Abstract: Methods and systems for a networked storage environment are provided. As an example, one method includes registering a resource model with an application programming interface (API) server, where the resource model includes information regarding a plurality of resources used for storing data in a networked storage system having a plurality of storage devices; executing a data source service by the API server for obtaining information regarding any of the plurality of resources of the networked storage system, where the data source service obtains information from different data sources associated with different storage system platform types; and in response to a request, providing information regarding any metrics that are used for monitoring the plurality of resources by the API server.

Abstract: A method, non-transitory computer readable medium, and device that manages API requests includes receiving an API request to obtain a list of storage volumes from one or more storage devices, wherein the received API request is non-compatible with the API server computing device or the one or more storage devices. The received API request is scanned to identify a service type associated with the received request. Next, one or more service instances associated with the identified service type are identified. The list of storage volumes from the one or more storage devices using information from at least one service instance of the identified one or more service instances without converting the received API request is provided.

Abstract: Methods and systems for a networked storage environment are provided. For example, one method includes generating by a processor in response to a request, a storage service level class (SLC) defined by a storage attribute and a protection SLC defined by a protection attribute for a storage item managed by a storage server, where the storage attribute provides a performance level for the storage item and the protection attribute provides a protection level for the storage item; identifying by the processor, a first resource for complying with the storage attribute of the storage SLC for storing the storage item; configuring a second resource for complying with the protection attribute of the protection SLC for storing information associated with the storage item; and monitoring the first resource for compliance with the storage attribute for the storage SLC.

Abstract: A system and method for transacting data using meta-models to express data relationships is provided. In some embodiments, the method comprises requesting, from a computing system, a meta-model expressing a data relationship. The data relationship relates to a topology of a deployed computing system, and the requesting specifies the meta-model from among a plurality of data models stored within the computing system. The requested meta-model is received from the computing system, and at least a portion of the meta-model is provided to an application. A data transaction based on the meta-model is received from the application and issued. In some such embodiments, a navigational interface is provided in order to navigate the meta-model and specify the portion to provide.

Abstract: Methods and systems for a networked storage environment are provided. For example, one method includes generating by a processor in response to a request, a storage service level class (SLC) defined by a storage attribute and a protection SLC defined by a protection attribute for a storage item managed by a storage server, where the storage attribute provides a performance level for the storage item and the protection attribute provides a protection level for the storage item; identifying by the processor, a first resource for complying with the storage attribute of the storage SLC for storing the storage item; configuring a second resource for complying with the protection attribute of the protection SLC for storing information associated with the storage item; and monitoring the first resource for compliance with the storage attribute for the storage SLC.

Abstract: Methods and systems for a networked storage environment are provided. As an example, one method includes registering a resource model with an application programming interface (API) server, where the resource model includes information regarding a plurality of resources used for storing data in a networked storage system having a plurality of storage devices; executing a data source service by the API server for obtaining information regarding any of the plurality of resources of the networked storage system, where the data source service obtains information from different data sources associated with different storage system platform types; and in response to a request, providing information regarding any metrics that are used for monitoring the plurality of resources by the API server.

Abstract: A method, non-transitory computer readable medium, and device that manages API requests includes receiving an API request to obtain a list of storage volumes from one or more storage devices, wherein the received API request is non-compatible with the API server computing device or the one or more storage devices. The received API request is scanned to identify a service type associated with the received request. Next, one or more service instances associated with the identified service type are identified. The list of storage volumes from the one or more storage devices using information from at least one service instance of the identified one or more service instances without converting the received API request is provided.

Abstract: Analysis is performed on a collection of data that is recorded for the storage system during a first time frame. The recorded collection of data includes a plurality of performance parameters that are determined from, for example, diagnostic tools that continually operate on the storage system. A set of baseline values are determined for each of the plurality of performance parameters by analyzing the recorded collection of data from an older portion of the time frame. For each parameter, a set of performance parameter values obtained from a recent portion of the time frame is compared to a corresponding baseline value of that performance parameter. From performing the comparison, one or more anomalies that are indicative of a particular problem on the storage system are determined for one or more of the plurality of performance parameters.

Abstract: A system and method for transacting data using meta-models to express data relationships is provided. In some embodiments, the method comprises requesting, from a computing system, a meta-model expressing a data relationship. The data relationship relates to a topology of a deployed computing system, and the requesting specifies the meta-model from among a plurality of data models stored within the computing system. The requested meta-model is received from the computing system, and at least a portion of the meta-model is provided to an application. A data transaction based on the meta-model is received from the application and issued. In some such embodiments, a navigational interface is provided in order to navigate the meta-model and specify the portion to provide.

Abstract: A method and an apparatus to manage data in a networked storage server have been disclosed. In one embodiment, the method includes providing a single logical layer between a number of logical subsystems in a storage server and a number of data management agents in the storage server, wherein the data management agents are operable to service requests from a number of client machines following different data management protocols, and exporting a set of application programming interfaces (APIs) from the single logical layer to interface with the data management agents.

Abstract: Methods, systems, and computer program products implementing a sizing infrastructure module that receives from a calling application program input requirements for a data storage system. A configuration for the data storage system is computed based on the input requirements, and the configuration is returned to the calling application program.

Abstract: Available performance capacities of storage servers and storage devices in a storage infrastructure are determined using a plurality of metrics, wherein each of the storage devices is managed by one of the storage servers. Each aggregate is hosted by a storage server and includes multiple storage devices. A relationship between the plurality of metrics is analyzed. An aggregate from which to allocate storage capacity to a volume from a plurality of aggregates is selected based on the available performance capacities and the relationship between the plurality of metrics. The selection is performed without information about properties of the volume. Storage capacity of the selected aggregate is automatically allocated to the volume. After said allocation, the plurality of aggregates is approximately load balanced.

Abstract: An apparatus and method for automatically load balancing one or more workload groups to a set of available physical resources of a data storage system, and generating a layout planning recommendation of the set of available physical resources that supports the one or more workload groups based on the load balancing.

Abstract: Available performance capacities of storage servers and storage devices in a storage infrastructure are determined using a plurality of metrics, wherein each of the storage devices is managed by one of the storage servers. Each aggregate is hosted by a storage server and includes multiple storage devices. A relationship between the plurality of metrics is analyzed. An aggregate from which to allocate storage capacity to a volume from a plurality of aggregates is selected based on the available performance capacities and the relationship between the plurality of metrics. The selection is performed without information about properties of the volume. Storage capacity of the selected aggregate is automatically allocated to the volume. After said allocation, the plurality of aggregates is approximately load balanced.

Abstract: A method and a system for modeling file system operation streams of a computing environment. File system operations of the file system operation streams are workloads produced by the devices of the computing environment. For example, a device of the computing environment produces the workloads, which are requests for information from a storage system of the computing environment. The requests are captured and form traces. The traces are then used as input to a model of the computing environment. The model is iteratively trained by using the traces to learn the dynamic behavior of the computing environment. Specifically, the model is trained to identify sequences of file system operations that are legal. Legal sequences can then be generated in realistic workloads that simulate the workloads produced by the devices of the computing environment to a high degree of probability.

Abstract: A system and method for real-time load balancing of user workload across a plurality of physical storage systems with shared back-end storage is provided. A load balancing process tracks usage metrics and determines a source and destination physical storage system and a virtual storage system (vfiler) to be migrated.

Abstract: A system and method for real-time load balancing of user workload across a plurality of physical storage systems with shared back-end storage is provided. A load balancing process tracks usage metrics and determines a source and destination physical storage system and a virtual storage system (vfiler) to be migrated.