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Auflistung nach Autor:in "Brosig, Fabian"

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  • Konferenzbeitrag
    Analysis of the trade-offs in different modeling approaches for performance prediction of software systems
    (Software Engineering 2016, 2016) Kounev, Samuel; Brosig, Fabian; Meier, Philipp; Becker, Steffen; Koziolek, Anne; Koziolek, Heiko; Rygielski, Piotr
    A number of performance modeling approaches for predicting the performance of modern software systems and IT infrastructures exist in the literature. Different approaches differ in their modeling expressiveness and accuracy, on the one hand, and their modeling overhead and costs, on the other hand. Considering a representative set of established approaches, we analyze the semantic gaps between them as well as the trade-offs in using them; we further provide guidelines for selecting the right approach suitable for a given scenario.
  • Konferenzbeitrag
    The Descartes modeling language for self-aware performance and resource management
    (Software-engineering and management 2015, 2015) Kounev, Samuel; Brosig, Fabian; Huber, Nikolaus
    The Descartes Modeling Language (DML) is a novel architecture-level language for modeling performance and resource management related aspects of modern dynamic software systems and IT infrastructures. Technically, DML is comprised of several sub-languages, each of them specified using OMG's Meta-Object Facility (MOF) and referred to as meta-model in OMG's terminology. The various sublanguages can be used both in offline and online settings for application scenarios like system sizing, capacity planning and trade-off analysis, as well as for self-aware resource management during operation. Modern software systems have increasingly distributed architectures composed of looselycoupled services that are typically deployed on virtualized infrastructures. Such system architectures provide increased flexibility by abstracting from the physical infrastructure, which can be leveraged to improve system efficiency. However, these benefits come at the cost of higher system complexity and dynamics. The inherent semantic gap between application-level metrics, on the one hand, and resource allocations at the physical and virtual layers, on the other hand, significantly increase the complexity of managing endto-end application performance. To address this challenge, techniques for online performance prediction are needed. Such techniques should make it possible to continuously predict at runtime: a) changes in the application workloads [HHKA14], b) the effect of such changes on the system performance, and c) the expected impact of system adaptation actions [BHK14]. Online performance prediction can be leveraged to design systems that proactively adapt to changing operating conditions, thus enabling what we refer to as self-aware1 performance and resource management [KBH14, HvHK+14, KBHR10]. Existing approaches to performance and resource management in the research community are mostly based on coarsegrained performance models that typically abstract systems and applications at a high level, e.g., [JHJ+10, ZCS07, CAAS07]. Such models do not explicitly model the software architecture and execution environment, distinguishing performance-relevant behavior at the virtualization level vs. at the level of applications hosted inside the running VMs. Thus, their online prediction capabilities are limited and do not support complex scenarios such as, for example, predicting how changes in application workloads propagate through the 1Self-awareness is understood as adopted for Dagstuhl Seminar 15041 (http://www.dagstuhl.de/15041) 33 layers and tiers of the system architecture down to the physical resource layer, or predict- ing the effect on the response times of different services, if a VM in a given application tier is to be replicated or migrated to another host, possibly of a different type. To enable online performance prediction in scenarios such as the above, architecturelevel modeling techniques are needed, specifically designed for use in online settings. We present a new architecture-level language, called Descartes Modeling Language (DML)2, which provides appropriate modeling abstractions to describe the resource landscape, the application architecture, the adaptation space, and the adaptation processes of a software system and its IT infrastructure [BHK14, HvHK+14]. We present an overview of the different constituent parts of DML and describe how they can be leveraged to enable online performance prediction and proactive model-based system adaptation. The complete DML specification is available as a technical report [KBH14]. A set of related tools and libraries are available from the DML website at http://descartes.tools/dml. Finally, we present some exemplary results from an industrial case study showing the applicability of our approach in a real-life setting [HvHK+14]. References [BHK14] F. Brosig, N. Huber, and S. Kounev. Architecture-Level Software Performance Ab- stractions for Online Performance Prediction. Elsevier Science of Computer Programming Journal (SciCo), Vol. 90, Part B:71-92, 2014. [CAAS07] I. Cunha, J Almeida, V. Almeida, and M. Santos. Self-Adaptive Capacity Management for Multi-Tier Virtualized Environments. In IFIP/IEEE Int. Symposium on Integrated Network Management, pages 129-138, 2007. [HHKA14] N. Herbst, N. Huber, S. Kounev, and E. Amrehn. Self-Adaptive Workload Classification and Forecasting for Proactive Resource Provisioning. Concurrency and Computation - Practice and Experience, John Wiley and Sons, $26(12)$:2053-2078, 2014. [HvHK+14] N. Huber, A. van Hoorn, A. Koziolek, F. Brosig, and S. Kounev. Modeling Run-Time Adaptation at the System Architecture Level in Dynamic Service-Oriented Environments. Service Oriented Computing and Applications Journal, $8(1)$:73-89, 2014. [JHJ+10] Gueyoung Jung, M.A. Hiltunen, K.R. Joshi, R.D. Schlichting, and C. Pu. Mistral: Dynamically Managing Power, Performance, and Adaptation Cost in Cloud Infrastructures. In IEEE Int. Conf. on Distributed Computing Systems, pages 62 -73, 2010. [KBH14] S. Kounev, F. Brosig, and N. Huber. The Descartes Modeling Language. Technical report, Department of Computer Science, University of Wuerzburg, October 2014.
  • Konferenzbeitrag
    Model-Based Self-Aware Performance and Resource Management Using the Descartes Modeling Language
    (Software Engineering 2017, 2017) Kounev, Samuel; Huber, Nikolaus; Brosig, Fabian; Spinner, Simon; Bähr, Manuel
    We present the results of our recent work published in [Hu17] and summarized in [Ko16]. We introduce a holistic model-based approach for self-aware performance and resource management of modern IT systems and infrastructures. Based on a novel online performance prediction process, we implement a model-based control loop for proactive system adaptation. We evaluate our 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 of modern IT systems and infrastructures.
  • Konferenzbeitrag
    Online performance prediction with architecture-level performance models
    (Software Engineering 2011 – Workshopband, 2011) Brosig, Fabian
    Today's enterprise systems based on increasingly complex software architectures often exhibit poor performance and resource efficiency thus having high operating costs. This is due to the inability to predict at run-time the effect of changes in the system environment and adapt the system accordingly. We propose a new performance modeling approach that allows the prediction of performance and system resource utilization online during system operation. We use architecture-level performance models that capture the performance-relevant information of the software architecture, deployment, execution environment and workload. The models will be automatically maintained during operation. To derive performance predictions, we propose a tailorable model solving approach to provide flexibility in view of prediction accuracy and analysis overhead.