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Kamal Hakimzadeh – Reproducible Distributed Experiments

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Reproducible Distributed Experiments Kamal Hakimzadeh PhD Student mahh@kth.se 1 Jim Dowling Associate Professor jdowling@kth.se www.karamel.io
Agenda 2 • Motivation • Reproducibility • Demo: Simple experiment 30-40 min • Karamel Rep. • Karamel Engine • Orchestration • Challenges www.karamel.io
Motivation 3 Analytical vs Empirical proof DS supports many scientific advancements Scheduling, fault tolerant, scalability … Extremely complex www.karamel.io
Reproducible vs. Replicable 4 1. Laboratory 2. Experimenter 3. Apparatus Reproducible Replicable Computational Reproducibility: Infrastructure, software, experiment and data www.karamel.io
Demo : Word Count 5 Text Generator Text Generator Text Generator Word Count www.karamel.io
Karamel: Rep. in different layers 6 Bare Metal Google Compute Engine Virtual Machine is and abstract entity Software is defined in Chef It is publicly available in Github www.karamel.io
Karamel Engine 7 DSL Service Cloud Clients Karamel Engine Physical Mapping Orchestrator www.karamel.io
Orchestration – queuing model 8www.karamel.io
Result 9www.karamel.io
Challenges and future work 10 Scalability Fault Recovery Model Elasticity – Handle Churn Instrumentation Recommendation System Language Support Load generators Scheduling Container base machines Result Management Debugging www.karamel.io
Team members 11 Kamal Hakimzadeh PhD Student at KTH mahh@kth.se Alberto Lorente Leal Software Developer at Comeon a.lorenteleal@gmail.com Jim Dowling Associate Professor at KTH jdowling@kth.se Hooman Peiro Sajjad PhD Student at KTH shps@kth.se Abhimanyu Babbar Backend Developer at Wrap abhimanyu.babbar88@gmail.comwww.karamel.io
12www.karamel.io

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