CAREER: Self-Managing Resource Allocation in Unsupervised Distributed Systems

职业生涯：在无人监督的分布式系统中自我管理资源分配近年来，网格、PlanetLab、@Home和点对点系统等分布式计算基础设施的部署越来越多，这些基础设施运行各种Web、商业和科学应用程序。其中许多基础设施是无人监督的-它们由大量松散连接的节点组成，这些节点提供计算和存储资源，但不是集中管理的。这种无人监督的基础设施的特点是由于故障、变化的负载条件和节点波动导致其资源可用性的不确定性，从而为应用程序的成功部署和执行给应用程序编写者和系统管理员带来了不必要的负担。该项目正在开发一个自我管理的资源分配框架，该框架将从应用程序中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。作为该框架的一部分，该项目正在开发以下技术：(I)可预测的资源聚合，以在存在动态负载和不断变化的资源可用性的情况下向应用程序提供资源保证；(Ii)可靠性感知的资源管理，以提供所需的可靠性和可用性水平；以及(Iii)系统推理和预测，以使能够对全球系统条件进行分散推理，以主动响应基础设施的动态变化。这些技术基于基础设施中节点之间的合作和冗余，以提供可伸缩性和分散化。拟议的研究将对分布式计算产生重大影响，因为它能够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

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