Paper Title: Optimizing SSD Performance with AI and Real-World Workloads

Paper Abstract: The best way to optimize SSD performance is to utilize real world applications, but how should designers analyze the effects of attempts to improve wear leveling, task scheduling, pre-fetching, and caching and to minimize write amplification and garbage collection? Furthermore, how can they predict I/O stream traffic? The answer is to use AI techniques to develop a model from the results obtained using the workload data, and the model will learn from the results presented to it during training and then optimize performance during execution. This talk will present an example that shows the use of these methods on advanced XL flash SSDs in which a recurrent neural network serves as the underlying model and produces substantial performance improvements.

Paper Author: Eden Kim, CEO, Calypso Systems

Author Bio: Eden Kim is CEO of Calypso Systems, Inc., through which he evangelizes Real World Workload capture, analysis and test. His company publishes industry white papers, and presents at international trade and association shows.Eden is also chair of the SNIA Solid State Storage Technical Working Group which has published industry standard performance test specifications for synthetic and real world workloads for SSDs and Data Center storage.