Dell Products Inc.

Emc IP Holding Company LLC

Dell & Products L.p.

Gajanan S Natu

Steven A Morley

Rasa Raghavan

Vladimir Shveidel

Michael Lee Burriss

Susan Rundbaken Young

Derek Michael Scott

Kenneth Hu

Vamsi K Vankamamidi

Wai Chuen Yim

Shaul Dar

Vasudevan Subramanian

George Papadopoulos

Rahul D Pradhan

Alexander Yurievich Burmak

Niharika Karia

Aleksey Kabishcher

Sumanta Kashyapi

Shreyans Suresh Jasoriya

Michael John Catalanotti

Michael Barnes

David C Sydow

In at least one embodiment, a method can include: establishing a bi-directional synchronous replication configuration for a stretched resource configured across first and second sites; determining a first difference between a first maximum normalized total I/O workload of the first site and a first current normalized total I/O workload of the first site without including read I/O workload of the stretched resource; determining a second difference between a second maximum normalized total I/O workload of the second site and a second current normalized total I/O workload of the second site without including read I/O workload of the stretched resource; and selecting, in accordance with the first difference and the second difference, only one of the first site and the second site to receive and service subsequent read and write I/Os directed to the stretched resource. The configuration can be equidistant with respect to a host and the stretched resource.

Techniques for path selection in an equidistant uniform metro replication configuration

A method for time series anomaly detection with rare event failure prediction includes determining, by a device including a processor and using a first machine learning model, a location of a data anomaly within sequential data. The method also includes determining, by the device and using a second machine learning model that is not the first machine learning model, whether a probability of a future failure event is at least a threshold probability based on the data anomaly and the sequential data.

Time series anomaly detection with rare event failure prediction

In at least one embodiment, processing can include: configuring a file server on a first node of a storage system, the file server including a file system configured on a volume; configuring a first path exposing the volume to the file server as active-optimized (AO) and configuring a second path exposing the volume to the file server as active-non-optimized (ANO); for the volume, while the first path's state is AO and the second path's state is ANO, determining an internode I/O workload imbalance between the first node and the second node; and responsive to determining the internode I/O workload imbalance, reducing or removing the internode I/O workload imbalance including: redirecting I/O workload of the volume from the first node to the second node over the second path by modifying the first path's state from AO to ANO and by modifying the second path's state from ANO to AO.

Techniques for node resource balancing

A method, computer program product, and computing system for measuring a total storage device throughput value for a workload processed on a storage device within a storage array enclosure of a storage system. A maximum storage device throughput value may be determined for the workload. A storage device saturation value for the storage device may be determined based upon, at least in part, the total storage device throughput value for the workload and the maximum storage device throughput value for the workload. One or more IO requests may be processed on the storage device based upon, at least in part, the storage device saturation value determined for the storage device.

System and method for managing storage saturation in storage systems

A method for recommending a test case (TC) includes: obtaining TCs; obtaining a document associated with the TCs; analyzing the TCs and document to generate a reference document (RD); transforming, the RD into a first embedding vector (EV); receiving a query about a recommendation from an administrator; analyzing the query to infer a length of the query; making a first determination that the length of the query is greater than an input length of a model; based on the first determination, employing a summarizer model to obtain a summarized query; transforming, the summarized query into a second EV; performing a search for a most matching asset; analyzing the summarized query and most matching asset to generate the recommendation; making a second determination that the recommendation is generated; and initiating displaying of the recommendation to the administrator.

Method and system for recommending test cases using machine learning models

A method, computer program product, and computing system for determining a storage device saturation value for a workload processed on a storage device within a storage array enclosure of a storage system. A storage controller saturation value may be determined for a workload processed on a storage controller within the storage array enclosure of the storage system. A storage array enclosure saturation value may be determined based upon, at least in part, the storage device saturation value and the storage controller saturation value. One or more IO requests may be processed on the storage device using the storage controller within the storage array enclosure based upon, at least in part, the storage array enclosure saturation value determined for the storage array enclosure.

A method, computer program product, and computing system for measuring a total storage controller throughput value for a workload processed on a storage controller within a storage array enclosure of a storage system. A maximum storage controller throughput value may be determined for the workload. A storage controller saturation value may be determined for the storage controller based upon, at least in part, the total storage controller throughput value for the workload and the maximum storage controller throughput value for the workload. One or more IO requests may be processed on one or more storage devices associated with the storage controller based upon, at least in part, the storage controller saturation value determined for the storage controller.

A method of dynamically adjusting workload of a data storage system includes, while processing a first workload, calculating a saturation value of a saturation metric that scales substantially linearly with an I/O per second (IOPS) rate relative to a maximum IOPS rate of the system, determining that the saturation value is one of (1) above a high threshold and (2) below a low threshold, and performing a workload adjustment operation that establishes a second workload by (1) subtracting from the first workload based on the saturation value being above the high threshold, and (2) adding to the first workload based on the saturation value being below the low threshold, then subsequently processing the second workload.

Data storage system with dynamic workload adjustment based on headroom estimation

Load balancing in a Hyper-Converged Infrastructure (HCI) environment, in which a compute migration request is received that indicates a virtual machine to be migrated, a source appliance in which the virtual machine is currently executing, and a destination appliance to which the virtual machine is to be moved by the requested compute migration. The technology determines whether performing the requested compute migration would cause the virtual machine to experience an increased remote data storage Input/Output (I/O) performance penalty as a result of moving the virtual machine to the destination appliance. The disclosed technology may prevent the requested compute migration from being performed in response at least in part to a determination that the requested compute migration would cause the virtual machine to experience an increased remote data storage I/O performance penalty as a result of moving the virtual machine to the destination appliance.

Load balancing in a hyper-converged infrastructure (HCI) environment

Load balancing may include: receiving I/O workloads of storage server entities that service I/O operations received for logical devices, wherein each logical device has an owner that is one of the storage server entities that processes I/O operations directed to the logical device; determining normalized I/O workloads corresponding to the I/O workloads of the storage server entities; determining, in accordance with utilization criteria, imbalance criteria and the normalized I/O workloads, whether to rebalance the I/O workloads of the storage server entities; and responsive to determining to rebalance the I/O workloads of the storage server entities, performing processing to alleviate a detected I/O workload imbalance between two storage server entities. The processing may include moving logical device from a first storage server entity to a second storage server entity; and transferring ownership of the logical device from the first to the second storage server entity.

Techniques for workload balancing

A method, computer program product, and computing system for detecting a data location event concerning a data storage system; holistically analyzing the data storage system to determine a status for the data storage system; and generating one or more recommendations concerning the data location event based, at least in part, upon the status for the data storage system.

Storage management system and method for storage migration services

Systems and methods are disclosed for automatically identifying and recovering storage objects. A storage system includes a plurality of storage objects associated with storage resources in communication with the storage system, and a storage processor. The storage processor initiates a recovery process in response to a first state of the storage processor and determines a status for each of the plurality of storage objects. The storage processor identifies one or more storage objects requiring recovery based on the status of each of the storage objects and determines a first storage object to recover based on a predetermined hierarchy of storage objects. The predetermined hierarchy is based on a dependency between one or more of the storage objects. The storage processor determines a recovery operation to recover the first storage object and initiates the recovery operation on the first storage object.

Method and system for automatically identifying and recovering storage objects

Described are licensing techniques that may include: receiving a license specifying a maximum limit of a resource where the maximum limit denotes a maximum allowable consumption amount of the resource; providing the maximum limit to one or more software components; and enforcing, by the one or more software components, consumption of the maximum limit of the resource whereby an actual amount of the resource consumed is not allowed to exceed the maximum limit. The maximum limit may be a maximum storage capacity limit in connection with a virtualized storage system or appliance as well as a non-virtualized storage system or appliance. Software components may enforce the maximum limit, or a dependent limit derived therefrom, on the control path and data path. Limits specified in the license, or a dependent limit determined therefrom, may denote a maximum or minimum limit.

Growing community of inventors

Cary, NC, United States of America

Gajanan S Natu