On the Use of Queueing Petri Nets for Modeling and Performance Analysis of Distributed Systems

Predictive performance models are used increasingly throughout the phases of the software engineering lifecycle of distributed systems. However, as systems grow in size and complex-ity, building models that accurately capture the different aspects of their behavior becomes a more and more challenging task. The challenge stems from the limited model expressivenes

An industrial case study of performance and cost design space exploration

Determining the trade-off between performance and costs of a distributed software system is important as it enables fulfilling performance requirements in a cost-efficient way. The large amount of design alternatives for such systems of-ten leads software architects to select a suboptimal solution, which may either waste resources or cannot cope with fu-ture workloads. Recently, several approaches have appeared to assist software architects with this design task. In this paper, we present a case study applying one of these ap-proaches, i.e. PerOpteryx, to explore the design space of an existing industrial distributed software system from ABB. To facilitate the design exploration, we created a highly de-tailed performance and cost model, which was instrumental in determining a cost-efficient architecture solution using an evolutionary algorithm. The case study demonstrates the capabilities of various modern performance modeling tools and a design space exploration tool in an industrial setting, provides lessons learned, and helps other software architects in solving similar problems

Automated Experiments for Deriving Performance-relevant Properties of Software Execution Environments

The execution environment can play a crucial role when analyzing the performance of a software system. However, detecting execution environment properties and integrating such properties into performance analyses is a manual, error-prone task. In this thesis, a novel approach for detecting performance-relevant properties of the software execution environment is presented. These properties are automatically detected using predefined experiments and integrated into performance prediction tools

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

Autonomic Performance-Aware Resource Management in Dynamic IT Service Infrastructures

Model-based techniques are a powerful approach to engineering autonomic and self-adaptive systems. This thesis presents a model-based approach for proactive and autonomic performance-aware resource management in dynamic IT infrastructures. Core of the approach is an architecture-level modeling language to describe performance and resource management related aspects in such environments. With this approach, it is possible to autonomically find suitable system configurations at the model level

Representing, reasoning and answering questions about biological pathways - various applications

Biological organisms are composed of numerous interconnected biochemical processes. Diseases occur when normal functionality of these processes is disrupted. Thus, understanding these biochemical processes and their interrelationships is a primary task in biomedical research and a prerequisite for diagnosing diseases, and drug development. Scientists studying these processes have identified various pathways responsible for drug metabolism, and signal transduction, etc. Newer techniques and speed improvements have resulted in deeper knowledge about these pathways, resulting in refined models that tend to be large and complex, making it difficult for a person to remember all aspects of it. Thus, computer models are needed to analyze them. We want to build such a system that allows modeling of biological systems and pathways in such a way that we can answer questions about them. Many existing models focus on structural and/or factoid questions, using surface-level knowledge that does not require understanding the underlying model. We believe these are not the kind of questions that a biologist may ask someone to test their understanding of the biological processes. We want our system to answer the kind of questions a biologist may ask. Such questions appear in early college level text books. Thus the main goal of our thesis is to develop a system that allows us to encode knowledge about biological pathways and answer such questions about them demonstrating understanding of the pathway. To that end, we develop a language that will allow posing such questions and illustrate the utility of our framework with various applications in the biological domain. We use some existing tools with modifications to accomplish our goal. Finally, we apply our system to real world applications by extracting pathway knowledge from text and answering questions related to drug development.Comment: thesi