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Auflistung nach Autor:in "Krach, Sebastian D."

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  • Zeitschriftenartikel
    Leveraging State to Facilitate Separation of Concerns in Reuse-oriented Performance Models
    (Softwaretechnik-Trends Band 37, Heft 3, 2017) Werle, Dominik; Seifermann, Stephan; Krach, Sebastian D.
    Each of the five dedicated roles of the Palladio process considers one or more concerns that form a performance prediction model, altogether. Modeling systems that vary their behavior based on a request history, however, requires to break role separation and create dependencies between concerns, thus reducing the reusability of components. Model elements that allow expressing such behavior while maintaining role separation do not exist. We propose a model extension that allows expressing behavior statefully and a transformation to a basic stateless Palladio model. This allows to maintain the role separation and thereby the reusability of components without the need for changes of existing analyses.
  • 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.