CSR: EAGER: An Integrated Framework for Performance and Reliability in Large-scaled Computing Systems

CSR：EAGER：大规模计算系统性能和可靠性的集成框架

• 批准号: 1251129
• 负责人: Ningfang Mi
• 金额: $ 27.24万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1251129&HistoricalAwards=false
• 关键词: CSR; EAGER; Integrated; Framework; Performance

Large-scale computing environments such as data centers and cloud computing are becoming the core computing infrastructure, making the availability of such services extremely critical. However, these environments are increasingly vulnerable to both hardware and software failures. This project designs failure-aware techniques for modeling, prediction, and resource management in large-scale computing environments with the presence of hardware and software failures at various levels. Intellectually, this project develops fundamental understanding of workload and reliability characteristics, and investigates how improved capacity planning models and prediction techniques can obtain useful information for system design and maintenance. This project further provides insights of the impact of software/hardware component failures in the area of resource management. The results of this project will include new capacity planning models that evaluate both reliability and performance of a given system and new prediction techniques that forecast the future failure occurrences by taking advantage of temporal dependence in failure events. Based on the modeling and prediction techniques, this project will develop new failure-aware runtime strategies for job scheduling, node allocation, and system maintenance, aiming to achieve high system performance and reliability in complex large scale systems.

数据中心和云计算等大规模计算环境正在成为核心计算基础设施，这使得此类服务的可用性变得至关重要。然而，这些环境越来越容易受到硬件和软件故障的影响。该项目设计了故障感知技术，用于在存在各种级别的硬件和软件故障的大规模计算环境中进行建模，预测和资源管理。在智力上，该项目开发的工作负载和可靠性特性的基本理解，并研究如何改进的容量规划模型和预测技术可以获得有用的信息，系统的设计和维护。该项目进一步提供了在资源管理领域的软件/硬件组件故障的影响的见解。该项目的结果将包括新的容量规划模型，评估给定系统的可靠性和性能，以及新的预测技术，通过利用故障事件的时间依赖性来预测未来的故障发生。基于建模和预测技术，本项目将开发新的故障感知运行时策略，用于作业调度，节点分配和系统维护，旨在实现复杂大型系统的高系统性能和可靠性。