FSAD: Flow Similarity Analysis for Anomaly Detection in Cloud Applications

• Type: Article in International Conference Proceedings Book
• Date: 2015

Authors

Fu, SB (Author) Other; Kim, H (Author) Other; Rui Prior (Author) FCUP

Conference proceedings International

Title: 2015 IEEE 7TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING TECHNOLOGY AND SCIENCE (CLOUDCOM)

Pages: 426-429

IEEE 7th International Conference on Cloud

vancouver, CANADA, NOV 30-DEC 03, 2015

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 1 Citation

Other information

Authenticus ID: P-00K-AQ2

DOI: 10.1109/cloudcom.2015.74

Abstract (EN): Fast detection of performance anomalies is critical in Cloud applications, but challenging to implement in a general and effective tool with low operational overload. We propose FSAD, a performance anomaly detection system based on the concept of flow similarity. It stems from the observation that, in general, the number of responses generated by a component closely follows the number of received requests, but this relation stops holding in presence of performance anomalies. In FSAD, components are regarded as black boxes, and time series of incoming and outgoing packets are fed to the flow similarity analysis for anomaly detection. The effectiveness of FSAD is demonstrated in experimental results.(1)

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4