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Johannes Grohmann
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2020 – today
- 2022
- [b1]Johannes Grohmann:
Model Learning for Performance Prediction of Cloud-native Microservice Applications. Julius Maximilians University Würzburg, Germany, 2022 - [j7]Simon Eismann
, Joel Scheuner
The State of Serverless Applications: Collection, Characterization, and Community Consensus. IEEE Trans. Software Eng. 48(10): 4152-4166 (2022) - [c39]Stefan Herrnleben, Johannes Grohmann, Veronika Lesch, Thomas Prantl, Florian Metzger, Tobias Hoßfeld
Investigating the Predictability of QoS Metrics in Cellular Networks. IWQoS 2022: 1-10 - [c38]Mark Leznik
Same, Same, but Dissimilar: Exploring Measurements for Workload Time-series Similarity. ICPE 2022: 89-96 - [c37]Martin Straesser
Why Is It Not Solved Yet?: Challenges for Production-Ready Autoscaling. ICPE 2022: 105-115 - 2021
- [j6]Simon Eismann
Serverless Applications: Why, When, and How? IEEE Softw. 38(1): 32-39 (2021) - [j5]Johannes Grohmann, Simon Eismann, André Bauer
SARDE: A Framework for Continuous and Self-Adaptive Resource Demand Estimation. ACM Trans. Auton. Adapt. Syst. 15(2): 6:1-6:31 (2021) - [c36]Marwin Züfle, Joachim Agne, Johannes Grohmann, Ibrahim Dörtoluk, Samuel Kounev:
A Predictive Maintenance Methodology: Predicting the Time-to-Failure of Machines in Industry 4.0. INDIN 2021: 1-8 - [c35]Simon Eismann, Long Bui, Johannes Grohmann, Cristina L. Abad
Sizeless: predicting the optimal size of serverless functions. Middleware 2021: 248-259 - [c34]Stefan Herrnleben
ComBench: A Benchmarking Framework for Publish/Subscribe Communication Protocols Under Network Limitations. VALUETOOLS 2021: 72-92 - [c33]Johannes Grohmann, Martin Straesser
SuanMing: Explainable Prediction of Performance Degradations in Microservice Applications. ICPE 2021: 165-176 - [c32]Jörg Domaschka, Mark Leznik
Buzzy: Towards Realistic DBMS Benchmarking via Tailored, Representative, Synthetic Workloads: Vision Paper. ICPE (Companion) 2021: 175-178 - [c31]André Bauer
Libra: A Benchmark for Time Series Forecasting Methods. ICPE 2021: 189-200 - 2020
- [j4]Johannes Grohmann
A Taxonomy of Techniques for SLO Failure Prediction in Software Systems. Comput. 9(1): 10 (2020) - [c30]Mirko D'Angelo, Sona Ghahremani, Simos Gerasimou, Johannes Grohmann, Ingrid Nunes, Sven Tomforde, Evangelos Pournaras:
Learning to Learn in Collective Adaptive Systems: Mining Design Patterns for Data-driven Reasoning. ACSOS Companion 2020: 121-126 - [c29]Sonya Voneva, Manar Mazkatli
Optimizing Parametric Dependencies for Incremental Performance Model Extraction. ECSA Companion 2020: 228-240 - [c28]Manar Mazkatli
Incremental Calibration of Architectural Performance Models with Parametric Dependencies. ICSA 2020: 23-34 - [c27]Johannes Grohmann, Daniel Seybold, Simon Eismann, Mark Leznik
Baloo: Measuring and Modeling the Performance Configurations of Distributed DBMS. MASCOTS 2020: 1-8 - [c26]Marwin Züfle, Christian Krupitzer, Florian Erhard, Johannes Grohmann, Samuel Kounev:
To Fail or Not to Fail: Predicting Hard Disk Drive Failure Time Windows. MMB 2020: 19-36 - [c25]Stefan Herrnleben, Piotr Rygielski, Johannes Grohmann, Simon Eismann, Tobias Hoßfeld
Model-Based Performance Predictions for SDN-Based Networks: A Case Study. MMB 2020: 82-98 - [c24]Stefan Herrnleben
A Simulation-Based Optimization Framework for Online Adaptation of Networks. SimuTools (1) 2020: 513-532 - [c23]Stefan Herrnleben
An IoT Network Emulator for Analyzing the Influence of Varying Network Quality. SimuTools (2) 2020: 580-599 - [c22]André Bauer
An Automated Forecasting Framework based on Method Recommendation for Seasonal Time Series. ICPE 2020: 48-55 - [c21]Simon Eismann, Johannes Grohmann, Erwin Van Eyk, Nikolas Herbst, Samuel Kounev:
Predicting the Costs of Serverless Workflows. ICPE 2020: 265-276 - [i6]Manar Mazkatli, David Monschein, Johannes Grohmann, Anne Koziolek:
Incremental Calibration of Architectural Performance Models with Parametric Dependencies. CoRR abs/2006.16953 (2020) - [i5]Mirko D'Angelo, Sona Ghahremani, Simos Gerasimou, Johannes Grohmann, Ingrid Nunes, Sven Tomforde, Evangelos Pournaras:
Learning to Learn in Collective Adaptive Systems: Mining Design Patterns for Data-driven Reasoning. CoRR abs/2008.03995 (2020) - [i4]Simon Eismann, Joel Scheuner, Erwin Van Eyk, Maximilian Schwinger, Johannes Grohmann, Nikolas Herbst, Cristina L. Abad, Alexandru Iosup:
A Review of Serverless Use Cases and their Characteristics. CoRR abs/2008.11110 (2020) - [i3]Simon Eismann, Joel Scheuner, Erwin Van Eyk, Maximilian Schwinger, Johannes Grohmann, Cristina L. Abad, Alexandru Iosup:
Serverless Applications: Why, When, and How? CoRR abs/2009.08173 (2020) - [i2]Simon Eismann, Long Bui, Johannes Grohmann, Cristina L. Abad, Nikolas Herbst, Samuel Kounev:
Sizeless: Predicting the optimal size of serverless functions. CoRR abs/2010.15162 (2020)
2010 – 2019
- 2019
- [j3]Erwin Van Eyk, Alexandru Iosup
The SPEC-RG Reference Architecture for FaaS: From Microservices and Containers to Serverless Platforms. IEEE Internet Comput. 23(6): 7-18 (2019) - [j2]Simon Spinner, Johannes Grohmann, Simon Eismann, Samuel Kounev:
Online model learning for self-aware computing infrastructures. J. Syst. Softw. 147: 1-16 (2019) - [j1]Johannes Grohmann, Simon Eismann, Samuel Kounev:
On Learning Parametric Dependencies from Monitoring Data. Softwaretechnik-Trends 39(4): 14-16 (2019) - [c20]Simon Eismann, Johannes Grohmann, Jürgen Walter, Jóakim von Kistowski, Samuel Kounev:
Integrating Statistical Response Time Models in Architectural Performance Models. ICSA 2019: 71-80 - [c19]Mirko D'Angelo, Simos Gerasimou, Sona Ghahremani, Johannes Grohmann, Ingrid Nunes, Evangelos Pournaras, Sven Tomforde:
On learning in collective self-adaptive systems: state of practice and a 3D framework. SEAMS@ICSE 2019: 13-24 - [c18]Johannes Grohmann, Simon Eismann, Sven Elflein, Jóakim von Kistowski, Samuel Kounev, Manar Mazkatli
Detecting Parametric Dependencies for Performance Models Using Feature Selection Techniques. MASCOTS 2019: 309-322 - [c17]Johannes Grohmann, Patrick K. Nicholson, Jesús Omana Iglesias, Samuel Kounev, Diego Lugones:
Monitorless: Predicting Performance Degradation in Cloud Applications with Machine Learning. Middleware 2019: 149-162 - [c16]André Bauer
Systematic Search for Optimal Resource Configurations of Distributed Applications. FAS*W@SASO/ICAC 2019: 120-125 - [c15]Johannes Grohmann, Simon Eismann, André Bauer
Utilizing Clustering to Optimize Resource Demand Estimation Approaches. FAS*W@SASO/ICAC 2019: 134-139 - [c14]Simon Eismann, Jóakim von Kistowski, Johannes Grohmann, André Bauer, Norbert Schmitt, Samuel Kounev:
TeaStore - A Micro-Service Reference Application. FAS*W@SASO/ICAC 2019: 263-264 - [c13]Jóakim von Kistowski, Simon Eismann, Norbert Schmitt, André Bauer, Johannes Grohmann, Samuel Kounev:
TeaStore: A Micro-Service Reference Application for Benchmarking, Modeling and Resource Management Research. SE/SWM 2019: 99-100 - [c12]Cor-Paul Bezemer
How is Performance Addressed in DevOps? ICPE 2019: 45-50 - [c11]Jóakim von Kistowski, Simon Eismann, Johannes Grohmann, Norbert Schmitt, André Bauer, Samuel Kounev:
TeaStore - A Micro-Service Reference Application for Performance Engineers. ICPE Companion 2019: 47-48 - [c10]Jóakim von Kistowski, Johannes Grohmann, Norbert Schmitt, Samuel Kounev:
Predicting Server Power Consumption from Standard Rating Results. ICPE 2019: 301-312 - 2018
- [c9]Johannes Grohmann, Nikolas Herbst, Simon Spinner, Samuel Kounev:
Using Machine Learning for Recommending Service Demand Estimation Approaches - Position Paper. CLOSER 2018: 473-480 - [c8]Johannes Grohmann, Simon Eismann, Samuel Kounev:
The Vision of Self-Aware Performance Models. ICSA Companion 2018: 60-63 - [c7]Jóakim von Kistowski, Simon Eismann, Norbert Schmitt, André Bauer
TeaStore: A Micro-Service Reference Application for Benchmarking, Modeling and Resource Management Research. MASCOTS 2018: 223-236 - [c6]André Bauer, Johannes Grohmann, Nikolas Herbst, Samuel Kounev:
On the Value of Service Demand Estimation for Auto-scaling. MMB 2018: 142-156 - [c5]Vanessa Ackermann, Johannes Grohmann, Simon Eismann, Samuel Kounev:
Blackbox Learning of Parametric Dependencies for Performance Models. MoDELS (Workshops) 2018: 78-86 - [c4]Simon Eismann, Jóakim von Kistowski, Johannes Grohmann, André Bauer, Norbert Schmitt, Nikolas Herbst, Samuel Kounev:
TeaStore: A Micro-Service Reference Application for Cloud Researchers. UCC Companion 2018: 11-12 - [c3]Erwin Van Eyk, Alexandru Iosup
A SPEC RG Cloud Group's Vision on the Performance Challenges of FaaS Cloud Architectures. ICPE Companion 2018: 21-24 - [c2]Jürgen Walter, Simon Eismann, Johannes Grohmann, Dusan Okanovic, Samuel Kounev:
Tools for Declarative Performance Engineering. ICPE Companion 2018: 53-56 - [i1]Cor-Paul Bezemer, Simon Eismann, Vincenzo Ferme, Johannes Grohmann, Robert Heinrich, Pooyan Jamshidi, Weiyi Shang, André van Hoorn, Mónica Villavicencio, Jürgen Walter, Felix Willnecker:
How is Performance Addressed in DevOps? A Survey on Industrial Practices. CoRR abs/1808.06915 (2018) - 2017
- [c1]Johannes Grohmann, Nikolas Herbst, Simon Spinner, Samuel Kounev:
Self-Tuning Resource Demand Estimation. ICAC 2017: 21-26
Coauthor Index
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