PipeSim: Trace-driven Simulation of Large-Scale AI Operations Platforms
- URL: http://arxiv.org/abs/2006.12587v1
- Date: Mon, 22 Jun 2020 19:55:37 GMT
- Title: PipeSim: Trace-driven Simulation of Large-Scale AI Operations Platforms
- Authors: Thomas Rausch and Waldemar Hummer and Vinod Muthusamy
- Abstract summary: We present a trace-driven simulation-based experimentation and analytics environment for large-scale AI systems.
Analytics data from a production-grade AI platform developed at IBM are used to build a comprehensive simulation model.
We implement the model in a standalone, discrete event simulator, and provide a toolkit for running experiments.
- Score: 4.060731229044571
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Operationalizing AI has become a major endeavor in both research and
industry. Automated, operationalized pipelines that manage the AI application
lifecycle will form a significant part of tomorrow's infrastructure workloads.
To optimize operations of production-grade AI workflow platforms we can
leverage existing scheduling approaches, yet it is challenging to fine-tune
operational strategies that achieve application-specific cost-benefit tradeoffs
while catering to the specific domain characteristics of machine learning (ML)
models, such as accuracy, robustness, or fairness. We present a trace-driven
simulation-based experimentation and analytics environment that allows
researchers and engineers to devise and evaluate such operational strategies
for large-scale AI workflow systems. Analytics data from a production-grade AI
platform developed at IBM are used to build a comprehensive simulation model.
Our simulation model describes the interaction between pipelines and system
infrastructure, and how pipeline tasks affect different ML model metrics. We
implement the model in a standalone, stochastic, discrete event simulator, and
provide a toolkit for running experiments. Synthetic traces are made available
for ad-hoc exploration as well as statistical analysis of experiments to test
and examine pipeline scheduling, cluster resource allocation, and similar
operational mechanisms.
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