Multi-dimensional Impact Detection and Diagnosis in Cellular Networks
Multi-dimensional Impact Detection and Diagnosis in Cellular Networks
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DOI：
10.1109/msn50589.2020.00093
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
M. Qureshi;L. Qiu;A. Mahimkar;Jian He;Ghufran Baig
中科院分区：
文献类型：
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作者：
M. Qureshi;L. Qiu;A. Mahimkar;Jian He;Ghufran Baig
性能影响通常在蜂窝网络中观察到，并且由几个因素引起，例如软件升级和配置更改。不同粒度的流量模式的可变性可能导致影响消除或稀释。因此，如果不对有问题的功能进行汇总，则很难捕获性能影响。分析所有可能的功能组合对性能的影响成本太高。另一方面，由于高度动态和异构的蜂窝网络，导致问题的特征集是不可预测的。在本文中，我们提出了一种新的算法，动态地探索那些网络功能组合，可能有问题，通过使用一个摘要结构草图。我们进一步设计了一个基于神经网络的算法来定位根本原因。我们通过利用Lattice和Sketch结构实现了神经网络的高可扩展性。我们证明了我们的影响检测和诊断的有效性，通过广泛的评估，使用从美国的主要一级蜂窝运营商和合成痕迹收集的数据。
Performance impacts are commonly observed in cellular networks and are induced by several factors, such as software upgrade and configuration changes. The variability in traffic patterns across different granularities can lead to impact cancellation or dilution. As a result, performance impacts are hard to capture if not aggregated over problematic features. Analyzing performance impact across all possible feature combinations is too expensive. On the other hand, the set of features that causes issues is unpredictable due to the highly dynamic and heterogeneous cellular networks. In this paper, we propose a novel algorithm that dynamically explores those network feature combinations that are likely to have problems by using a summary structure Sketch. We further design a neural network based algorithm to localize root cause. We achieve high scalability in neural network by leveraging the Lattice and Sketch structure. We demonstrate the effectiveness of our impact detection and diagnosis through extensive evaluation using data collected from a major tier-1 cellular carrier in US and synthetic traces.