Darkstar:使用探索性数据挖掘来提高网络可靠性和性能

C. Kalmanek, Ihui Ge, Seungjoon Lee, C. Lund, Dan Pei, J. Seidel, J. Merwe, Jennifer Ates
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引用次数: 33

摘要

网络已经成为关键的基础设施,基于网络的应用程序的性能要求也越来越严格。这种趋势对服务提供商提出了挑战,要求他们提高网络服务的性能和可靠性。为了实现这一目标,需要新的网络和服务管理系统,使提供商能够不断提高性能,识别在网络操作雷达下飞行的问题,并排除复杂问题。本文介绍了Darkstar系统,该系统允许分析人员使用探索性数据挖掘和复杂的关联工具来解决这些挑战。我们提出了建立在暗星系统之上的关键应用的概述,以说明该方法的力量。
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Darkstar: Using exploratory data mining to raise the bar on network reliability and performance
Networks have become a critical infrastructure, and performance requirements for network-based applications are becoming increasingly stringent. This trend challenges service providers to raise the bar on the performance and reliability of network services. To achieve this, new network and service management systems are needed that enable providers to continually improve performance, identify issues that are flying under the radar of network operations, and troubleshoot complex issues. This paper presents the Darkstar system, which allows analysts to address these challenges using exploratory data mining and sophisticated correlation tools. We present an overview of key applications that are built on top of the Darkstar system to illustrate the power of the approach.
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