意图驱动的路径状态监控,以实现集中的状态感知流转向

Christoph Hardegen
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引用次数: 1

摘要

对网络交换机和链路的运行状态监视使派生基于路径的数据视图成为可能。因此,可以在数据收集期间针对各种监视意图,如利用率和延迟感知。虽然这些目标中的每一个都依赖于一组特定的状态度量,但是可以运行不同的监视方法来收集作为后续分析目的的决策输入的数据基础。此外,路径条件对沿着特定路径转发的单个数据包流的观察状态有影响。虽然路径级状态数据是相关的,例如,为了评估过去的负载比率,以便运行状态感知和有效的路径确定,流级状态有助于监控流体验条件,如实现吞吐量或感知延迟,例如,跟踪对基于流的需求的遵从性。本文提出了一种用于路径状态监控的模块化架构,该架构将端口计数器查询、网络探测和带内网络遥测作为需求驱动数据收集的方法,并将利用率和延迟感知作为监控意图。状态数据由集中控制器采集,分布式模块部署在交换机的数据平面进行数据跟踪,采用可编程交换机作为运行基础,保证监控协议的灵活性。评估表明,连续收集的数据快照允许跟踪准确的路径状态趋势,可以利用路径状态感知流量转向来改善基于流的负载分布,并解决诸如路径负载不平衡或拥堵等低效率问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intent-Driven Path State Monitoring to Enable Centralized State-Aware Flow Steering
Running state monitoring for network switches and links enables the derivation of a path-based data view. Thereby, various monitoring intents like utilization and latency awareness can be targeted during data collection. Whereas each of these objectives relies on a particular set of state metrics, different monitoring methods may be run to gather the data basis serving as decision input for subsequent analysis purposes. In addition, path conditions have an impact on the state observed for individual packet streams being forwarded along a specific path. While path level state data is of relevance, e.g., to evaluate past load ratios in order to run state-aware and efficient path determination, flow level state helps to monitor flow experience conditions like achieved throughput or perceived latency, e.g., to track the compliance with flow-based requirements. This paper presents a modular architecture for path state monitoring that considers port counter query, network probing and in-band network telemetry as methods for demand-driven data collection and focuses on utilization and latency awareness as monitoring intents. State data is collected by a centralized controller in collaboration with distributed modules deployed in a switch’s data plane to run data tracking, wherefore programmable switches are used as operational basis to ensure a flexible monitoring protocol. Evaluations show that continuously collected data snapshots allow to track accurate path state trends that – w.r.t. path state-aware traffic steering – can be leveraged to improve flow-based load distribution across available path capacities and to resolve inefficiencies like imbalanced path load or congestion.
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