Auflistung nach Autor:in "Hilbrich, Marcus"
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- ZeitschriftenartikelAchieving scalability for job centric monitoring in a distributed infrastructure(PARS: Parallel-Algorithmen, -Rechnerstrukturen und -Systemsoftware: Vol. 29, No. 1, 2012) Hilbrich, Marcus; Müller-Pfefferkorn, RalphJob centric monitoring allows to observe jobs on remote computing resources. It may offer visualisation of recorded monitoring data and helps to find faulty or misbehaving jobs. If installations like grids or clouds are observed monitoring data of many thousands of jobs have to be handled. The challenge of job centric monitoring infrastructures is to store, search and access data collected in huge installations like grids or clouds. We take this challenge with a distributed layer based architecture which provides a uniform view to all monitoring data. The concept of this infrastructure called SLAte and an analysis of the scalability is provided in this paper.
- KonferenzbeitragAchieving scalability for job centric monitoring in a distributed infrastructure(ARCS 2012 Workshops, 2012) Hilbrich, Marcus; Müller-Pfefferkorn, RalphJob centric monitoring allows to observe jobs on remote computing resources. It may offer visualisation of recorded monitoring data and helps to find faulty or misbehaving jobs. If installations like grids or clouds are observed monitoring data of many thousands of jobs have to be handled. The challenge of job centric monitoring infrastructures is to store, search and access data collected in huge installations like grids or clouds. We take this challenge with a distributed layer based architecture which provides a uniform view to all monitoring data. The concept of this infrastructure called SLAte and an analysis of the scalability is provided in this paper.
- ZeitschriftenartikelGeneralisierter Informationszugriff zu verteilten Datenquellen im D-Grid(PARS: Parallel-Algorithmen, -Rechnerstrukturen und -Systemsoftware: Vol. 28, No. 1, 2011) Jäkel, René; Hünich, Denis; Hilbrich, Marcus; Daivandy, Milad; Schuller, Bernd; Harms, PatrickRené Jäkel, Denis Hünich und Marcus Hilbrich Zentrum für Informationsdienste und Hochleistungsrechnen, Technische Universität Dresden Milad Jason Daivandy und Bernd Schuller Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH Patrick Harms Niedersächsische Staats- und Universitätsbibliothek, Georg-August-Universität Göttingen
- TextdokumentIn Microservices We Trust — Do Microservices Solve Resilience Challenges?(Tagungsband des FB-SYS Herbsttreffens 2019, 2019) Hilbrich, MarcusResilience is an open challenge. In this paper we look into microservices – a concept that argues to be resilient. We look into the definition of microservices and argue whether the definition provides the promised advantages regarding to resilience.
- ZeitschriftenartikelIs the PCM Ready for ACTORs and Multicore CPUs? — A Use Case-based Evaluation(Softwaretechnik-Trends Band 37, Heft 3, 2017) Frank, Markus; Staude, Stefan; Hilbrich, MarcusMulticore CPUs have been common for years. However, developing parallel software is still an issue. To ease the development, software developers can use a range of frameworks and approaches, e.g., OpenMP, MPI or ACTOR. These approaches have an enormous impact on the performance of the software. Thus, Software Performance Engineering (SPE) needs to consider the impact of the parallelization approaches to deliver reliable results. In this paper, we evaluate the capability of the Palladio Component Model1 (PCM) based on the use case of a bank transaction example with a realization following the ACTOR approach. We observed that the accuracy of the performance predictions is unsatisfying, the modeling is challenging, and the characteristics of the ACTOR approach cannot be modeled. In future we need to consider additional attributes or properties to enrich the PCM as well to include concepts like active resources, message passing, and automatization concepts.
- WorkshopbeitragOn Managing Large Collections of Scientific Workflows(Modellierung 2024 Satellite Events, 2024) Elfaramawy, Nourhan; Deniz, Fatma; Grunske, Lars; Hilbrich, Marcus; Kehrer, Timo; Lamprecht, Anna-Lena; Mendling, Jan; Weidlich, Matthias
- ZeitschriftenartikelPerformance Prediction for Multicore Environments — An Experiment Report(Softwaretechnik-Trends Band 36, Heft 4, 2016) Frank, Markus; Hilbrich, MarcusMulticore systems are a permanent part of our daily life. Regardless whether we consider nowadays desktop PC’s, notebooks, or smart phones: all devices are running on multicore CPUs. To use such hardware in an efficient way, we need parallel enabled software. But the development of such software is more complex and more error-prone than developing sequential software. To handle the rising complexity, it is necessary to develop software in an engineering way. In such a process, software architects have to plan and analyze software designs on model level. Software architects can use approaches like Palladio to simulate and analyze early phase software designs. However, it is uncertain how Palladio can handle multicore systems. In this paper we evaluate the current state of Palladio regarding multicore awareness based on an experiment. We implemented an easy to parallelize use case, modeled, and simulated it using Palladio. We predicted the performance of an 16 core system with an accuracy of 79 %, but noticed a decreasing accuracy for a rising number of cores. Based on the experiment, we discuss the need to model attributes like memory, memory bandwidth, and caches, which are currently included.
- ZeitschriftenartikelQuantifying Performance and Scalability of the Distributed Monitoring Infrastructure SLAte(PARS-Mitteilungen: Vol. 32, Nr. 1, 2015) Hilbrich, Marcus; Müller-Pfefferkorn, RalphJob-centric monitoring allows to observe the execution of programs and services (so called jobs) on remote and local computing resources. Especially large installations like Grids, Clouds and HPC systems with many thousands of jobs can have large benefits from intelligent visualisations of recorded monitoring data and semi-automatic analyses. The latter can reveal misbehaving jobs or non-optimal job execution and enables future optimisations to establish a more efficient use of the allocated resources. The challenge of job-centric monitoring infrastructures is to store, search and access data collected on huge installations. We take this challenge with a distributed layer-based architecture which provides a uniform view to all monitoring data. The concept of this infrastructure called SLAte, a performance evaluation, and the consequences for scalability are presented in this paper.
- ZeitschriftenartikelSecurity Modeling with Palladio—Different Approaches(Softwaretechnik-Trends Band 36, Heft 4, 2016) Hilbrich, Marcus; Frank, Markus; Lehrig, SebastianSecurity is never perfect, security deals with a lot of uncertainty, and security is complex. Nevertheless, security is one of the non-functional properties, that we, as software architects, have to consider. It is needed to include security in many trade-off decisions (usability, performance, costs, etc. versus security), to compare the security of different architectures, and to check whether legal constraints are meet. Thus it is demanded to include security modeling to approaches like Palladio. In this paper, we describe two approaches to model and analyze security using Palladio. The first approach is an external one and requires to adapt Palladio. The second approach is proposed by us and does not need to modify Palladio. Furthermore, we explain why we needed to develop a new approach based on a use case and its demanded pragmatism for the model.
- ZeitschriftenartikelThe Collaborative Research Center FONDA(Datenbank-Spektrum: Vol. 21, No. 3, 2021) Leser, Ulf; Hilbrich, Marcus; Draxl, Claudia; Eisert, Peter; Grunske, Lars; Hostert, Patrick; Kainmüller, Dagmar; Kao, Odej; Kehr, Birte; Kehrer, Timo; Koch, Christoph; Markl, Volker; Meyerhenke, Henning; Rabl, Tilmann; Reinefeld, Alexander; Reinert, Knut; Ritter, Kerstin; Scheuermann, Björn; Schintke, Florian; Schweikardt, Nicole; Weidlich, MatthiasToday’s scientific data analysis very often requires complex Data Analysis Workflows (DAWs) executed over distributed computational infrastructures, e.g., clusters. Much research effort is devoted to the tuning and performance optimization of specific workflows for specific clusters. However, an arguably even more important problem for accelerating research is the reduction of development, adaptation, and maintenance times of DAWs. We describe the design and setup of the Collaborative Research Center (CRC) 1404 “FONDA -– Foundations of Workflows for Large-Scale Scientific Data Analysis”, in which roughly 50 researchers jointly investigate new technologies, algorithms, and models to increase the portability, adaptability, and dependability of DAWs executed over distributed infrastructures. We describe the motivation behind our project, explain its underlying core concepts, introduce FONDA’s internal structure, and sketch our vision for the future of workflow-based scientific data analysis. We also describe some lessons learned during the “making of” a CRC in Computer Science with strong interdisciplinary components, with the aim to foster similar endeavors.