Title

Detecting Flow Anomalies in Distributed Systems

Publication Type

Conference Proceeding Article

Publication Date

12-2014

Abstract

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 micro blogs. 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 micro blogs matches the locations of our detect anomalies, suggesting that our algorithm performs well in locating the anomalies within distributed systems.

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

2014 IEEE International Conference on Data Mining (ICDM): 14-17 December, Shenzhen, China: Proceedings

First Page

100

Last Page

109

ISBN

9781479943036

Identifier

10.1109/ICDM.2014.94

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

http://dx.doi.org/10.1109/ICDM.2014.94