DOS Group at TU Berlin (dos-group)

DOS Group at TU Berlin

dos-group

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Distributed and Operating Systems group at Technische Universität Berlin

Location:Technische Universität Berlin

Home Page:https://tu.berlin/en/dos

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DOS Group at TU Berlin's repositories

leaf

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

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vessim

A co-simulation testbed for carbon-aware applications and systems 🍃

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lets-wait-awhile

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

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fedzero

Implementation and evaluation of "FedZero: Leveraging Renewable Excess Energy in Federated Learning"

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fed_challenge

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

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cucumber

Renewable-Aware Admission Control for Delay-Tolerant Cloud and Edge Workloads

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hector_iot_testing_framework

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

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bitflow-process-agent

REST API for managing bitflow-pipeline worker subprocesses

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LogLAB

Repository for LogLAB Paper

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c3o

C3O: Collaborative Cluster Configuration Optimization for Distributed Data Processing in Public Clouds

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crispy

Code and evaluation for the Crispy resource allocation assistant

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daedalus

Fork of the original repository, but with small modifications

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ruya

Code and evaluation for Ruya, the memory-aware cluster configuration optimizer

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enel-experiments

Prototypical implementation of "Enel" for runtime prediction / resource allocation / dynamic scaling.

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karasu-cloud-configuration-profiling

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

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perona-infrastructure-fingerprinting

Prototypical implementation of "Perona" for explicit infrastructure fingerprinting.

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phoebe

QoS-Aware Distributed Stream Processing through Anticipating Dynamic Workloads

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khaos

Fine-grained Failure Injector For Containerized Distributed Systems

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magpie

Magpie: Automatically Tuning Static Parameters for Distributed File Systems using Reinforcement Learning

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pm-data-privacy

Performance model data privacy: Paper evaluation

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stream-processing-enrichment-methods

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

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yahoo_streaming_benchmark_experiment

Streaming Application for Experiments

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