Jun Li
Jun Li earned a PhD in Materials Sciences and Engineering from Washington State University, a MBA from Santa Clara University, and a BS in Applied Physics from Peking University. He has over 15 years of experiences in reliability engineering and failure analysis in storage systems, lasers, LEDs, solar and fuel cells. He had held engineering or scientific positions at Coherent Inc., Charles Evans &Associates, Cornell Nanofabrication Facilities and Pacific Northwest National Labs before talking a reliability engineering role at NetApp Inc. in 2006. He has over 20 publications and has developed numerous products. He is currently with Performance Product Group at NetApp leading reliability improvement efforts for NetApp’s FAS storage systems. He has recently led a study to investigate field reliability of NAND flash products and published the results at RAMS 2015. He is a certified reliability engineer (CRE) of American Society of Quality.
Leveraging Big Data to Improve Reliability & Maintainability
Jun Li
Jun Li earned a PhD in Materials Sciences and Engineering from Washington State University, a MBA from Santa Clara University, and a BS in Applied Physics from Peking University. He has over 15 years of experiences in reliability engineering and failure analysis in storage systems, lasers, LEDs, solar and fuel cells. He had held engineering or scientific positions at Coherent Inc., Charles Evans &...read more
Details
Big Data has brought tremendous opportunities for Reliability & Maintainability professionals. This paper shares how NetApp leverages big data from NetApp’s AutoSupportTM feature to deliver industry leading reliability and preventive maintenance. When AutoSupport is enabled, NetApp systems in the field periodically send selective system data to a NetApp corporate repository. This system data, consisting of configuration and log files with warnings, error messages and various sensor readings contains gold nuggets, which if mined and analyzed properly, can be of great benefit for improving system R&M.; A few case studies are presented to show the effectiveness of the approach. These cases address the effect of cooling air temperature on hardware reliability, characteristics of DRAM memory errors, NAND flash field characteristics and preventive maintenance of defective memory modules. Results from each case also provide answers to some frequently asked questions in the computing industry.
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