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mPlane: an intelligent measurement plane for the internet

机译:mPlane:互联网的智能测量平台

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摘要

The Internet??s universality is based on its decentralization and diversity. However, its distributed nature leads to operational brittleness and difficulty in identifying the root causes of performance and availability issues, especially when the involved systems span multiple administrative domains. The first step to address this fragmentation is coordinated measurement: we propose to complement the current Internet??s data and control planes with a measurement plane, or mPlane for short. mPlane??s distributed measurement infrastructure collects and analyzes traffic measurements at a wide variety of scales to monitor the network status. Its architecture is centered on a flexible control interface, allowing the incorporation of existing measurement tools through lightweight mPlane proxy components, and offering dynamic support for new capabilities. A focus on automated, iterative measurement makes the platform well-suited to troubleshooting support. This is supported by a reasoning system, which applies machine learning algorithms to learn from success and failure in drilling down to the root cause of a problem. This article describes the mPlane architecture and shows its applicability to several distributed measurement problems involving content delivery networks and Internet service roviders. A first case study presents the tracking and iterative analysis of cache selection policies in Akamai, while a second example focuses on the cooperation between Internet service providers and content delivery networks to better orchestrate their traffic engineering decisions and jointly improve their performance.
机译:互联网的普遍性是基于其分散性和多样性。但是,它的分布式性质导致操作脆弱,并且难以确定性能和可用性问题的根本原因,尤其是当所涉及的系统跨越多个管理域时。解决这种碎片的第一步是协调测量:我们建议用测量平面(简称mPlane)来补充当前Internet的数据和控制平面。 mPlane的分布式测量基础架构可以收集和分析各种规模的流量测量值,以监视网络状态。其架构以灵活的控制界面为中心,允许通过轻量级的mPlane代理组件合并现有的测量工具,并为新功能提供动态支持。专注于自动迭代测量,使该平台非常适合于故障排除支持。这由推理系统支持,该系统应用机器学习算法来从深入研究问题的根本原因中学习成败。本文介绍了mPlane体系结构,并显示了其对涉及内容交付网络和Internet服务提供商的几个分布式测量问题的适用性。第一个案例研究提出了Akamai中缓存选择策略的跟踪和迭代分析,而第二个示例则着重于Internet服务提供商和内容交付网络之间的合作,以更好地协调其流量工程决策并共同提高其性能。

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