Abstract
Over the years, SSD QoS has become more important to a variety of storage market segments. Traditional latency reporting methods do not always accurately depict QoS behaviors. This is problematic when attempting to understand what events lead to a specific QoS level and how to mitigate latency events that lead to levels of QoS. Defining correct statistical techniques for large populations of latencies deepens our understanding of what drives levels of QoS. Advanced statistical techniques, such a machine learning and utilizing AI, allows for deeper understanding of what drives QoS and how to correctly manage large quantities of latencies. New visualization techniques enhance capabilities to understand latency behavior and define critical scenarios that drive latency.