Applying AI/ML Methodologies to Categorize Storage Workloads and Replaying them in Standard Test Environments
With the complexity of applications increasing every day, the workloads generated by these applications are complicated and hard to replicate in test environments. We propose an efficient method to synthesize a close approximation of these application workloads based on analyzing the historic autosupport data from field using an iterative mechanism and also a method to store and replay these workloads in the test environment for achieving the goals of customer driven testing.