CAREER: Self-Managing Resource Allocation in Unsupervised Distributed Systems

职业：无监督分布式系统中的自我管理资源分配

• 批准号: 0643505
• 负责人: Abhishek Chandra
• 金额: $ 40万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0643505&HistoricalAwards=false
• 关键词: CAREER; Self; Managing; Resource; Allocation

CAREER: Self-Managing Resource Allocation in Unsupervised Distributed SystemsRecent years have seen a growing deployment of distributed computinginfrastructures such as Grids, PlanetLab, @home, and peer-to-peer systems,that run a variety of Web, commercial, and scientific applications.Many of these infrastructures are unsupervised---they consist of large number of loosely-connected nodes that contribute computational and storage resources but are not centrally managed. Such unsupervised infrastructures are characterized by uncertainty in their resource availability caused by failures, varying load conditions, and node churn, thus putting undue burden onapplication writers and system administrators for the successfuldeployment and execution of applications. This project is developing a self-managing resource allocation framework that would hidethe infrastructure uncertainties and dynamics from applications, while transparently adapting to changing conditions within the infrastructure.As part of this framework, this project is developing techniques for:(i) Predictable resource aggregation to provide resource guarantees to applications in the presence of dynamic loads and changing resource availability,(ii) Reliability-aware resource management to provide desired levelsof reliability and availability, and(iii) System inference and prediction to enable decentralized inferenceof global system conditions for proactive response to dynamic infrastructure changes.These techniques are based on cooperation and redundancy among nodes in the infrastructure to provide scalability and decentralization. The proposed research will have significant impact on distributed computing by enabling effective deployment of large-scale scientific and commercial applications on resource-rich but unreliable infrastructures

职业：在无监督的分布式系统中自我管理资源分配近年来，分布式计算基础设施如网格、PlanetLab、@home和对等系统的部署越来越多，这些基础设施运行各种Web、商业和科学应用程序。这种无监督的基础设施的特点是不确定性，在其资源的可用性所造成的故障，变化的负载条件，和节点的流失，从而把不适当的负担，应用程序的成功部署和执行的应用程序的作者和系统管理员。该项目正在开发一个自我管理的资源分配框架，该框架将隐藏基础设施的不确定性和动态，同时透明地适应基础设施内不断变化的条件。（i）可预测的资源聚合，以在存在动态负载和变化的资源可用性的情况下向应用提供资源保证，（ii）可靠性感知的资源管理，以提供期望水平的可靠性和可用性，以及（iii）系统推理和预测，以实现对全球系统条件的分散式推理，从而主动响应动态基础设施变化。这些技术基于基础设施中节点之间的合作和冗余，以提供可扩展性和去中心化。拟议的研究将通过在资源丰富但不可靠的基础设施上有效部署大规模科学和商业应用程序，对分布式计算产生重大影响