R&D: Can LLMs Model the Environmental Impact on SSD?

ACM Digital Library has published, in HotStorage ’25: Proceedings of the 17th ACM Workshop on Hot Topics in Storage and File Systems, an article written by Mayur Akewar, Gang Quan, Florida International University, Miami, Florida, USA, Sandeep Madireddy, Argonne National Laboratory, Lemont, Illinois, USA, and Janki Bhimani, Florida International University, Miami, Florida, USA.

Abstract: “Environmental stressors such as temperature, humidity, vibration, and radiation can severely impact the performance and reliability of SSDs, particularly in edge, automotive, aerospace, and datacenter deployments. Capturing sensor data in the field and conducting accelerated lab experiments are challenging, as they are time-consuming, resource-intensive, and often destructive to hardware. Specialized setups, such as thermal chambers or vibration rigs, are also required, which is why few studies explore this area, and current storage management techniques like RAID, tiering, and deduplication do not consider environmental factors. Models to capture these impacts would open new research opportunities across various fields. However, accurately modeling these effects remains challenging due to, (1) the limited availability of experimental data, (2) the complex, domino-like impact of historical exposure, (3) the interrelated nature of environmental factors, such as temperature and humidity, which exhibit correlation, (4) different response of each type of NAND flash memory TLC, MLC, and SLC to environmental factors, and (5) the difficulty that analytical and simple machine learning models face in generalizing across devices, environments, and unseen combinations of stressors. We believe that LLMs may offer a transformative alternative to this complex problem, with embedded domain knowledge and reasoning capabilities, to facilitate prompt-based natural language interaction. We propose a hybrid framework that combines Chain-of-Thought prompting and Retrieval-Augmented Generation to guide LLMs using physical principles and prior experiments. It enables interpretable “what-if” analysis of SSD behavior under environmental changes. Our results show that the LLM can effectively model the impact of temperature, humidity, and vibration on SSD performance, producing tail latency and bandwidth predictions with minimal error. The code and data are available on GitHub at https://github.com/Damrl-lab/SSD_LLM.“