ITR-(ASE)-(dmc+int): Reconfigurable, Data-driven Resource Allocation in Complex Systems: Practice and Theoretical Foundations

ITR-(ASE)-(dmc int)：复杂系统中可重构、数据驱动的资源分配：实践和理论基础

• 批准号: 0428330
• 负责人: Evgenia Smirni
• 金额: $ 41.39万
• 依托单位: College of William and Mary
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-15 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0428330&HistoricalAwards=false
• 关键词: ITR; ASE; dmc+int; Reconfigurable; Data

Reconfigurable, data-driven resource allocation in complex systems:practice and theoretical foundationsAs web servers are developing into central components in the information infrastructure of our society, it becomes challenging to serve their ever-increasing and diversified customer population while ensuring high availability in a cost-effective way. The complexity of today's web servers systems and the variability of their workload often make effective resource allocation an elusive goal.This proposal seeks support for the development of a data-driven performance-engineering framework to automate the process of robust, workload-aware resource allocation and management in today's complex web server systems. The researcher's focus is on the development of better understanding of the workload resource demands, on the development and implementation of efficient methodologies for bottleneck identification and resource allocation at the system level, and on the development of efficient analytic methodologies for performance prediction. To meet the above targets the following research tasks will be accomplished:o A better understanding of the workload resource demands in web servers that serve dynamic pages will be obtained, focusing on identifying the different resource bottlenecks and the workload conditions under which these bottlenecks are triggered.o A data collection mechanism at the system level will be devised that will gather statistical information, which can prompt scheduler reconfigurations. This mechanism will provide a better understanding on what system and workload data, and at what level of detail, needs to be monitored at run-time to readily provide to the allocation policies information about the state of the system.o New, data-driven scheduling policies will be developed and will be implemented at the system level for the various bottleneck resources that will allow quick system recovery under transient overload conditions.o New theoretical results will allow modeling of the workload and resource allocation policies with compact and tractable models. These models will guide parameterization of the resource allocation policies.Intellectual Merit: The proposed research will advance science and engineering by integrating data and analytic models for the development and implementation on actual systems of both workload-aware and system-aware algorithms to modulate resource allocation in web servers serving dynamic pages under constantly changing workload conditions. The proposed research, even assuming that not all results are positive, will attempt to answer several fundamental questions for the development of cost-effective, autonomic systems. The theoretical contributions of this research will advance the state-of-the-art in modeling of complex systems that are subject to continuous and severe changes in workload intensities and demands.Broader Impact: The impact of this research will affect that state-of-the-practice in actual off-the-shelf systems via industrial collaborations, specifically Seagate Research, by providing algorithms and tools that can modulate and automate the process of resource allocation in complex environments. Through this project, the researcher will also be able to impact the education of several students, preparing them to better meet industry demands in the areas of performance modeling and resource allocation in complex environments.

复杂系统中的可重新配置，数据驱动的资源分配：实践和理论基础，网络服务器正在我们社会的信息基础设施中发展为中心组成部分，服务于他们不断增长和多元化的客户人数的同时确保以成本效益的方式提供高可用性，这是一项挑战。当今的Web服务器系统的复杂性及其工作量的可变性通常使有效的资源分配成为难以捉摸的目标。该提案寻求支持开发数据驱动的性能工程工程框架，以自动化当今复杂的Web服务器系统中强大的，工作负载感知的资源分配和管理过程。研究人员的重点是更好地理解工作量资源的需求，开发和实施有效的瓶颈识别方法和资源分配，以及开发有效的绩效预测方法。为了满足上述目标，将完成以下研究任务：o更好地了解将获得服务的网络服务器中提供动态页面的工作量资源需求，重点是确定触发这些瓶颈的不同资源瓶颈和这些瓶颈的工作量条件。这种机制将提供更好地了解哪些系统和工作负载数据，以及在运行时需要监控的详细信息，以便于提供有关系统状态的分配策略的信息。紧凑和可拖动的模型。这些模型将指导资源分配策略的参数化。智能优点：拟议的研究将通过整合数据和分析模型来推动科学和工程的发展，以开发和实施工作负载感知和系统吸引算法的实际系统，以及在不断变化的工作负载条件下，在不断变化的动态页面中调节网络服务器中的资源分配。拟议的研究，甚至假设并非所有结果都是积极的，都将试图回答几个基本问​​题，以发展具有成本效益的自主系统。这项研究的理论贡献将推进对复杂系统建模的最新建模，这些系统会受到工作负载强度和需求的持续和严重变化。BROADER的影响：这项研究的影响将影响通过工业合作，通过为算法和工具提供精心构建的杂志和自动的工具，通过工业协作，通过工业协作来影响实际的现成系统中的最新实践。通过该项目，研究人员还将能够影响几个学生的教育，为他们在复杂环境中的绩效建模和资源分配方面更好地满足行业需求做好准备。