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Auflistung nach Autor:in "Willnecker, Felix"

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  • Zeitschriftenartikel
    Full-Stack Performance Model Evaluation using Probabilistic Garbage Collection Simulation
    (Softwaretechnik-Trends Band 35, Heft 3, 2015) Willnecker, Felix; Koch-Kemper, Bernhard
    Performance models can represent the performance relevant aspects of an enterprise application. Corresponding simulation engines use such models for simulating performance metrics (e.g., response times, resource utilization, throughput) and allow for performance evaluations without load testing the actual system. Creating such models manually often outweighs their benefits. Therefore, recent research created performance model generators, which can generate such models out of Application Performance Management software. However, a full-stack evaluation containing all relevant resources of an enterprise application (Central Processing Unit, memory, network and Hard Disk Drive) has not been conducted to the best of our knowledge. This work closes this gap using a pre-release version of the next generation industry benchmark SPECjEnterpriseNEXT of the Standard Performance Evaluation Corporation as example enterprise application, the Palladio Component Model as performance model and the performance model generator of the RETIT Capacity Manager. Furthermore, this work extends the generated model with a probabilistic garbage collection model to simulate memory allocation and releases more accurately.
  • Konferenzbeitrag
    Model-based Energy Consumption Prediction for Mobile Application
    (Proceedings of the 28th Conference on Environmental Informatics - Informatics for Environmental Protection, Sustainable Development and Risk Management, 2014) Willnecker, Felix; Brunnert, Andreas; Krcmar, Helmut
    Investigating the energy consumption of mobile applications (apps) is becoming a growing software engineering challenge due to the limited battery lifetime of mobile devices. Energy consumption is defined as the power demand integrated over time. Profiling the power demand of an app is a time consuming activity and the results are only valid for the target hardware used during the measurements. The energy consumption is influenced by the resource demands of an app, the hardware on which the app is running, and its workload. This work adapts resource profiles for enterprise applications to predict the energy consumption of mobile apps without the need to own a physical device. Resource profiles are models that represent all aspects influencing the energy consumption of an app. They can be used to predict the energy consumption for different hardware devices and evaluate the overall efficiency of an app. Moreover, the workload can be changed so that the impact of different usage patterns can be investigated. These capabilities lay the foundation for a platform-independent way of quantifying the energy consumption of mobile apps
  • Konferenzbeitrag
    Optimization of component allocations in middleware platforms using performance models
    (Software-engineering and management 2015, 2015) Willnecker, Felix
    Distributed enterprise applications are typically implemented as system-ofsystems composed of components and linked via middleware. These systems often utilize corresponding resources far below available capacity. In order to increase resource utilizations the consolidation of components demands several tests on environments comparable to the production system. Performance models can be used to landscape such system architectures and to simulate changes in the component topology or resource environment without harming production systems. Therefore, this work aims at extracting performance models from distributed middleware platforms. Based on these models, an architecture optimizer is built to test different allocation topologies. Subsequently, the optimized model is simulated and the prediction accuracy of architecture changes is evaluated in this work. This allows architects to evaluate component changes and topology variations without a replica of the production system.
  • Zeitschriftenartikel
    SiaaS: Simulation as a Service
    (Softwaretechnik-Trends Band 36, Heft 4, 2016) Willnecker, Felix; Vögele, Christian; Krcmar,Helmut
    One major advantage of performance models over tests using real systems is the ability to simulate design alternatives by simply modifying or exchanging parts of such models. However, the evaluation of numerous design alternatives can be time consuming depending on the number of alternatives and the complexity of the model. To overcome this challenge, this work presents a scalable simulation service for the Palladio Component Model (PCM) workbench based on a headless Eclipse instance, a Java EE application server, packaged in a docker container and run in kubernetes. The simulation service supports parallel simulation runs, multiple PCM instances in the same container and scales out automatically, when resources of one container instance exceed. Simulation jobs are triggered by a platform-independent REST interface and can be re-used by other applications. This allows to simulate a vast amount of model instances in parallel on cloud or on-premise installations.
  • Zeitschriftenartikel
    Towards Predicting Performance of GPU-dependent Applications on the Example of Machine Learning in Enterprise Applications
    (Softwaretechnik-Trends Band 37, Heft 3, 2017) Willnecker, Felix; Krcmar, Helmut
    Algorithms processed by Graphics Processing Units (GPU) became popular recently. Bitcoin mining algorithms, image processing and all types of machine learning are famous examples for that. Infrastructureas-a-Service provider picked up this trend and offer graphics processing power as part of their service portfolio. The performance gains when choosing a GPU implementation can be enormous. Designing and implementing a GPU-depended algorithm has some fundamental differences compared to classical algorithms, but not all algorithmic problems benefit from GPU usage regarding the overall performance and response time. Especially the interaction between Central Processing Unit (CPU) and GPU must be considered as it can become a bottleneck. Predicting and comparing the performance of GPU-depended applications in combination with their corresponding CPUs allows to assist design decisions in modern applications. In this work, we present concepts on how to predict algorithm performance relying on GPU processing and their relationship with the CPU using the Palladio Component Model and the Palladio Bench.