Auflistung nach Autor:in "Barnert, Maximilian"
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- ZeitschriftenartikelConverting Traces of In-Memory Database Systems to OPEN.XTRACE on the Example of SAP HANA(Softwaretechnik-Trends Band 37, Heft 3, 2017) Barnert, Maximilian; Streitz, Adrian; Kienegger,Harald; Krcmar, HelmutThe shift of data-intensive application logic to inmemory Database Management Systems increases their importance for the overall performance of the software system. The performance of a processed query on a Database Management System is influenced by the utilized query execution plan, while traces capture the runtime behavior of the processed execution plan. However, the use of proprietary trace formats limits the usability within Application Performance Management tools and Software Performance Engineering approaches. OPEN.XTRACE is an open format to exchange execution traces, but its current data model does not support the integration of internal Database Management System operations. In this paper, we propose a modification to OPEN.XTRACE that enables a common representation of a query execution trace. In addition, we convert traces of the state-of-the-art in-memory Database Management System SAP HANA into this format.
- KonferenzbeitragPredicting Scaling Efficiency of Distributed Stream Processing Systems via Task Level Performance Simulation(Softwaretechnik-Trends Band 43, Heft 1, 2023) Rank, Johannes; Barnert, Maximilian; Hein, Andreas; Krcmar, HelmutStream processing systems (SPS) are a special class of Big Data systems that firms employ in (near) real time business scenarios. They ensure low-latency processing through a high degree of parallelization and elasticity. However, firms often do not know which scaling direction: horizontally, vertically, or mixed, is the best strategy in terms of CPU performance to scale those systems. Especially in cloud deployments with a pay-per-use model and cluster sizes that can span dozens of cores and machines, firms would profit from more accurate measurement-based approaches. In this paper, we show how to predict the CPU consumption of Apache Flink for different scaling scenarios using the Palladio Component Model. Our approach models the individual streaming tasks that make up the application and parametrizes it with fine grained CPU metrics obtained by combining BPF pro filing and querying the CPU’s performance measurement unit. Through this “task-level model approach”, we can achieve highly accurate predictions, despite using a simple model and only requiring a few mea surements for parametrization. Our experiment also shows that we achieve more accurate results than an alternative approach based on regression analysis.
- KonferenzbeitragSupporting 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, HelmutThe 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.
- KonferenzbeitragTowards Model-based Performance Predictions of SAP Enterprise Applications(Softwaretechnik-Trends Band 39, Heft 3, 2019) Streitz, Adrian; Barnert, Maximilian; Rank, Johannes; Kienegger, Harald; Krcmar, HelmutHigh-performing Enterprise Applications are the basis for efficient running business processes. In order to evaluate software performance, traditional methods refer to complex test scenarios following the development phase and neglect that problems are easier fixable when discovered early. This paper tackles the problem of late performance evaluations and presents a conceptual approach that enables response time predictions for SAP Enterprise Applications during the development phase. We introduce a performance model generator that transforms ABAP source code into Palladio Component Model instances by using Abstract Syntax Trees which allows to conduct early performance simulations. Our approach supports conditional and probabilistic control flows to improve prediction accuracy. Based on subsequent performance simulations, we predict response time of applications and their underlying processing systems.
- KonferenzbeitragUsing OPEN.xtrace and Architecture-Level Models to Predict Workload Performance on In-Memory Database Systems(Softwaretechnik-Trends Band 39, Heft 4, 2019) Barnert, Maximilian; Streitz, Adrian; Rank, Johannes; Kienegger, Harald; Krcmar, HelmutIn-Memory Database Systems (IMDB) come into operation on highly dynamic on-premise and cloud environments. Existing approaches use classical modeling notations such as queuing network models (QN) to reflect performance on IMDB. Changes to workload or hardware come along with a recreation of entire models. At the same time, new paradigms for IMDB increase parallelism within database workload, which intensifies the effort to create and parameterize models. To simplify and reduce the effort for researchers and practitioners to model workload performance on IMDB, we propose the use of architecture level performance models and present a model creation process, which transforms database traces of SAP HANA to the Palladio Component Model (PCM). We evaluate our approach based on experiments using analytical workload. We receive prediction errors for response time and throughput below 4 %.