Introduction to HDD Field Accessible Reliability Metrics to Machine Learning Applications

Data is growing at an exponential pace and the need to manage this data at the core and edge is a multi-facet problem. New innovative methods to ensure data availability & utilization of the resources that store this data are being developed. Storage device health monitoring & utilization is one such issue. Developing models to predict drive degradation while using machine learning principles is highly desirable. A recent Google blog described the machine learning techniques being used to promote & improve drive maintenance of their fleet. Enabling this capability requires the ability to efficiently pull drive information. Seagate has developed a method that can allow data to be extracted quickly and reliably while combining many different data sets into a single command operation. Customers including Google and Tencent implement such drive health monitoring into their eco-system. This presentation will review what is openly available from these high capacity devices and how they can be used to create novel ML models to predict device behavior and make future utilization decisions.

• Understand the FARM log data and why it is an improvement over current industry standard and how it facilitates predictive model
• Understand the methodologies of machine learning and how it can be used to provide better data analytics assessments.
• Understand the open source tools availability to extract and parse FARM data in standard formats like json & prometheus prom for