Kamal Hakimzadeh – Reproducible Distributed Experiments

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Reproducible Distributed

Experiments

Kamal HakimzadehPhD Student mahh@kth.se

Jim DowlingAssociate Professor

jdowling@kth.se

www.karamel.io 2

Agenda

• Motivation

• Reproducibility

• Demo: Simple experiment 30-40 min

• Karamel Rep.

• Karamel Engine

• Orchestration

• Challenges

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Motivation

Analytical vs Empirical proof

DS supports many scientific advancements

Scheduling, fault tolerant, scalability … Extremely complex

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Reproducible vs. Replicable

1. Laboratory2. Experimenter3. Apparatus

Different and same conclusion

Same and same results

Reproducible

Replicable

Computational Reproducibility: Infrastructure, software, experiment and data

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Demo : Word Count

Hadoop NN

Flink JM

Hadoop DNFlink TM

Hadoop DNFlink TM

Hadoop DNFlink TM

Text Generator Text Generator Text Generator

Word Count

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Karamel: Rep. in different layers

Bare MetalGoogle Compute Engine

Virtual Machine is and abstract entity

Software is defined in Chef It is publicly available in Github

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Karamel Engine

DSL Service

Cloud Clients

Karamel Engine

Physical Mapping

Orchestrator

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Orchestration – queuing model

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Result

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Challenges and future work

Scalability Fault Recovery Model

Elasticity – Handle ChurnInstrumentation

Recommendation System

Language Support

Load generators

Scheduling

Container base machines Result Management

Debugging

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Team members

Kamal HakimzadehPhD Student at KTH

mahh@kth.se

Alberto Lorente LealSoftware Developer at Comeon

a.lorenteleal@gmail.com

Jim DowlingAssociate Professor at KTH

jdowling@kth.se

Hooman Peiro SajjadPhD Student at KTH

shps@kth.se

Abhimanyu BabbarBackend Developer at Wrap

abhimanyu.babbar88@gmail.com

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