、可靠性和可用性）之间的相关性和依赖性。该项目能够增强并行和分布式系统的研究生课程，包括数据密集型、云和绿色计算等新兴范例，并促进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.