公开(公告)号：US11436052B2

公开(公告)日：2022-09-06

申请号：US16865716

申请日：2020-05-04

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Mayukh Dutta ,  Manoj Srivatsav ,  Aesha Dhar Roy

IPC: G06F3/00 ,  G06F9/50 ,  G06F30/27

Abstract: In some examples, using a model generated from an aggregation of parameter values for a plurality of host systems, a system predicts an operational metric representing usage or performance of a shared resource due to a requester in a first host system of the plurality of host systems, the shared resource being outside of the plurality of host systems.

公开(公告)号：US10778552B2

公开(公告)日：2020-09-15

申请号：US16034531

申请日：2018-07-13

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Mayukh Dutta ,  Manoj Srivatsav ,  John J. Sengenberger

IPC: G06F15/16 ,  H04L12/26 ,  G06F11/34 ,  G06K9/62 ,  H04L29/08 ,  G06F11/30

Abstract: A system or method for identifying latency contributors in a data storage network, that may include creating a historical workload fingerprint model for a data storage network from training data, along with monitoring and classifying a current sample data from the data storage network into a cluster, current workload fingerprint, and current workload type. The method may further include assigning a score to the current sample data based on the historical workload fingerprint model and correlating measured latency values from the current sample data to historically measured latency related factors to create a latency score chart that identifies factors causing latency in the data storage network for the current sample data.

公开(公告)号：US20190334786A1

公开(公告)日：2019-10-31

申请号：US16034608

申请日：2018-07-13

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Mayukh Dutta ,  Manoj Srivatsav ,  John J. Sengenberger

IPC: H04L12/24 ,  H04L12/26 ,  G06F11/34 ,  G06F11/30 ,  H04L29/08 ,  G06K9/62

Abstract: A system or method for predicting workload and latency patterns in a data storage network that includes training a model in a cloud based on data storage network I/O data, monitoring sample I/O data in a data storage network at predetermined intervals, and determining a workload fingerprint in the trained model that corresponds to the sample I/O data. The method further includes calculating a workload value for the sample I/O data and forecasting future workload and latency patterns using an autoregressive integrated moving average statistical calculation based on the sample I/O time series data and the calculated workload value for the sample I/O.