Mark Golez | Senior Staff Software Engineer
Solidigm

Future of Memory and Storage - Day 2 @ 15:20

Using Deep Q-Network (DQN) to Optimize Data Centre SSD Quality of Service and Performance

AI-driven data centers impose extremely strict Quality of Service (QoS) and performance requirements on storage systems, creating a significant challenge for SSD vendors tasked with optimizing diverse product portfolios across multiple hyperscalers, each with distinct workloads, hardware configurations, and power constraints. To address this complexity, we present a reinforcement-learning based mechanism that autonomously optimizes SSD performance and QoS using a DEEP Q-Network (DQN). The system iteratively observes the current SSD firmware parameter configuration, proposes targeted adjustments and applies them before executing representative workloads. The plan is to present how Athena operates in practice and the tangible performance and QoS improvements that were seen on a real SSD hardware. Manual SSD tuning time was reduced by more than 50% and read latency improved by up to 67%, and random mixed workloads saw as much as 19% higher IOPs. The discussion will also cover why reinforcement learning is well-suited for SSD tuning, what tunable parameters exist, and forward-looking view on integrating this capability directly into SSD controller for scalable deployment.

Mark Golez, Senior Staff Software Engineer, Solidigm

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Mark Golez / Using Deep Q-Network (DQN) to Optimize Data Centre SSD Quality of Service and Performance

Mark Golez, Senior Staff Software Engineer,
Solidigm

Mark Golez is a Senior Staff Software Engineer at Solidigm, where he focuses on advancing SSD firmware and performance quality (PQ) at scale. In this role, he leads innovation across firmware, tools, and AI-driven optimization techniques to deliver real-world performance improvements for next...

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