SNIA Developer Conference September 15-17, 2025 | Santa Clara, CA
Paul Burnett
Principal Engineer
Seagate Technologies
Paul Burnett is a Principal Engineer in the Product Design and Development organization at Seagate Technologies and has been in storage industry for 40 years. Prior to joining the design organization, he held a lead engineer position in the Failure Analysis Organization. In this role he developed an understanding of how a disc drive can fail and how to identify signatures in the internal and external data sets. This is highly important in understanding how failures manifest themselves in customer environments as well as in internal reliability testing. This knowledge is now used in insuring information that is tracked in disc drives are informative and accurate to be able to assess potential failure mechanisms. His work with cross functional organizations in Seagate has led to a broad range of knowledge within a complex design of a disc drive.
Related Sessions
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.