Hewlett Packard Enterprise Development LP

Mantej Singh Gill

Madhusoodhana Chari Sesha

Dhamodhran Sathyanarayanamurthy

Anil Babulal

Arun Mahendran

Systems and methods are provided for using historic input power periodic data from a server in an IT data center to train a machine learning (ML) model to obtain forecasted power consumption data of the server for a future time period. Time windows of hotspots or coldspots are then identified in the forecasted power consumption data, hotspots being defined as areas or regions of over-utilization in a time series data, and coldspots being defined as areas or regions of under-utilization in a time series data. The hotspots and coldspots are identified by calculating an exponential mean average (EMA) of the forecasted power consumption data, taking points above the EMA as hotspots and points below the EMA as coldspots. The identified hotspots and coldspots can be used to schedule workloads for a server or a data center, to more efficiently plan existing workloads, or to introduce new workloads at more optimal time periods.

Identifying hotspots and coldspots in forecasted power consumption data in an it data center for workload scheduling

A device and corresponding method are provided to provide accurate estimates of how long it will take to install updates to compute nodes in a large-scale computer deployment. a duration prediction model is trained using historical data from previous updates to compute nodes. The features selected to train the duration prediction model are update features including update component type, update component size, update component duration and compute node features including operating system, BMC type/version, UEFI type/version, and generation for each of the compute nodes updated. The historical data for the features is accessed from a metadata store.

Maintenance time window prediction for installing updates to a compute node

Systems and methods are provided for compressing a time-series dataset from a monitored device into a compressed dataset representation. Using an unsupervised machine learning model, the system may group a contiguous set of datapoints of the time-series dataset and group, using a distance algorithm, the first cluster to a first motif. Many motifs can be generated to identify different data signatures in the time-series dataset. The plurality of motifs can be used to generate a data definition, motif sequence graph, directed graph, or other combinations of datapoints. These datapoints can be combined through a summation process with other datapoints generated by a second machine learning model. The output of the summation process can be used to forecast device usage of a monitored device in a data center.

Forecasting device usage using motifs of univariate time-series dataset and combined modeling

Systems and methods are provided for receiving a time series dataset from a monitored processor and group the dataset into a plurality of clusters. Using an unsupervised machine learning model, the system may combine a subset of the plurality of clusters by data signature similarities to form a plurality of motifs and combine the plurality of motifs into one or more shapelets. In some examples, the system may train a supervised machine learning model using the plurality of motifs and the one or more shapelets as input to the supervised machine learning model. The system can perform various actions in response to labelling the time series dataset, including predicting a second time series dataset, determining that a monitored processor corresponds with an overutilization at a particular time, or suggesting a reduction of additional utilization of the monitored processor.

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Karnataka, India

Mantej Singh Gill