Hewlett Packard Enterprise Development LP

Mayukh Dutta

Manoj Srivatsav

Aesha Dhar Roy

Jharna Aggarwal

Manu Sharma

Gautham Parameshwar Hegde

Sivasakthi Thirugnanapandi

John J Sengenberger

Soumen Shekhar Das

Ganesha Devadiga

Prasenjit Saha

Geethanjali N Rao

Sudheer Vanapalli

Gautam Kaistha

Jaffer Sherif Rajasab

Ashwin Jaydev Nair

In some examples, a computing system receives an indication of an increased workload portion to be added to a workload of a storage system, the workload comprising buckets of operations of different characteristics. The computing system computes, based on quantities of operations of the different characteristics in the workload, factor values that indicate distribution of operations of the increased workload portion to the buckets of operations of the different characteristics, and distributes, according to the factor values, the operations of the increased workload portion into the buckets of operations of the different characteristics.

Distribution of quantities of an increased workload portion into buckets representing operations

In some examples, a system creates a training data set based on features of sample workloads, the training data set comprising labels associated with the features of the sample workloads, where the labels are based on load indicators generated in a computing environment relating to load conditions of the computing environment resulting from execution of the sample workloads. The system groups selected workloads into a plurality of workload clusters based on features of the selected workloads, and computes, using a model trained based on the training data set, parameters representing contributions of respective workload clusters of the plurality of workload clusters to a load in the computing environment. The system performs workload management in the computing environment based on the computed parameters.

Workload management using a trained model

In some examples, a system receives a first collection of tokens relating to characteristics of workloads for a computing system. The system encodes the first collection of tokens, the encoding including computing weights representing relationships among tokens of the first collection of tokens, and generating a representation of the first collection of tokens based on the weights. The system determines, based on the representation, a correlation between the first collection of tokens and a second collection of tokens relating to elapsed times in executing the workloads.

Correlations between workload characteristics and elapsed times

In some examples, a system receives input information of characteristics relating to a storage volume to be provisioned in a collection of storage systems, determines, based on the input information of the characteristics relating to the storage volume, a workload profile, and simulates execution of a workload according to the workload profile in each storage system of the collection of storage systems. Based on the simulation, the system determines a respective amount of headroom used by the workload in each storage system of the collection of storage systems, and selects, based on the determined respective amounts of headroom used by the workload in respective storage systems of the collection of storage systems, a storage system from the collection of storage systems on which the storage volume is to be provisioned.

Storage system selection for storage volume deployment

In some examples, a fleet storage provider performs storage management operations for a fleet of storage arrays, the storage arrays in the fleet of storage arrays being of one or more storage types. In response to an addition, to the fleet of storage arrays, of a new storage array of a first storage type different from each storage type of the one or more storage types, a system identifies the new storage array as being associated with a first storage class of a plurality of different storage classes, and associates the new storage array with a fleet service that abstracts component details of the fleet of storage arrays to the fleet storage provider. In response to a request, the system provisions a storage volume on a selected storage array of the fleet of storage arrays, the provisioning performed by the fleet storage provider in cooperation with the fleet service.

Storage array fleet management

In some examples, a system identifies resource contention for a resource in a storage system, and determines that a workload collection that employs data reduction is consuming the resource. The system identifies relative contributions to consumption of the resource attributable to storage volumes in the storage system, where the workload collection that employs data reduction are performed on data of the storage volumes. The system determines whether storage space savings due to application of the data reduction for a given storage volume of the storage volumes satisfy a criterion, and in response to determining that the storage space savings for the given storage volume do not satisfy the criterion, the system indicates that the data reduction is not to be applied for the given storage volume.

Data reduction for storage volumes

In some examples, a system aggregates operational metric data of a plurality of storage volumes into aggregated operational metric data groups that correspond to different workload types of workloads for accessing data of a storage system. The system computes an operational metric for a first workload type of the different workload types, the operational metric relating to a resource of the storage system, where the computing of the operational metric for the first workload type comprises inputting aggregated operational metric data of a first aggregated operational metric data group of the aggregated operational metric data groups into a model trained at a system level of the storage system.

Operational metric computation for workload type

In some examples, a system assigns workload fingerprints to each respective storage volume of a plurality of storage volumes, the workload fingerprints assigned to the respective storage volume across a plurality of points. Based on the workload fingerprints assigned to respective storage volumes of the plurality of storage volumes, the system groups the storage volumes into clusters of storage volumes. The system manages an individual cluster of the clusters of storage volumes according to an attribute associated with the individual cluster.

Storage volume clustering based on workload fingerprints

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.

Shared resource operational metric

A system or method for identifying anomalies indicating misconfiguration or software bugs in a data storage network that may include capturing data storage network latency metrics, identifying periods of high latency in the captured latency metrics, applying a statistical deviation operation to the latency metrics in the periods of identified high latency, and identifying outliers in the statistically deviated latency metrics. The method further includes calculating a median of the identified outliers, normalizing the median of the identified outliers, and scoring the normalized median values of the identified outliers.

Storage system latency outlier detection

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.

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Bengaluru, India

Mayukh Dutta