Combining statistical monitoring and predictable recovery for self-management

Proceedings of the ACM SIGSOFT Symposium on the Foundations of Software Engineering

Volumn , Issue , 2004, Pages 49-53

  Fox, Armando ^a   Klclman, Emre ^a   Patterson, David ^b  

^a Stanford University   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANOMALY DETECTION; APRIORI; BASELINE MODELS; E-COMMERCE; ENTERPRISE APPLICATIONS; EXTERNAL STIMULUS; INTERNAL ARCHITECTURE; INTERNET SERVICES; MACHINE LEARNING TECHNIQUES; MEAN RESPONSE TIME; NETWORK-BASED; NEW APPROACHES; NEW SCIENCE; NORMAL OPERATIONS; OPERATIONAL STATISTICS; PATTERN MINING; RESEARCH CHALLENGES; RUNNING-IN; SELF MANAGEMENT; SELF-MANAGING SYSTEMS; SHORT PERIODS; STATISTICAL LEARNING THEORY; STATISTICAL MONITORING; STRUCTURAL BEHAVIORS; SYSTEM DEPENDABILITY; SYSTEMS RESEARCH;

COMPUTER SOFTWARE; INTERNET; LEARNING ALGORITHMS;

MATHEMATICAL MODELS;

EID: 51649120375     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1075405.1075415     Document Type: Conference Paper

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