Distributed Resource Management in Workstation and PC Clusters

工作站和 PC 集群中的分布式资源管理

• 批准号: 9632769
• 负责人: Henry Levy
• 金额: $ 20.24万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-07-15 至 1999-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9632769&HistoricalAwards=false
• 关键词: Distributed; Resource; Management; Workstation; PC

Advances in network and processor technology are greatly changing the communication and computational power of local-area clusters of workstations and personal computers. In particular, the replacement of Ethernets by newer switched network technologies, which provide both higher throughput and lower latency, presents an opportunity for operating systems to better use network resources. This research seeks to improve the performance of applications through distributed (cluster-wide) resource management. The project will develop algorithms that permit the effective sharing of all network resources: processors, network, memory, and disk storage. For example, programs on heavily- loaded nodes can use available memory on idle nodes for both file system and virtual memory backing store, rather than disk, thereby reducing the data latency on access to that data. As another example, applications may migrate to nodes that provide either a better processing environment or a better memory environment for their execution. A key issue in developing algorithms for such closely-coupled system is finding the right balance in order to optimize system-wide performance while minimizing any degradation of performance on individual systems as a result of network-wide resource sharing. ***

网络和处理器技术的进步正在极大地改变工作站和个人计算机的局域集群的通信和计算能力。 特别是，以太网被更新的交换网络技术取代，提供更高的吞吐量和更低的延迟，为操作系统提供了更好地使用网络资源的机会。 本研究旨在提高应用程序的性能，通过分布式（集群范围内）的资源管理。 该项目将开发允许有效共享所有网络资源的算法：处理器，网络，内存和磁盘存储。 例如，重负载节点上的程序可以将空闲节点上的可用存储器用于文件系统和虚拟存储器后备存储，而不是磁盘，从而减少访问该数据时的数据延迟。 作为另一个示例，应用程序可以迁移到为其执行提供更好的处理环境或更好的存储器环境的节点。 为这种紧密耦合系统开发算法的一个关键问题是找到正确的平衡，以优化系统范围的性能，同时最大限度地减少由于网络范围的资源共享而导致的单个系统的性能下降。 ***