Patent ID: 11915153
Assignee: DELL PRODUCTS L.P.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 13:
14. A method of predicting response time of a storage system, comprising:
obtaining a set of training examples, the set of training examples including a plurality of training examples obtained from a plurality of storage systems, each training example being obtained from a respective one of a plurality of storage systems, and including physical configuration information of the storage system describing a number of storage engines and a number of back-end drive arrays of the respective one of the plurality of storage systems, workload features characterizing a workload processed by the respective one of the plurality of storage systems during a time interval, and storage system response time of the respective one of the plurality of storage systems when processing workload characterized by the workload features during the time interval, in which the storage system characteristics of each training example describe features of a respective storage system that was used to process a workload during a time interval, the workload features are derived from the workload during the time interval, and the storage system response time describes the average response time of the storage system when the storage system processed the workload during the time interval;
clustering the set of training examples into K clusters according to the workload features using an unsupervised learning process, each cluster including a subset of the training examples to cause training examples based on workloads with similar workload features to be assigned to the same clusters, wherein K is an integer greater than 1 (K≥2); and
using each subset of training examples to train a respective supervised learning process for the cluster, to cause each supervised learning process to learn a respective regression between two independent variables, the number of storage engines and number of back-end drive arrays of the storage system and the workload features, and a dependent variable, the storage system response time;
wherein each of the supervised learning processes is a decision tree supervised learning process, each decision tree including a plurality of branches containing nodes and terminating at leaves, the nodes of the decision trees being the number of storage engines and the number of back-end drive arrays of the storage system, and the leaves of the comprise nodes of the decision tree being the learned storage system response times.