1,218 research outputs found
Detecting Flow Anomalies in Distributed Systems
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
Adapted K-Nearest Neighbors for Detecting Anomalies on Spatio–Temporal Traffic Flow
Outlier detection is an extensive research area, which has been intensively studied in several domains such as biological sciences, medical diagnosis, surveillance, and traffic anomaly detection. This paper explores advances in the outlier detection area by finding anomalies in spatio-temporal urban traffic flow. It proposes a new approach by considering the distribution of the flows in a given time interval. The flow distribution probability (FDP) databases are first constructed from the traffic flows by considering both spatial and temporal information. The outlier detection mechanism is then applied to the coming flow distribution probabilities, the inliers are stored to enrich the FDP databases, while the outliers are excluded from the FDP databases. Moreover, a k-nearest neighbor for distance-based outlier detection is investigated and adopted for FDP outlier detection. To validate the proposed framework, real data from Odense traffic flow case are evaluated at ten locations. The results reveal that the proposed framework is able to detect the real distribution of flow outliers. Another experiment has been carried out on Beijing data, the results show that our approach outperforms the baseline algorithms for high-urban traffic flow
- …