PRISMA: Efficient Algorithms and Methods for Online Extraction of Performance Models in Virtualized Environments

• Applicant: Professor Dr.-Ing. Samuel Kounev
• Subject Area: Software Engineering and Programming Languages
• Term: from 2015 to 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 251959028

Modern software systems are becoming increasingly complex and dynamic. Systems typically have a layered architecture which includes application components, middleware platform(s), virtual machine(s), hypervisor, and physical hardware. Each layer influences the system performance, however, it is a major challenge to separate, isolate and quantify the performance influences of each layer while explicitly taking into account configuration and deployment parameters which can change dynamically during operation. Over the past five years, layered architecture-level performance models have become increasingly popular as a powerful tool for run-time performance management, however, such models are usually costly to build manually and require extensive experimental analysis in a controlled environment. The aim of the PRISMA project is to develop novel methods for automatic extraction of architecture-level performance models of virtualization platforms and their hosted applications during system operation. The project will provide a novel reference architecture for virtualization platforms and virtual appliances with integrated model extraction capabilities. The extraction will be based solely on generic model skeletons (composable model building-blocks) integrated into the platforms at the virtualization and middleware level, and monitoring data collected at system run-time, without assuming availability of source code or possibility to conduct static code analysis. The extracted performance models will capture the performance-relevant aspects of the hosted applications and their execution environment including the virtualization platform itself. Thus, extensive and costly experimental analysis to build performance models for capacity management will no longer be necessary. The developed methods will facilitate the creation of performance models and will lay the foundation for proactive performance and resource management by means of the models. By automating and integrating online model extraction, refinement and maintenance as features provided by the virtualization platform, PRISMA will set the basis for a breakthrough in the practical use of predictive models in real-life systems. The adoption of model-based run-time management techniques promises to significantly improve the efficiency of modern virtualized service infrastructures by avoiding to over-provision system resources without having to sacrifice application performance guarantees.

• DFG Programme: Research Grants