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Auflistung nach Schlagwort "Palladio Component Model"

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  • Zeitschriftenartikel
    Modeling Big Data Systems by Extending the Palladio Component Model
    (Softwaretechnik-Trends Band 35, Heft 3, 2015) Kroß, Johannes; Brunnert, Andreas; Krcmar, Helmut
    The growing availability of big data has induced new storing and processing techniques implemented in big data systems such as Apache Hadoop or Apache Spark. With increased implementations of these systems in organizations, simultaneously, the requirements regarding performance qualities such as response time, throughput, and resource utilization increase to create added value. Guaranteeing these performance requirements as well as efficiently planning needed capacities in advance is an enormous challenge. Performance models such as the Palladio component model (PCM) allow for addressing such problems. Therefore, we propose a metamodel extension for PCM to be able to model typical characteristics of big data systems. The extension consists of two parts. First, the meta-model is extended to support parallel computing by forking an operation multiple times on a computer cluster as intended by the single instruction, multiple data (SIMD) architecture. Second, modeling of computer clusters is integrated into the meta-model so operations can be properly scheduled on contained computing nodes.
  • Konferenzbeitrag
    Supporting Backward Transitions within Markov Chains when Modeling Complex User Behavior in the Palladio Component Model
    (Softwaretechnik-Trends Band 40, Heft 3, 2020) Barnert, Maximilian; Krcmar, Helmut
    The specification of complex user behavior as accurate as possible is required in order to evaluate performance characteristics for application systems. Approaches exist to model probabilistic aspects within user behavior for session-based application systems using Markov chains. To integrate these approach into performance prediction activities, the authors transform the workload specifications of WESSBAS into performance model instances of the Palladio Component Model (PCM). This paper presents our approach to enable backward transitions within Markov chains using available elements of the PCM meta-model. By extending the existing approach, further complexity within workload for application systems is supported during performance modeling.