Our Requirements for a Workload Generator Empirical evaluation of our research in • Software performance evaluation (van Hoorn, 2007) • Timing behavior anomaly detection and automatic fault localization (Rohr, 2008) • Runtime reconfiguration (Matevska and Hasselbring, 2007) Desired features • Workload specification should be • Maintainable, • Reusable, and • Application-generic • Probabilistic ...

Performance models can represent the performance relevant aspects of an enterprise application. Corresponding simulation engines use such models for simulating performance metrics (e.g., response times, resource utilization, throughput) and allow for performance evaluations without load testing the actual system. Creating such models manually often outweighs their benefits. Therefore, recent re...

When executing time-sensitive distributed applications, a middleware that provides dependability and timeliness is faced with the important problem of preventing timing failures both under normal conditions and when the quality of service is degraded due to replica failures and transient overload on the server. To address this problem, we have designed a probabilistic model-based replica select...

uncertainty is undoubtedly an inevitable part of programming process. a lot of effective factors in the water resources’ performance are not deterministic while their values are surely needed for the design and implantation of water systems. since an optimal design of water distribution systems is in demand of a just evaluation of the costs, efficient operation of such systems calls for a prope...

Several prototypes for predicting the short-term market reaction to news based on text mining techniques have been developed. However, no detailed comparison of the systems and their performances is available thus far. This paper describes the main systems developed and presents a framework for comparing the approaches. The prototypes differ in the text mining methods applied and the data sets ...

In the literature there exist several soft computing methods for building predictive models: neural network models, fuzzy models and probabilistic approaches. In this paper we are interested in the question which one of these approaches is likely to give best performance in practice. We study this problem empirically by selecting a set of typical models from the diierent model families, and by ...