SCORE: A Scalable Architecture for Implementing Resource Management Algorithms in High Speed Networks

SCORE：用于在高速网络中实施资源管理算法的可扩展架构

• 批准号: 9814929
• 负责人: Hui Zhang
• 金额: $ 48.56万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-09-15 至 2003-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9814929&HistoricalAwards=false
• 关键词: SCORE; Scalable; Architecture; Implementing; Resource

The ability to support per-flow management -- that is, to maintain state, manage buffers, perform packet classification and/or scheduling on a per flow basis -- provides a powerful tool to address many of the challenging resource management problems in today's Internet, such as congestion control andend-to-end quality of service. However, given that the number of flows in a backbone routers can be on the order of hundred of thousands, the complexity incurred by per-flow management mechanisms may prevent them from being effectively implemented and widely deployed.To address these problems, the principal investigator (PI) proposes an architecture, called Scalable Core (SCORE for short) in which the network is partitioned in continuous regions, called islands. Within each island the PI distinguishes between edge and core nodes. While the edge nodes perform per flow management, the core nodes do not. Since edge nodes manage fewer flows and at the limit an edge node can be a host, this architecture is highly scalable. The main goal is then to use the SCORE architecture to approximate a reference island in which all nodes perform per flow management. In this way it is possible to provideefficient support for a wide variety of resource management policies.To approximate the reference network the PI introduces two schemes. In the first scheme, each ingress node labels every packet as it enters the network with the state of the flow (e.g., rate) to which it belongs. Further, each core node processes the packet based on its label and the node state (e.g., utilization). In the second scheme, the states of all nodes on the flow's path are aggregated and sent to the edge nodes. Based on this information and the flow state, the edge routers decide how to process an incoming packet (e.g., forward or drop it).The PI plans to develop an integrated set of algorithms within the SCORE framework to support congestion control for best-effort service, end-to-end QoS guarantee, and differential services. He will conduct the study via a combination of analysis, simulation, and implementation. In addition, he plans to conduct scaled experiments over the CAIRN high speed testbed network and work with industrial partners (Cisco, Ascend, 3Com, Intel, MCI) to speed-up the transition of the developed technologies to the industry. The results of this work are expected to have significant impacts on the design of resource management algorithms for the Internet.

支持人均管理的能力 - 即维持状态，管理缓冲区，按照流量进行分类和/或计划 - 提供了一种有力的工具，可以解决当今互联网中许多具有挑战性的资源管理问题，例如拥塞控制和终端的服务质量。但是，鉴于骨干路由器中的流量可能是数百次的顺序，因此每流量管理机制所产生的复杂性可能会阻止它们有效地实施和广泛部署。 在每个岛上，PI区分边缘和核心节点。虽然边缘节点每流量管理执行，但核心节点却没有。由于边缘节点管理更少的流量，并且在极限上可以是主机，因此该体系结构是高度可扩展的。 然后，主要目标是使用分数体系结构来近似一个参考岛，其中所有节点在该岛上执行每个流量管理。通过这种方式，可以为各种资源管理策略提供效率的支持。要近似参考网络，PI引入了两个方案。在第一个方案中，每个输入节点在每个数据包中都标记每个数据包以其属于其所属的流量（例如，速率）进入网络时。此外，每个核心节点基于其标签和节点状态（例如利用率）处理数据包。 在第二个方案中，流动路径上所有节点的状态均已汇总并发送到边缘节点。基于此信息和流状态，边缘路由器决定如何处理传入的数据包（例如，向前或删除它）。PI计划在分数框架内开发一组集成的算法，以支持最佳及时服务，端到端QoS保证和不同服务的交通拥堵控制。 他将通过分析，模拟和实施的结合进行研究。此外，他计划通过CAIRN高速测试网络进行扩展实验，并与工业合作伙伴（Cisco，Ascend，3COM，Intel，MCI）合作，以加快开发技术向行业的过渡。 这项工作的结果预计将对互联网的资源管理算法设计产生重大影响。