Quantitative system performance: computer system analysis using queueing network models
Quantitative system performance: computer system analysis using queueing network models
On time and space decomposition of complex structures
Communications of the ACM
Using regression splines for software performance analysis
Proceedings of the 2nd international workshop on Software and performance
Decomposability, instabilities, and saturation in multiprogramming systems
Communications of the ACM
Performance analysis of distributed server systems
Performance analysis of distributed server systems
Model-Based Performance Prediction in Software Development: A Survey
IEEE Transactions on Software Engineering
Performance Model Estimation and Tracking Using Optimal Filters
IEEE Transactions on Software Engineering
The Palladio component model for model-driven performance prediction
Journal of Systems and Software
Performance evaluation of message-oriented middleware using the SPECjms2007 benchmark
Performance Evaluation
Parametric performance completions for model-driven performance prediction
Performance Evaluation
Statistical inference of software performance models for parametric performance completions
QoSA'10 Proceedings of the 6th international conference on Quality of Software Architectures: research into Practice - Reality and Gaps
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Software performance engineering enables software architects to find potential performance problems, such as bottlenecks and long delays, prior to implementation and testing. Such early feedback on the system's performance is essential to develop and maintain efficient and scalable applications. However, the unavailability of data necessary to design performance models often hinders its application in practice. During system maintenance, the existing system has to be included into the performance model. For large, heterogeneous, and complex systems that have grown over time, modelling becomes infeasible due to the sheer size and complexity of the systems. Re-engineering approaches also fail due to the large and heterogeneous technology stack. Especially for such systems, performance prediction is essential. In this position statement, we propose goal-oriented abstractions of large parts of a software system based on systematic measurements. The measurements provide the information necessary to determine Black-box Performance Models that directly capture the influence of a system's usage and workload on performance (response time, throughput, and resource utilisation). We outline the research challenges that need to be addressed in order to apply Black-box Performance Models.