A relational approach to monitoring complex systems
ACM Transactions on Computer Systems (TOCS)
FAST: A large scale expert system for application and system software performance tuning
SIGMETRICS '88 Proceedings of the 1988 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Monitoring and control of distributed systems
ISCI '90 Proceedings of the first international conference on systems integration on Systems integration '90
A knowledge based decision support system for computer performance management
Decision Support Systems
SIGMETRICS '94 Proceedings of the 1994 ACM SIGMETRICS conference on Measurement and modeling of computer systems
System Performance Tuning
EventBrowser: A Flexible Tool for Scalable Analysis of Event Data
DSOM '99 Proceedings of the 10th IFIP/IEEE International Workshop on Distributed Systems: Operations and Management: Active Technologies for Network and Service Management
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Managing the performance of large, distributed systems requires flexible and scalable approaches to automating measurement navigation. Unfortunately, existing approaches achieve scalability by severely limiting flexibility. Considered here is an approach that infers navigations from a dimensional representation of the measurement name space. Doing so provides flexible navigation and results in dramatic improvements in scalability, as quantified by analytic models herein developed. Indeed, our models indicate that it is inherently unscalable to automate navigation by requiring the specification of relationships between measurement names, as is done in existing approaches. In contrast, the dimensional approach is optimal for the class of data sources considered in our models. Exploiting the dimensional approach requires addressing issues such as: irregularities in the measurement name space; mappings between the name space used for measurement collection and storage and the dimensional structured name space; and efficient storage of measurement names. Solutions are proposed for all of the foregoing.