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...

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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
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Zusammenfassung: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 named flow intensity to measure the intensity with which internal monitoring data reacts to the volume of user requests. We calculated flow intensity measurements from monitoring data and proposed an algorithm to automatically search constant relationships between flow intensities measured at various points across distributed systems. If such relationships hold all the time, we regard them as invariants of the underlying systems. Invariants can be used to characterize complex systems and support various system management tasks. However, the computational complexity of the previous invariant search algorithm is high so that it may not scale well in large systems with thousands of measurements. In this paper, we propose two efficient but approximate algorithms for inferring invariants in large-scale systems. The computational complexity of new randomized algorithms is significantly reduced, and experimental results from a real system are also included to demonstrate the accuracy and efficiency of our new algorithms.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2007.190648