Abstract: Systems and methods for automated remediation of issues arising in a data management storage system are provided. Deployed assets of a storage solution vendor may deliver telemetry data to the vendor on a regular basis. The received telemetry data may be processed by an AIOps platform to perform predictive analytics and arrive at “community wisdom” from the vendor's installed user base. In one embodiment, an insight-based approach is used to facilitate risk detection and remediation including proactively addressing issues before they turn into more serious problems. For example, based on continuous learning based on the community wisdom and making one or both of a rule set and a remediation set derived therefrom available for use by cognitive computing co-located with a customer's storage system, a risk to which the storage system is exposed may be determined and a corresponding remediation may be deployed to address or mitigate the risk.

Abstract: Systems and methods for automated remediation of issues arising in a data management storage system are provided. Deployed assets of a storage solution vendor may deliver telemetry data to the vendor on a regular basis. The received telemetry data may be processed by an AIOps platform to perform predictive analytics and arrive at “community wisdom” from the vendor's installed user base. In one embodiment, an insight-based approach is used to facilitate risk detection and remediation including proactively addressing issues before they turn into more serious problems. For example, based on continuous learning based on the community wisdom and making one or both of a rule set and a remediation set derived therefrom available for use by cognitive computing co-located with a customer's storage system, a risk to which the storage system is exposed may be determined and a corresponding remediation may be deployed to address or mitigate the risk.

Abstract: Methods and systems for a networked storage system are provided. One method includes receiving a resource identifier identifying a resource of a network storage environment as an input to a processor executable application programming interface (API); and predicting available performance capacity of the resource by using an optimum utilization of the resource, a current utilization and a predicted utilization based on impact of a workload change at the resource, where the optimum utilization is an indicator of resource utilization beyond which throughput gains for a workload is smaller than increase in latency in processing the workload.

Abstract: Systems and methods for automated remediation of issues arising in a data management storage system are provided. Deployed assets of a storage solution vendor may deliver telemetry data to the vendor on a regular basis. The received telemetry data may be processed by an AIOps platform to perform predictive analytics and arrive at “community wisdom” from the vendor's installed user base. In one embodiment, an insight-based approach is used to facilitate risk detection and remediation including proactively addressing issues before they turn into more serious problems. For example, based on continuous learning based on the community wisdom and making one or both of a rule set and a remediation set derived therefrom available for use by cognitive computing co-located with a customer's storage system, a risk to which the storage system is exposed may be determined and a corresponding remediation may be deployed to address or mitigate the risk.

Abstract: Methods and systems for a networked storage system are provided. One method includes receiving a resource identifier identifying a resource of a network storage environment as an input to a processor executable application programming interface (API); and predicting available performance capacity of the resource by using an optimum utilization of the resource, a current utilization and a predicted utilization based on impact of a workload change at the resource, where the optimum utilization is an indicator of resource utilization beyond which throughput gains for a workload is smaller than increase in latency in processing the workload.

Abstract: Methods and systems for a networked storage system are provided. One method includes receiving a resource identifier identifying a resource of a network storage environment as an input to a processor executable application programming interface (API); and predicting available performance capacity of the resource by using an optimum utilization of the resource, a current utilization and a predicted utilization based on impact of a workload change at the resource, where the optimum utilization is an indicator of resource utilization beyond which throughput gains for a workload is smaller than increase in latency in processing the workload.

Abstract: Methods and systems for a networked storage system are provided. One method includes receiving a resource identifier identifying a resource of a network storage environment as an input to a processor executable application programming interface (API); and predicting available performance capacity of the resource by using an optimum utilization of the resource, a current utilization and a predicted utilization based on impact of a workload change at the resource, where the optimum utilization is an indicator of resource utilization beyond which throughput gains for a workload is smaller than increase in latency in processing the workload.

Abstract: Methods and systems for a networked storage system are provided. One method includes determining by a processor, a demand pattern for a first workload that is assigned a service level objective (SLO) for using a resource of a networked storage system. The SLO is defined by an allotted performance parameter, and the demand pattern identifies a first duration when a SLO allotment for the first workload is underutilized, and a second duration when the SLO allotment is being utilized. The SLO allotment is dynamically adjusted for the first duration by modifying a parameter associated with the performance parameter, while maintaining the SLO allotment for the second duration. This makes additional performance capacity of the resource available for re-allocation. The additional available performance capacity is dynamically allocated for an identified second workload that needs an increase in SLO allotment for a certain duration and/or for provisioning a new workload.

Abstract: Methods and systems for a networked storage system are provided. One method includes receiving a resource identifier identifying a resource of a network storage environment as an input to a processor executable application programming interface (API); and predicting available performance capacity of the resource by using an optimum utilization of the resource, a current utilization and a predicted utilization based on impact of a workload change at the resource, where the optimum utilization is an indicator of resource utilization beyond which throughput gains for a workload is smaller than increase in latency in processing the workload.

Abstract: Methods and systems for a networked storage system are provided. One method includes receiving a resource identifier identifying a resource of a network storage environment as an input to a processor executable application programming interface (API); and predicting available performance capacity of the resource by using an optimum utilization of the resource, a current utilization and a predicted utilization based on impact of a workload change at the resource, where the optimum utilization is an indicator of resource utilization beyond which throughput gains for a workload is smaller than increase in latency in processing the workload.

Abstract: Methods and systems for a networked storage system are provided. One method includes determining by a processor, a demand pattern for a first workload that is assigned a service level objective (SLO) for using a resource of a networked storage system. The SLO is defined by an allotted performance parameter, and the demand pattern identifies a first duration when a SLO allotment for the first workload is underutilized, and a second duration when the SLO allotment is being utilized. The SLO allotment is dynamically adjusted for the first duration by modifying a parameter associated with the performance parameter, while maintaining the SLO allotment for the second duration. This makes additional performance capacity of the resource available for re-allocation. The additional available performance capacity is dynamically allocated for an identified second workload that needs an increase in SLO allotment for a certain duration and/or for provisioning a new workload.

Abstract: Methods and systems for a networked storage system are provided. One method includes assigning by a processor executable management module a service level objective (SLO) for a workload, where the SLO is allotted a plurality of performance parameters for tracking performance of the workload for storing data in a networked storage environment; tracking historical performance of the workload to determine a duration when SLO allotment defined by the plurality of performance parameters is being under-utilized; adjusting automatically the SLO allotment for the workload during the duration when the SLO allotment is under-utilized; and re-allocating automatically the available performance capacity of a resource used by the workload to another workload whose assigned SLO is not being under-utilized.

Abstract: Methods and systems for a networked storage system are provided. One method includes categorizing by a processor performance data associated with a resource used in a networked storage environment for reading and writing data at a storage device based on a workload mix, where the workload mix is determined by a service time in which the resource processes the workload mix, a parameter indicating variability of the service time and a utilization bin index value indicating resource utilization at a given time; and determining by the processor available performance capacity of the resource using the categorized performance data, where the available performance capacity is based on optimum utilization of the resource and utilization of the resource.

Abstract: Methods and systems for a networked storage system are provided. One method includes assigning by a processor executable management module a service level objective (SLO) for a workload, where the SLO is allotted a plurality of performance parameters for tracking performance of the workload for storing data in a networked storage environment; tracking historical performance of the workload to determine a duration when SLO allotment defined by the plurality of performance parameters is being under-utilized; adjusting automatically the SLO allotment for the workload during the duration when the SLO allotment is under-utilized; and re-allocating automatically the available performance capacity of a resource used by the workload to another workload whose assigned SLO is not being under-utilized.

Abstract: Methods and systems for a networked storage system are provided. One method includes categorizing by a processor performance data associated with a resource used in a networked storage environment for reading and writing data at a storage device based on a workload mix, where the workload mix is determined by a service time in which the resource processes the workload mix, a parameter indicating variability of the service time and a utilization bin index value indicating resource utilization at a given time; and determining by the processor available performance capacity of the resource using the categorized performance data, where the available performance capacity is based on optimum utilization of the resource and utilization of the resource.

Abstract: Methods and systems for a networked storage system are provided. One method includes receiving a resource identifier identifying a resource of a network storage environment as an input to a processor executable application programming interface (API); and predicting available performance capacity of the resource by using an optimum utilization of the resource, a current utilization and a predicted utilization based on impact of a workload change at the resource, where the optimum utilization is an indicator of resource utilization beyond which throughput gains for a workload is smaller than increase in latency in processing the workload.