Speaker
Description
Dimensioning and validating large-scale highly-available computing and communication systems necessitate extensive benchmarking campaigns, which generate vast amounts of measurement data. Moreover, models derived from the evaluations of these campaigns should be scalable and portable in the sense that derived conclusions have to be applicable in a variety of deployment configurations of different size.
Identification of outliers which indicate workload induced failures is a core objective of this evaluation process. In operation time, the limited controllability of the external workload needs prevention of performability failures by integrating monitoring logic and, upon necessity, allocation of further resources for proper processing of the increased amount of input..
By its very nature, the problem is the identification of a hybrid workload-performability model. Its first step is the classification of the different operation domains to distinguish between normal, overloaded, etc. states. The next step is to synthetize of a qualitative system control model targeting the mitigation of overload problems.
The presented approach presented relies on visual exploratory analysis of sample data, resulting in a set of hypotheses for the qualitative model. Subsequently, the model is validated by confirmatory analysis carried out with the help of hybrid checking automata over the entire large dataset. If the model turns to be valid, the checking automata form the basis for monitoring and run-time verification, thus facilitating run-time resource control as a byproduct of the benchmark evaluation process.
