Modern IT systems have increasingly distributed and dynamic architectures providing flexibility to adapt to changes in the environment and thus enabling higher resource efficiency. However, these benefits come at the cost of higher system complexity and dynamics. Thus, engineering systems that manage their end-to-end application performance and resource efficiency in an autonomic manner is a challenge. In this article, we present a holistic model-based approach for self-aware performance and resource management leveraging the Descartes Modeling Language (DML), an architecture-level modeling language for online performance and resource management. We propose a novel online performance prediction process that dynamically tailors the model solving depending on the requirements regarding accuracy and overhead. Using these prediction capabilities, we implement a generic model-based control loop for proactive system adaptation. We evaluate our model-based approach in the context of two representative case studies showing that with the proposed methods, significant resource efficiency gains can be achieved while maintaining performance requirements. These results represent the first end-to-end validation of our approach, demonstrating its potential for self-aware performance and resource management in the context of modern IT systems and infrastructures.

During the last decade, researchers have proposed a number of model transformations enabling performance predictions. These transformations map performance-annotated software architecture models into stochastic models solved by analytical means or by simulation. However, so far, a detailed quantitative evaluation of the accuracy and efficiency of different transformations is missing, making it hard to select an adequate transformation for a given context. This paper provides an in-depth comparison and quantitative evaluation of representative model transformations to, e.g., queueing petri nets and layered queueing networks. The semantic gaps between typical source model abstractions and the different analysis techniques are revealed. The accuracy and efficiency of each transformation are evaluated by considering four case studies representing systems of different size and complexity. The presented results and insights gained from the evaluation help software architects and performance engineers to select the appropriate transformation for a given context, thus significantly improving the usability of model transformations for performance prediction.

Over the past few decades, many performance modeling formalisms and prediction techniques for software architectures have been developed in the performance engineering community. However, using a performance model to predict the performance of a software system normally requires extensive experience with the respective modeling formalism and involves a number of complex and time consuming manual steps. In this paper, we propose a generic declarative interface to performance prediction techniques to simplify and automate the process of using architecture-level software performance models for performance analysis. The proposed Descartes Query Language (DQL) is a language to express the demanded performance metrics for prediction as well as the goals and constraints of the specific prediction scenario. It reduces the manual effort and learning curve in working with performance models by a unified interface independent of the employed modeling formalism. We evaluate the applicability and benefits of the proposed approach in the context of several representative case studies.

Performance management and performance prediction of services deployed in virtualized environments is a challenging task. On the one hand, the virtualization layer makes the estimation of performance model parameters difficult and inaccurate. On the other hand, it is difficult to model the hyper visor scheduler in a representative and practically feasible manner. In this paper, we describe how to obtain relevant parameters, such as the virtualization overhead, depending on the amount and type of available monitoring data. We adapt classical queueing-theory-based modeling techniques to make them usable for different configurations of virtualized environments. We provide answers how to include the virtualization overhead into queueing network models, and how to take the contention between different VMs into account. Finally, we evaluate our approach in representative scenarios based on the SPECjEnterprise2010 standard benchmark and XenServer 5.5, showing significant improvements in the prediction accuracy and discussing further open issues for performance prediction in virtualized environments.

Modern enterprise applications have to satisfy increasingly stringent Quality-of-Service requirements. To ensure that a system meets its performance requirements, the ability to predict its performance under different configurations and workloads is essential. Architecture-level performance models describe performance-relevant aspects of software architectures and execution environments allowing to evaluate different usage profiles as well as system deployment and configuration options. However, building performance models manually requires a lot of time and effort. In this paper, we present a novel automated method for the extraction of architecture-level performance models of distributed component-based systems, based on monitoring data collected at run-time. The method is validated in a case study with the industry-standard SPECjEnterprise2010 Enterprise Java benchmark, a representative software system executed in a realistic environment. The obtained performance predictions match the measurements on the real system within an error margin of mostly 10-20 percent.