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SANgo: a storage infrastructure simulator with reinforcement learning support.

Kenenbek Arzymatov1, Andrey Sapronov1, Vladislav Belavin1

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Summary
This summary is machine-generated.

SANgo is a Go-based simulator for storage area networks (SANs). It models storage infrastructure to explore stability boundaries and train digital twins using reinforcement learning.

Keywords:
Discrete event simulationOptimal controlReinforcement learningStorage arrayStorage system simulation

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Area of Science:

  • Computer Science
  • Software Engineering

Background:

  • Modern storage infrastructure requires sophisticated simulation tools.
  • Understanding storage system dynamics is crucial for stability analysis.

Purpose of the Study:

  • Introduce SANgo, a Go-based package for simulating storage area networks (SANs).
  • Enable exploration of real storage system stability boundaries.
  • Facilitate the creation of realistic digital twins for reinforcement learning (RL).

Main Methods:

  • Discrete-event modeling paradigm to capture storage system structure and dynamics.
  • Flexible package design for configurable component modeling.
  • Interfaces for real-time monitoring, tuning, and external control of simulated parameters.

Main Results:

  • SANgo accurately replicates storage system behavior at various granularities.
  • The simulator allows tracking key metrics of storage controllers, network connections, and hard drives.
  • Support for OpenAI gym interface enables benchmarking of RL algorithms.

Conclusions:

  • SANgo provides a powerful tool for analyzing storage system stability.
  • The software facilitates the development and training of digital twins using RL.
  • SANgo serves as a valuable benchmark for evaluating RL algorithms in storage system optimization.