DocumentCode :
2693190
Title :
Supporting System-wide Similarity Queries for networked system management
Author :
Duan, Songyun ; Zhang, Hui ; Jiang, Guofei ; Meng, Xiaoqiao
Author_Institution :
T.J. Watson Res. Center, IBM, Hawthorne, NY, USA
fYear :
2010
fDate :
19-23 April 2010
Firstpage :
567
Lastpage :
574
Abstract :
Today´s networked systems are extensively instrumented for collecting a wealth of monitoring data. In this paper, we propose a framework called System-wide Similarity Query (S2Q) to support a new type of similarity queries on monitoring data for managing complex networked systems. The similarity queries are defined on a novel data model that captures system states, and the implementation includes a streaming algorithm for online state-modeling computation and a companion graph-based indexing technique for fast retrieval of historical system states. S2Q simplifies many systems management tasks through a simple and intuitive query interface available to operators, and two applications are evaluated in the paper: (i) fast diagnosis of repeated failures in enterprise IT systems, and (ii) automated application traffic profiling on computer networks. For the first application, the diagnosis accuracy can reach 95% on a multi-tier web service testbed. For the second application, major network applications were automatically identified in the traffic logs from a large campus wireless network.
Keywords :
complex networks; computer network management; graph theory; query processing; automated application traffic profiling; campus wireless network; complex networked systems; computer networks; enterprise IT systems; graph-based indexing technique; monitoring data; multitier Web service; networked system management; query interface; state modeling computation; system wide similarity queries; Application software; Computer network management; Data models; Indexing; Information retrieval; Instruments; Monitoring; Telecommunication traffic; Testing; Web services; Communication system; Data management; Data models; Management; Statistical Machine learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Network Operations and Management Symposium (NOMS), 2010 IEEE
Conference_Location :
Osaka
ISSN :
1542-1201
Print_ISBN :
978-1-4244-5366-5
Electronic_ISBN :
1542-1201
Type :
conf
DOI :
10.1109/NOMS.2010.5488451
Filename :
5488451
Link To Document :
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