SANgo: a storage infrastructure simulator with reinforcement learning support

• Published in: PeerJ. Computer science Vol. 6; p. e271
• Main Authors: Arzymatov, Kenenbek, Sapronov, Andrey, Belavin, Vladislav, Gremyachikh, Leonid, Karpov, Maksim, Ustyuzhanin, Andrey, Tchoub, Ivan, Ikoev, Artem
• Format: Journal Article
• Language: English
• Published: United States PeerJ, Inc 04.05.2020; PeerJ Inc
• Subjects: Algorithms; Behavior; Codes; Computer simulation; Control theory; Data Mining and Machine Learning; Deep learning; Discrete event simulation; Failure; Flexible structures; Infrastructure; Machine learning; Mathematical models; Metadata; Optimal control; Parameters; Reinforcement learning; Researchers; Scientific Computing and Simulation; Software; Software Engineering; Storage area networks; Storage array; Storage system simulation; Storage systems; Storage system simulation; Discrete event simulation; Optimal control; Reinforcement learning; Storage array
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.271

We introduce SANgo (Storage Area Network in the Go language)—a Go-based package for simulating the behavior of modern storage infrastructure. The software is based on the discrete-event modeling paradigm and captures the structure and dynamics of high-level storage system building blocks. The flexible structure of the package allows us to create a model of a real storage system with a configurable number of components. The granularity of the simulated system can be defined depending on the replicated patterns of actual system behavior. Accurate replication enables us to reach the primary goal of our simulator—to explore the stability boundaries of real storage systems. To meet this goal, SANgo offers a variety of interfaces for easy monitoring and tuning of the simulated model. These interfaces allow us to track the number of metrics of such components as storage controllers, network connections, and hard-drives. Other interfaces allow altering the parameter values of the simulated system effectively in real-time, thus providing the possibility for training a realistic digital twin using, for example, the reinforcement learning (RL) approach. One can train an RL model to reduce discrepancies between simulated and real SAN data. The external control algorithm can adjust the simulator parameters to make the difference as small as possible. SANgo supports the standard OpenAI gym interface; thus, the software can serve as a benchmark for comparison of different learning algorithms.