Johannes Grohmann

About Johannes Grohmann

Johannes Grohmann, With an exceptional h-index of 15 and a recent h-index of 15 (since 2020), a distinguished researcher at Julius-Maximilians-Universität Würzburg, specializes in the field of Performance Modeling, Machine Learning, Serverless Computing.

His recent articles reflect a diverse array of research interests and contributions to the field:

CAT: A Compound Attachment Tool for the Construction of Composite Chemical Compounds

Investigating the Predictability of QoS Metrics in Cellular Networks

Same, Same, but Dissimilar: Exploring Measurements for Workload Time-series Similarity

Why is it not solved yet? challenges for production-ready autoscaling

Modelling non-local neural information processing in the brain

Model Learning for Performance Prediction of Cloud-native Microservice Applications

Buzzy: Towards Realistic DBMS Benchmarking via Tailored, Representative, Synthetic Workloads: Vision Paper

Suanming: Explainable prediction of performance degradations in microservice applications

Johannes Grohmann Information

University

Position

___

Citations(all)

1163

Citations(since 2020)

1121

Cited By

279

hIndex(all)

15

hIndex(since 2020)

15

i10Index(all)

27

i10Index(since 2020)

25

Email

University Profile Page

Google Scholar

Johannes Grohmann Skills & Research Interests

Performance Modeling

Machine Learning

Serverless Computing

Top articles of Johannes Grohmann

CAT: A Compound Attachment Tool for the Construction of Composite Chemical Compounds

Journal of chemical information and modeling

2022/10/31

Investigating the Predictability of QoS Metrics in Cellular Networks

2022/6/10

Same, Same, but Dissimilar: Exploring Measurements for Workload Time-series Similarity

ICPE

2022

Why is it not solved yet? challenges for production-ready autoscaling

2022/4/9

Modelling non-local neural information processing in the brain

bioRxiv

2022/1/29

Johannes Grohmann
Johannes Grohmann

H-Index: 9

Model Learning for Performance Prediction of Cloud-native Microservice Applications

2022

Buzzy: Towards Realistic DBMS Benchmarking via Tailored, Representative, Synthetic Workloads: Vision Paper

2021/4/19

Suanming: Explainable prediction of performance degradations in microservice applications

2021/4/9

Libra: A benchmark for time series forecasting methods

2021/4/9

Combench: A benchmarking framework for publish/subscribe communication protocols under network limitations

2021

Sizeless: Predicting the optimal size of serverless functions

2021/12/6

The state of serverless applications: Collection, characterization, and community consensus

IEEE Transactions on Software Engineering

2021/9/21

A predictive maintenance methodology: predicting the time-to-failure of machines in industry 4.0

2021/7/21

Marwin Züfle
Marwin Züfle

H-Index: 5

Johannes Grohmann
Johannes Grohmann

H-Index: 9

SARDE: a framework for continuous and self-adaptive resource demand estimation

ACM Transactions on Autonomous and Adaptive Systems (TAAS)

2021/6/9

Baloo: Measuring and modeling the performance configurations of distributed dbms

2020/11/17

Serverless applications: Why, when, and how?

SERVERLESS APPLICATIONS ENGINEERING

2021/1

A Simulation-Based Optimization Framework for Online Adaptation of Networks

2020/8/28

An iot network emulator for analyzing the influence of varying network quality

2020/8/28

A review of serverless use cases and their characteristics

2020/8/25

Learning to learn in collective adaptive systems: mining design patterns for data-driven reasoning

2020/8/17

See List of Professors in Johannes Grohmann University(Julius-Maximilians-Universität Würzburg)

Co-Authors

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