Emc IP Holding Company LLC

Dell Products Inc.

Paul J McSweeney

Aaron T Twohig

Ciara Stacke

Andrea Graham

Kenneth Byrne

Fearghal O'Maolcatha

Colm O'Leary

Aidan Hally

Ellen Murphy

Elaine Holland

Andrew O'Shaughnessy

Helen Walsh

Aspects of the present disclosure relate to controlling resource consumption of a server and storage array. In embodiments, a request can be received by a server that is communicatively coupled to a storage array. Further, the services required to process the request can be identified. Additionally, services' activation can be controlled based on a mapping of request-related actions and initiated services.

Microservices server and storage resource controller

Storage array health scores based on alerts are auto-corrected by using alert validation and alert matching to identify missed, stale, and redundant alerts. In response to restoration of an event daemon or reboot of a storage management server, each alert associated with each storage array is validated to determine whether current state of storage array hardware or a logical object differs from the state indicated in the alert. Alerts that differ from current state are updated or invalidated and the health score is recalculated. Responsive to receipt of a new alert, existing alerts associated with the same storage array hardware or logical object are identified and Jaro-Winkler distance between the identified alerts and the new alert is calculated. Any existing alert for which the Jaro-Winkler distance relative to the new alert is greater than or equal to 0.9 is deleted and the health score is recalculated.

Auto-correct health score related to alerts from storage objects

Storage objects are mapped to host applications in a storage domain. Each of the storage objects is analyzed to determine importance in terms of predetermined criteria such as quality of service of the storage object, replication level of the storage object, size of the storage object, input-output loading of the storage object, and input-output loading of a host application that uses the storage object. Storage objects that satisfy predetermined importance characteristics are automatically tagged with the name of the host application that uses the storage object. The storage objects may also be tagged with the names of data structures that satisfy predetermined importance characteristics. Storage groups, masking views, and volume devices may also be tagged.

Automatic tagging of storage objects with associated application names

One or more aspects of the present disclosure relate to a storage health status. In embodiments, one or more events corresponding to a storage array can be received. Each event can include an alert. A type of each event can also be identified. For each event having a qualifying type, at least one prior event made obsolete by each received event can be cleared.

Storage device health status controller

A request manager analyzes API calls from a client to a host application for state and performance information. If current utilization of host application processing or memory footprint resources exceed predetermined levels, then the incoming API call is not forwarded to the application. If current utilization of the host application processing and memory resources do not exceed the predetermined levels, then the request manager quantifies the processing or memory resources required to report the requested information and determines whether projected utilization of the host application processing or memory resources inclusive of the resources required to report the requested information exceed predetermined levels. If the predetermined levels are not exceeded, then the request manager forwards the API call to the application for processing.

Processing rest API requests based on resource usage satisfying predetermined limits

Techniques for performing management operations may include: creating a job using a graphical user interface (GUI) of an application, wherein the job includes at least one task and the job performs a first management operation including the at least one task; storing first information that describes the job in an application database for the application; and performing first processing that generates a code module for the job, wherein said first processing uses the first information from the application database. The code module may be a script written in a target script language. Multiple providers may converts different types of tasks to scripts. The providers may register prior to performing the first processing. Registering may include each provider declaring each type of task the provider converts to a specified target scripting language. The task types may be organized in a hierarchy of classes or categories.

Techniques for converting jobs into scripts

Techniques for rendering information for display in a graphical user interface (GUI) of an application may include: navigating to a point in a workflow of the application; and responsive to navigating to the point in the workflow of the application, performing processing to render a component in the GUI of the application. The component may include user interface elements for actions. The processing may include: receiving priorities for the actions of the component, wherein each of the priorities denotes a priority of one of the actions relative to remaining ones of the actions and wherein the priorities are determined in accordance with frequencies of usage of the GUI of the application to perform the plurality of actions; organizing user interface elements for the actions in an arrangement in accordance with the priorities; and rendering the component using the arrangement of the user interface elements for the actions.

Dynamic restructuring of user interface based on usage data

Techniques for evaluating data sets for compression processing may include: receiving first information for a data set, the first information including I/O activity information for the data set, a service level objective for the data set, and a size of the data set; determining, in accordance with the first information, whether the data set meets criteria indicating a specified level of importance and a specified level of I/O activity; responsive to determining the data set meets the criteria, sending second information identifying one or more storage objects of the data set to a compression engine, wherein the one or more storage objects of the data set are identified as having the specified level of importance and at least the specified level of I/O activity; and performing processing, by the compression engine using the second information, to determine whether to compress first data stored in the data set.

Enhanced storage compression based on activity level

Techniques for identifying I/O workload patterns may include monitoring key performance indicators (KPIs) for a monitoring period; and at the end of the monitoring period, performing processing including: determining whether there have been a specified number of occurrences of a predefined event with respect to a data portion of the application, wherein the predefined event is a violation of an application KPI for the application and also a violation of at least one of a plurality of data storage KPIs for the data portion of the application; and responsive to determining that there have been the specified number of the occurrences of the predefined event with respect to the first data portion of the application, recording a pattern of I/O workload for the data portion in accordance with the occurrences of the predefined event. Hints, such as affecting data movement and/or compression, may be generated based on detected patterns.

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Cork, Ireland

Paul J McSweeney