CAREER: Support for Data-Intensive Distributed Programs in Large Systems

这项研究旨在提供一个新的框架，以支持分布式程序访问存储在数百万台服务器上的大量数据。 该框架由两部分组成：一组可扩展模块，用于提供基本的系统服务，以及构建在这些模块之上的更高级别的策略，用于协调分布式数据访问。 本研究在四个关键子系统的背景下利用这一框架：（1）动态数据复制以提高性能、提高可靠性并减少对存储的人工管理的需要，（2）可扩展和可缩放的数据一致性以协调跨多个节点的数据访问，（3）用于移动的对象的可缩放位置服务，以跟踪对象在动态复制框架中存储在何处，以及（4）基于市场的分布式资源管理，以平衡竞争有限系统资源的大量应用。 这项研究的影响将是更好地了解大规模数据系统所需的基本机制，以及一套在这种具有挑战性的环境中支持新的数据密集型应用程序的技术。 该项目还非常重视操作系统和分布式系统教学的现代化，为学生提供理解和构建大型分布式系统所需的技能和经验。

This research seeks to provide a new framework to support distributed programs that access large amounts of data stored across millions of servers. The framework consists of two parts: a set of extensible modules to provide basic system services plus higher-level policies built on top of these modules to coordinate distributed data access. This research leverages this framework in the context of four key subsystems: (1) dynamic data replication to improve performance, improve reliability, and reduce the need for human management of storage, (2) extensible and scalable data consistency to coordinate data access across multiple nodes, (3) scalable location services for mobile objects to track where objects are stored in the dynamic replication framework, and (4) market-based distributed resource management to balance large numbers of applications competing for limited system resources. The impact of this research will be a better understanding of the fundamental mechanisms required by large-scale data systems and a set of techniques that will support new classes of data-intensive applications in this challenging environment. This project also places a heavy emphasis on modernizing the teaching of operating systems and distributed systems to give students the skills and experience they need to understand and build large-scale distributed systems.