Abstract: Methods and systems for a networked computing system are provided. One method includes deploying a micro-service associated with data stored by a networked storage system at a storage device, the micro-service deployed as a virtual machine of a cloud-based system having a data store, and an application programming interface (API) for providing analytic information associated with the data and resources of the networked storage system, where the cloud based system is accessible to a client system via a first network connection; processing performance data associated with the micro-service by a first computing system; storing the processed performance data for the micro-service at a storage volume accessible via a second network connection; copying the processed performance data at the data store by a data loader; and providing access to the processed performance data to the client system from the data store via the API and the first network connection.

Abstract: Computing technology using artificial intelligence/machine learning methods and systems for a storage system is provided. One method includes measuring by a processor, a first metric indicating health of a first storage system in storing and retrieving data; quantifying by the processor, the first metric based on comparison of the first metric with a same metric for a plurality of storage systems; identifying by the processor, a plurality of features potentially impacting the first metric, based on a predictive, machine-learning algorithm built on performance and configuration data for the plurality of storage systems; selecting, a first feature by the processor, based on impact of the first feature on the first metric; and generating a command by the processor for making a change to the first feature.

Abstract: Methods and systems for a networked computing system are provided. One method includes deploying a micro-service associated with data stored by a networked storage system at a storage device, the micro-service deployed as a virtual machine of a cloud-based system having a data store, and an application programming interface (API) for providing analytic information associated with the data and resources of the networked storage system, where the cloud based system is accessible to a client system via a first network connection; processing performance data associated with the micro-service by a first computing system; storing the processed performance data for the micro-service at a storage volume accessible via a second network connection; copying the processed performance data at the data store by a data loader; and providing access to the processed performance data to the client system from the data store via the API and the first network connection.

Abstract: Computing technology using artificial intelligence/machine learning methods and systems for a storage system is provided. One method includes measuring by a processor, a first metric indicating health of a first storage system in storing and retrieving data; quantifying by the processor, the first metric based on comparison of the first metric with a same metric for a plurality of storage systems; identifying by the processor, a plurality of features potentially impacting the first metric, based on a predictive, machine-learning algorithm built on performance and configuration data for the plurality of storage systems; selecting, a first feature by the processor, based on impact of the first feature on the first metric; and generating a command by the processor for making a change to the first feature.

Abstract: A distributed object store in a network storage system uses location-independent global object identifiers (IDs) for stored data objects. The global object ID enables a data object to be seamlessly moved from one location to another without affecting clients of the storage system, i.e., “transparent migration”. The global object ID can be part of a multilevel object handle, which also can include a location ID indicating the specific location at which the data object is stored, and a policy ID identifying a set of data management policies associated with the data object. The policy ID may be associated with the data object by a client of the storage system, for example when the client creates the object, thus allowing “inline” policy management. An object location subsystem (OLS) can be used to locate an object when a client request does not contain a valid location ID for the object.

Abstract: Various embodiments are generally directed to techniques for generating effective visualizations of some or all of a storage cluster system. An apparatus includes an API component of a visualization server to make an API available to be called by another device via a network to request information associated with an object that represents a component of a storage cluster system; and a translation component of the visualization server to, in response to a call to the API received via the network that requests information associated with the object, generate query instructions to search for a system entry corresponding to the storage cluster system within a system database and to search for the requested information within the system entry.

Abstract: Various embodiments are generally directed to techniques for generating effective visualizations of some or all of a storage cluster system. An apparatus includes a processor component; a rendering component to generate a visualization of at least a portion of a storage cluster system for presentation on a display, the visualization to comprise a depiction of an object that corresponds to a component of the storage cluster system; and an interpretation component to interpret received indications of operation of an input device to select the depicted object and to select a first time and a second time along a timeline presented on the display, and to generate a command to request information indicating a change in state of the object between the first and second times.

Abstract: A cluster configuration system arranged to manage a graph database for tracking and identifying a time-varying state of a cluster of objects. The graph database may include one or more nodes and one or more associations between the nodes to represent time-varying states of the cluster. Management of the graph database may include creating, maintaining, updating, storing, administrating, querying, and/or presenting one or more elements of the graph database.

Abstract: Techniques for a data storage cluster and a method for maintaining and updating reliability data and reducing data communication between nodes, are disclosed herein. Each data object is written to a single data zone on a data node within the data storage cluster. Each data object includes one or more data chunks, and the data chunks of a data object are written to a data node in an append-only log format. When parity is determined for a reliability group including the data zone, there is no need to transmit data from other data nodes where the rest of data zones of the reliability group reside. Thus, inter-node data communication for determining reliability data is reduced.

Abstract: Various embodiments are generally directed to techniques for generating effective visualizations of some or all of a storage cluster system. An apparatus includes a processor component; a rendering component to generate a visualization of at least a portion of a storage cluster system for presentation on a display, the visualization to comprise a depiction of an object that corresponds to a component of the storage cluster system; and an interpretation component to interpret received indications of operation of an input device to select the depicted object and to select a first time and a second time along a timeline presented on the display, and to generate a command to request information indicating a change in state of the object between the first and second times.

Abstract: Various embodiments are generally directed to techniques for generating effective visualizations of some or all of a storage cluster system. An apparatus includes an API component of a visualization server to make an API available to be called by another device via a network to request information associated with an object that represents a component of a storage cluster system; and a translation component of the visualization server to, in response to a call to the API received via the network that requests information associated with the object, generate query instructions to search for a system entry corresponding to the storage cluster system within a system database and to search for the requested information within the system entry.

Abstract: A distributed object store in a network storage system uses location-independent global object identifiers (IDs) for stored data objects. The global object ID enables a data object to be seamlessly moved from one location to another without affecting clients of the storage system, i.e., “transparent migration”. The global object ID can be part of a multilevel object handle, which also can include a location ID indicating the specific location at which the data object is stored, and a policy ID identifying a set of data management policies associated with the data object. The policy ID may be associated with the data object by a client of the storage system, for example when the client creates the object, thus allowing “inline” policy management. An object location subsystem (OLS) can be used to locate an object when a client request does not contain a valid location ID for the object.

Abstract: A distributed object store in a network storage system uses location-independent global object identifiers (IDs) for stored data objects. The global object ID enables a data object to be seamlessly moved from one location to another without affecting clients of the storage system, i.e., “transparent migration”. The global object ID can be part of a multilevel object handle, which also can include a location ID indicating the specific location at which the data object is stored, and a policy ID identifying a set of data management policies associated with the data object. The policy ID may be associated with the data object by a client of the storage system, for example when the client creates the object, thus allowing “inline” policy management. An object location subsystem (OLS) can be used to locate an object when a client request does not contain a valid location ID for the object.

Abstract: A distributed object store in a network storage system uses location-independent global object identifiers (IDs) for stored data objects. The global object ID enables a data object to be seamlessly moved from one location to another without affecting clients of the storage system, i.e., “transparent migration”. The global object ID can be part of a multilevel object handle, which also can include a location ID indicating the specific location at which the data object is stored, and a policy ID identifying a set of data management policies associated with the data object. The policy ID may be associated with the data object by a client of the storage system, for example when the client creates the object, thus allowing “inline” policy management. An object location subsystem (OLS) can be used to locate an object when a client request does not contain a valid location ID for the object.

Abstract: The techniques introduced here provide for efficient management of storage resources in a modern, dynamic data center through the use of virtual storage appliances. Virtual storage appliances perform storage operations and execute in or as a virtual machine on a hypervisor. A storage management system monitors a storage system to determine whether the storage system is satisfying a service level objective for an application. The storage management system then manages (e.g., instantiates, shuts down, or reconfigures) a virtual storage appliance on a physical server. The virtual storage appliance uses resources of the physical server to meet the storage related needs of the application that the storage system cannot provide. This automatic and dynamic management of virtual storage appliances by the storage management system allows storage systems to quickly react to changing storage needs of applications without requiring expensive excess storage capacity.

Abstract: Techniques for a data storage cluster and a method for maintaining and updating reliability data and reducing data communication between nodes, are disclosed herein. Each data object is written to a single data zone on a data node within the data storage cluster. Each data object includes one or more data chunks, and the data chunks of a data object are written to a data node in an append-only log format. When parity is determined for a reliability group including the data zone, there is no need to transmit data from other data nodes where the rest of data zones of the reliability group reside. Thus, inter-node data communication for determining reliability data is reduced.

Abstract: A method of performing a global deduplication may include: collecting a data chunk to be written to a backing storage of a storage system at a staging area in the storage system; generating a data fingerprint of the data chunk; sending the data fingerprint in batch along with other data fingerprints corresponding to data chunks collected at different times to a metadata server system in the storage system; receiving an indication, at the staging area, of whether the data fingerprint is unique in the storage system from the metadata server system; and discarding the data chunk when committing a data object containing the data chunk to the backing storage, when the indication indicates that the data chunk is not unique.

Abstract: A system and method for improving the relevance of search results using data container access patterns. An indexing process tracks data access patterns and updates an access data structure. When executing a search operation, a search process first identifies a set of data containers containing the search terms. The search process then utilizes the access data structure to rank the identified data containers based on the collected data access pattern information.

Abstract: Techniques for a data storage cluster and a method for maintaining and updating reliability data and reducing data communication between nodes, are disclosed herein. Each data object is written to a single data zone on a data node within the data storage cluster. Each data object includes one or more data chunks, and the data chunks of a data object are written to a data node in an append-only log format. When parity is determined for a reliability group including the data zone, there is no need to transmit data from other data nodes where the rest of data zones of the reliability group reside. Thus, inter-node data communication for determining reliability data is reduced.

Abstract: Techniques for a data storage cluster and a method for deduplicating data in the data storage cluster in a scalable manner, by (among other things) using an epoch-based global chunk data structure, are disclosed herein. A global chunk data structure for an epoch is distributed and maintained at a plurality of metadata nodes within the data storage cluster. Fingerprints and identifiers of data chunks are written to the cluster after a particular epoch are written to delta chunk data structures stored in different metadata nodes of the cluster. When the data storage cluster advances to the next epoch, the global chunk data structure is updated using the delta chunk data structures. At any given time, data deduplication in the data storage cluster can be conducted based on the global chunk data structure for the current epoch.