A Density-Based Anomaly Detection Method for MapReduce

Author

Wang, Kai ; Wang, Ying ; Yin, Bo

Author_Institution

State Key Lab. of Networking & Switching Technol., Beijing Univ. of Posts & Telecommun., Beijing, China

fYear

2012

fDate

23-25 Aug. 2012

Firstpage

159

Lastpage

162

Abstract

Cloud computing has been more and more popular and widely used as a new model of information technology. In order to achieve a reliable and efficient operation of the cloud environment, it is important for cloud providers to detect and deal with system anomalies in time. In this paper, we present a method for anomaly detection in MapReduce environment. This method is based on peer-similarity and uses density based clustering on OS-level metrics to perform real time analysis. The peer-similarity as well as our anomaly detection method is evaluated through experiments. Compared with other methods, the method proposed in this paper reflects the characteristics of simple, sensitive and efficient. And it can be deployed in both online and offline environment.

Keywords

cloud computing; pattern clustering; security of data; MapReduce environment; OS-level metrics; cloud computing; density based clustering; density-based anomaly detection method; information technology; peer-similarity; real time analysis; Algorithm design and analysis; Clustering algorithms; Complexity theory; Indexes; Measurement; Peer to peer computing; Real time systems; MapReduce; anomaly detection; density-based clustering;

fLanguage

English

Publisher

ieee

Conference_Titel

Network Computing and Applications (NCA), 2012 11th IEEE International Symposium on

Conference_Location

Cambridge, MA

Print_ISBN

978-1-4673-2214-0

Type

conf

DOI

10.1109/NCA.2012.15

Filename

6299088