Logo des Repositoriums

Auflistung nach Autor:in "Stier, Christian"

1 - 3 von 3
  • Konferenzbeitrag
    Considering Transient Effects of Self-Adaptations in Model-Driven Performance Analyses
    (Software Engineering 2017, 2017) Stier, Christian; Koziolek, Anne
    Self-adaptive systems reconfigure themselves to meet requirements under changing user load. Model-driven performance analyses for self-adaptive systems enable software architects to evaluate whether a self-adaptive system meets requirements under varying user load. It is essential to the efficiency of a self-adaptive system that it adapts its configuration at the right time. The effectiveness of adaptations depends not only on the time when an adaptation decision is made but also on its execution time. The execution of adaptations can cause additional stress on the system. This can further deteriorate system performance. Existing model-driven analyses do not consider these transient effects. We present an approach that enables systematic modeling and analysis of transient effects in software performance analyses. We apply our approach to a horizontally scalable media hosting application. By considering the transient effects of scale-outs we were able to increase prediction accuracy for response times of the applications services. Further experiments demonstrated that our approach enables detection and resolution of design deficiencies of self-adaptive systems.
  • Zeitschriftenartikel
    Improving IaaS Cloud Analyses by Black-Box Resource Demand Modeling
    (Softwaretechnik-Trends Band 35, Heft 3, 2015) Groenda, Henning; Stier, Christian
    In Infrastructure as a Service (IaaS) Cloud scenarios, data center operators require specifications of Virtual Machine (VM) behavior for data center middle- and long-term planning and optimization. The planning is usually supported by simulations. While users can leverage white-box application knowledge, data center operators have to rely on metrics at the level of resource demands provided by virtualization and cloud middleware platforms. Existing simulations for data center planning do not combine both viewpoints and either require white-box knowledge or focus on short-term predictions using statistical estimators. Our approach allows modeling varying resource demand of black-box VMs based on the Descartes Load Intensity Model (DLIM). The black-box VM models are integrated in the SimuLizar performance simulator complementing the existing grey- and white-box models in order to improve reasoning on (de-) consolidation decisions.
  • Zeitschriftenartikel
    Modeling IaaS Usage Patterns for the Analysis of Cloud Optimization Policies
    (Softwaretechnik-Trends Band 36, Heft 4, 2016) Krach, Sebastian D.; Stier, Christian; Tsitsipas, Athanasios
    Infrastructure as a Service (IaaS) operators need to balance multiple adversarial goals, such as data center performance and energy efficiency. Automated resource management policies implemented in IaaS Cloud middleware allow the operators to automate trade-off decisions. Simulation-based analyses are viable means to validate that the utilized policies achieve the goals of the operator. For an IaaS operator to perform meaningful analyses, changes in the workload mix of active VMs need to be considered. Current Cloud simulation approaches neglect the influence of VM submissions and terminations, or require the workload to be specified with little abstraction. In this paper, we present a unified approach for modeling IaaS workloads. Our workload model describes the IaaS workload as a sequence of time-triggered, eventdriven external influences. We implement our model as an extension to Palladio and SimuLizar. Finally, we illustrate how historical real-world measurements are leveraged to evaluate resource management policies.