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Machine learning approaches for network resiliency optimization for service provider networks

机译：服务提供商网络网络弹性优化的机器学习方法

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Network Service Provider (NSP), loosely defined as an organization that provides IP Network Transport as a service to either direct consumers or to other value add businesses. NSPs have struggled to reduce subscriber churn which we define as customers switching from current NSP to another competitor NSP due to dissatisfaction, for our purposes specifically dissatisfaction of network performance, such as excess latencies or downtime. The focus of this paper is reliability and maintenance, in particular network resiliency and operations. In the context of this paper, network resiliency is defined as the rate of taking corrective action due to an exogenous network disturbance or event that materially impacts the network service level as experienced by users. Operators not only want to mitigate this period of unsatisfactory network service but want to avoid it altogether, at the lowest possible operational costs by proactively monitoring user network experience, to detect anomalies and resolve by automatic root cause determination and ultimately restore satisfactory network service levels. However, in contrast, today, NSPs operate reactively, by employing teams of expensive network engineers, that manually sift through massive amounts of data to determine root causes either as a result of subscribers complaining about poor service (after customer impact) or triggered network alarms that may be a symptom of a more complex underlying root cause, or often noise, not materially impacting users. In this paper we evaluate standard machine learning approaches in extracting root causes and explain a key underlying reason for poor accuracy. The proposed contribution to improve accuracy, is a novel approach using a multi-tier ensemble machine learning approach that dynamically adapts to changing network data features sets or characteristics combinations to yield accurate causal estimations. It is due the complex interactions of different characteristics combinations that impact different algorithms to yield different accurate results. Results show that our approach improves customer experience and network operations by automatically detecting customer impacting network anomalies and identifying root causes with increased accuracy of 65.3% over any single machine learning approach.
机译：网络服务提供商（NSP），松散地定义为将IP网络传输作为服务提供给直接消费者或其他值添加业务的组织。 NSPS已经努力减少用户搅拌，我们将从当前NSP切换到另一个竞争对手NSP的客户，因为我们的目的特别不满，如过多的延迟或停机时间。本文的重点是可靠性和维护，特别是网络弹性和操作。在本文的上下文中，网络弹性被定义为由于外源网络干扰或事件而导致的纠正措施的速度，这些事件会影响用户所经历的网络服务级别。操作员不仅要缓解这一不令人满意的网络服务的时间，而且希望通过主动监控用户网络体验，以最低可能的运营成本来避免它，以通过自动根本原因确定和最终恢复令人满意的网络服务水平的最低可能的运营成本。然而，与此同时，今天，NSP通过使用昂贵的网络工程师的团队来运作，通过昂贵的网络工程师的团队来通过大量数据筛选，以确定根本原因，也可以是抱怨服务不良服务（在客户影响之后）或触发网络警报这可能是一种更复杂的根本原因，或经常噪音，而不是物质影响用户的症状。本文在提取根本原因中评估标准机器学习方法，并解释了较差准确性的关键原因。提高准确性的拟议贡献是一种新颖的方法，采用多层集合机器学习方法，动态地适应改变网络数据特征集或特征组合，以产生准确的因果估计。它是由于不同特征的复杂相互作用影响不同算法以产生不同的准确结果。结果表明，我们的方法通过自动检测客户影响网络异常，并通过在任何单机学习方法上识别出65.3％的准确度提高了根本原因，提高了客户体验和网络操作。

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