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.

复杂系统中可重构、数据驱动的资源分配：实践和理论基础随着网络服务器正在发展成为我们社会信息基础设施的核心组件，在保证高可用性的同时，为不断增长和多样化的客户群提供服务变得具有挑战性。 -有效的方法。当今网络服务器系统的复杂性及其工作负载的多变性常常使有效的资源分配成为一个难以实现的目标。该提案寻求支持开发数据驱动的性能工程框架，以实现稳健的、工作负载感知的资源分配过程的自动化和管理当今复杂的网络服务器系统。研究人员的重点是更好地理解工作负载资源需求，开发和实施系统级瓶颈识别和资源分配的有效方法，以及开发性能预测的有效分析方法。为了实现上述目标，将完成以下研究任务： o 更好地了解服务动态页面的 Web 服务器中的工作负载资源需求，重点识别不同的资源瓶颈以及触发这些瓶颈的工作负载条件o 将设计系统级别的数据收集机制来收集统计信息，这可以提示调度程序重新配置。该机制将提供对哪些系统和工作负载数据以及需要在运行时监控的详细程度的更好理解，以便轻松向分配策略提供有关系统状态的信息。 o 新的、数据驱动的将针对各种瓶颈资源制定并在系统级别实施调度策略，从而允许系统在瞬态过载条件下快速恢复。新的理论结果将允许使用紧凑且易于处理的模型对工作负载和资源分配策略进行建模。这些模型将指导资源分配策略的参数化。 智力优点：所提出的研究将通过集成数据和分析模型来开发和实现实际系统上的工作负载感知和系统感知算法来调节资源分配，从而推动科学和工程的发展在不断变化的工作负载条件下提供动态页面的 Web 服务器中。即使假设并非所有结果都是积极的，拟议的研究也将尝试回答开发具有成本效益的自主系统的几个基本问​​题。这项研究的理论贡献将推动复杂系统建模的最新技术，这些系统会受到工作负载强度和需求的持续和剧烈变化。更广泛的影响：这项研究的影响将影响该状态通过工业合作（特别是希捷研究）在实际现成系统中进行实践，提供可以在复杂环境中调节和自动化资源分配过程的算法和工具。通过这个项目，研究人员还将能够影响几名学生的教育，帮助他们更好地满足复杂环境中性能建模和资源分配领域的行业需求。