分布式系统中的流异常检测
Detecting Flow Anomalies in Distributed Systems
摘要：
在分布式系统的网络深处，人们经常会发现影响其效率和性能的异常。这些异常难以检测，因为分布式系统可能没有足够的传感器来监测网络的互连节点内的业务流。如果不及早发现和纠正，这些异常情况可能会随着时间的推移而加剧，并可能在不可预见的未来对系统造成灾难性的后果。仅使用粗粒度的信息从两个端点的网络流，我们提出了一个网络传输模型和定位算法，检测异常的位置，并使用建议的度量在分布式系统中进行排名。我们评估了我们对城市化城市公共交通系统乘客记录的方法，并将我们的发现与乘客在社交媒体微博上的帖子相关联。我们的实验表明，使用我们的本地化算法得到的度量提供了一个更好的排名的异常相比，从统计模型的标准偏差措施。我们的案例研究还表明，在社交媒体微博中报告的交通事件与我们检测到的异常的位置相匹配，这表明我们的算法在分布式系统中定位异常方面表现良好。
英文摘要：
Deep within the networks of distributed systems, one often finds anomalies that affect their efficiency and performance. These anomalies are difficult to detect because the distributed systems may not have sufficient sensors to monitor the flow of traffic within the interconnected nodes of the networks. Without early detection and making corrections, these anomalies may aggravate over time and could possibly cause disastrous outcomes in the system in the unforeseeable future. Using only coarse-grained information from the two end points of network flows, we propose a network transmission model and a localization algorithm, to detect the location of anomalies and rank them using a proposed metric within distributed systems. We evaluate our approach on passengers' records of an urbanized city's public transportation system and correlate our findings with passengers' postings on social media microblogs. Our experiments show that the metric derived using our localization algorithm gives a better ranking of anomalies as compared to standard deviation measures from statistical models. Our case studies also demonstrate that transportation events reported in social media microblogs matches the locations of our detect anomalies, suggesting that our algorithm performs well in locating the anomalies within distributed systems.
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