DOS Group at TU Berlin

dos-group

Organization data from Github https://github.com/dos-group

Distributed and Operating Systems group at Technische Universität Berlin

Technische Universität Berlin

https://tu.berlin/en/dos

DOS Group at TU Berlin's repositories

leaf

Simulator for modeling energy consumption in cloud, fog, and edge computing environments 🌱

Language:PythonMIT116 13 1

vessim

A co-simulation testbed for carbon-aware computing systems 🍃

Language:PythonMIT66 8 60

lets-wait-awhile

Simulator and datasets to research on carbon-aware temporal workload shifting.

Language:Jupyter Notebook25 12 1

fedzero

[ACM e-Energy'24] Code and data for "FedZero: Leveraging Renewable Excess Energy in Federated Learning"

Language:PythonMIT23 90

fed_challenge

Superiority of Simplicity: A Lightweight Model for Network Device Workload Prediction

Language:Python12 110

benchspark

An extensible toolset for Spark performance benchmarking

Language:ScalaMIT9 50

hector_iot_testing_framework

A framework, available as a Python API, for doing experiments on real and emulated IoT environments.

Language:Shell6 150

demeter

Language:Jupyter NotebookApache-2.04 9 1

bitflow-process-agent

REST API for managing bitflow-pipeline worker subprocesses

Language:GoGPL-3.03 30

Real-Time-Offloading-Simulator-IIoT

Language:PythonGPL-3.03 90

sizey

Language:Python3 8 1

daedalus

Fork of the original repository, but with small modifications

Language:Mustache200

LLMWorkflowGenerator

Language:PythonMIT2 80

karasu-cloud-configuration-profiling

Prototypical implementation of "Karasu" for collective and efficient cloud configuration profiling

Language:PythonGPL-3.01 120

qualitytime

Language:Python100

expo-demo-server

Language:JavaScriptApache-2.00140

k-Segments

Language:Python090

k-Segments-traces

080

batch-workload-characteristics-analysis

Language:Jupyter Notebook000

CoCoMaMa

Implementaion of the CoCoMaMa and Neural-CoCoMaMa methods, combinatorial contextual volatile multi-armed bandit approaches that leverage similarities between tasks and agents by learning on online feedback. It can handle volatile arms by incorporating agent cards without requiring changes to the internal structures or retraining.

Language:Python000

flora

Code and evaluation for the CCGrid 2025 paper "Flora"

Language:Python090

KSPlus

Language:Python060

llarm

Language:PythonMIT000

pm-data-privacy

Performance model data privacy: Paper evaluation

Language:Python080

py-lotaru

Language:Python01 1

QONNECT

This repository introduces QONNECT, an approach we developed as a test bed orchestrator for the Swarmchestrate project.

Apache-2.0000

spark-autoscaling-evaluation

Data and evaluation scripts for the paper "Experimentally Evaluating the Resource Efficiency of Big Data Autoscaling"

Language:Python080

stream-processing-enrichment-methods

Fork of the original implementation, but with additional data and code

Language:HTML000

sysprog

Language:Shell000

vessim-opt

Code and data for the paper "Optimizing Microgrid Composition for Sustainable Data Centers" @ SC25 Sustainable Supercomputing Workshop

000