A flexible and scalable approach to navigating measurement data in performance management applications

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. This paper considers 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 that are developed in this paper. 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 the efficient storage of measurement names. Solutions are proposed for all of these issues.