Efficient and Scalable Algorithms for Inferring Likely Invariants in Distributed Systems

Distributed systems generate a large amount of monitoring data such as log files to track their operational status. However, it is hard to correlate such monitoring data effectively across distributed systems and along observation time for system management. In previous work, we proposed a concept n...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on knowledge and data engineering Jg. 19; H. 11; S. 1508 - 1523
Hauptverfasser:	Guofei Jiang, Haifeng Chen, Yoshihira, K.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.11.2007 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Algorithms for data and knowledge management Analysis of Algorithms and Problem Complexity Applied sciences Approximation Complexity Computation Computational complexity Computer networks Computer science; control theory; systems Computerized monitoring Costs Data mining Data processing. List processing. Character string processing Distributed Systems Exact sciences and technology Fault detection Fluid flow measurement Hardware Information systems. Data bases Invariants Large-scale systems Management information systems Memory organisation. Data processing Monitoring Software Studies System Management Systems management Time series analysis Volume measurement Invariant data management Decision support system Complex system Time series Distributed system Randomized algorithm Computational complexity Search algorithm monitoring data Surveillance Storage management Time management Distributed systems randomized algorithms Algorithm complexity Log file Monitoring Large scale system invariants
ISSN:	1041-4347, 1558-2191
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!