CSR: Small: Feedback-Controlled Management of Virtualized Resources for Predictable Escience

CSR：小：虚拟化资源的反馈控制管理，实现可预测的科学

• 批准号: 0916905
• 负责人: Marty Humphrey
• 金额: $ 37.5万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916905&HistoricalAwards=false
• 关键词: CSR; Small; Feedback; Controlled; Management

The use of virtual machines for Escience has been advocated both within the enterprise to replace aging machines and as the underlying technology of cloud computing whereby scientific researchers can ?rent? servers on demand. However, both scenarios can lead to inadequate performance. Within the enterprise, with incorrect planning or under unexpected heavy or even moderate load, there might not be enough physical capacity for every virtual machine to achieve reasonable performance. In cloud-computing-based scenarios, the ?renters? are largely subject to the informal service promises of the cloud provider based on a granularity that can be too coarse or at the wrong level of abstraction. This project pursues a novel unified framework to ensure predictable Escience based on these two dominant emerging uses of virtualized resources. The foundation of the approach is to wrap an Escience application in a performance container framework and dynamically regulate the application?s performance through the application of formal feedback control theory. The application?s progress is monitored and ensured such that the job meets its performance goals (e.g., deadline) without requiring exclusive access to physical resources even in the presence of a wide class of unexpected disturbances. This project extends this foundation and early results in three important dimensions: creating support for non-specialists to use the framework; implementing these techniques in Eucalyptus, one of the major open-source cloud computing frameworks; and applying the techniques to ?Software-as-a-Service? (SaaS), in which applications in the cloud are regulated to provide predictable performance.

在企业内部倡导将虚拟机用于eScience，以取代老化的机器，并将其作为云计算的底层技术，科学研究人员可以借此租用？按需提供服务器。然而，这两种情况都可能导致性能不足。在企业内部，如果规划不正确，或者在意外的繁重甚至中等负载下，可能没有足够的物理容量来满足每台虚拟机实现合理性能的要求。在基于云计算的场景中，租户？在很大程度上受制于云提供商基于过于粗糙或抽象级别错误的粒度做出的非正式服务承诺。该项目追求一种新颖的统一框架，以确保基于虚拟资源的这两个主要新兴用途的可预测的eScience。该方法的基础是将eScience应用程序封装在性能容器框架中，并应用形式反馈控制理论对应用程序S的性能进行动态调整。监控和确保应用程序-S的进度，以确保作业满足其性能目标(例如，截止日期)，而不需要独占访问物理资源，即使在存在广泛的意外干扰的情况下也是如此。该项目在三个重要方面扩展了这一基础和早期成果：创建对非专业人员使用该框架的支持；在主要的开源云计算框架之一Eucalyptus中实现这些技术；将这些技术应用于软件即服务？(SaaS)，对云中的应用进行监管，以提供可预测的性能。