Certifying Software Component Performance Specifications

In component-based software engineering, performance prediction approaches support the design of business information systems on the architectural level. They are based on behavior specifications of components. This work presents a round-trip approach for using, assessing, and certifying the accuracy of parameterized, probabilistic, deterministic, and concurrent performance specifications. Its applicability and effectiveness are demonstrated using the CoCoME benchmark

Um modelo para deployment de componentes em CORBA

Orientador: Edmundo Roberto Mauro MadeiraDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoMestrad

Parameter dependencies for reusable performance specifications of software components

To avoid design-related per­for­mance problems, model-driven performance prediction methods analyse the response times, throughputs, and re­source utilizations of software architectures before and during implementation. This thesis proposes new modeling languages and according model transformations, which allow a reusable description of usage profile dependencies to the performance of software components. Predictions based on this new methods can support performance-related design decisions

Quantitative prediction of quality attributes for component-based software architectures

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Quantifying and Predicting the Influence of Execution Platform on Software Component Performance

The performance of software components depends on several factors, including the execution platform on which the software components run. To simplify cross-platform performance prediction in relocation and sizing scenarios, a novel approach is introduced in this thesis which separates the application performance profile from the platform performance profile. The approach is evaluated using transparent instrumentation of Java applications and with automated benchmarks for Java Virtual Machines

Architecture-Level Software Performance Models for Online Performance Prediction

Proactive performance and resource management of modern IT infrastructures requires the ability to predict at run-time, how the performance of running services would be affected if the workload or the system changes. In this thesis, modeling and prediction facilities that enable online performance prediction during system operation are presented. Analyses about the impact of reconfigurations and workload trends can be conducted on the model level, without executing expensive performance tests