Netapp, Inc.

Sun Microsystems, Inc.

Shankar Pasupathy

Garth Richard Goodson

Timothy Clayton Bisson

Kaladhar Voruganti

Sudhir Srinivasan

Minglong Shao

Steven Robert Kleiman

Kiran Nenmeli Srinivasan

Ravi Kumar Kavuri

Mark Walter Storer

Nam Le

Andrew W Leung

Ross Ackerman

Vijayan Prabhakaran

Siamak Nazari

Paul Yuedong Mu

Gokul Soundararajan

Shravan Gaonkar

Lakshmi Narayanan Bairavasundaram

Vipul Mathur

Prashanth Radhakrishnan

Sushil Thomas

Garrett R Mueller

Apoorva Sareen

Thirumale Niranjan

William Earl Taylor, Jr

Xiaoqin Ma

Amod Dani

Maneesh Abraham

Deepak Viswanath

Samya Maiti

Scott Dawkins

Shubhvardhan Manjayya

Yuvraj Patel

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.

Methods and systems for providing cloud based micro-services

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.

Artificial intelligence and machine learning systems and methods for a storage system

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.

In-line policy management with multi-level object handle

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.

Techniques for visualizing storage cluster system configurations and API therefore

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.

Techniques for visualizing storage cluster system configurations and events

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.

Methods for managing storage in a data storage cluster with distributed zones based on parity values and devices thereof

Object location service for network-based content repository

Distributed object store for network-based content repository

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.

System and method for improving the relevance of search results using data container access patterns

Using append only log format in data storage cluster with distributed zones for determining parity of reliability groups

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.

Distributed deduplication using global chunk data structure and epochs

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.

Dynamic storage management using virtual storage appliances

A method and apparatus for proxying search requests for a storage system and maintaining a central index for performing the search requests is described herein. An index manager on the storage system may initially produce the central index by examining each file in a file system and update the central index thereafter by examining only those files that have changed since the central index was initially produced or last updated. The index manager may receive a changed file list from a differencing layer configured for comparing snapshots of the file system at different time points to produce changed file lists. A search proxy module may receive search requests in a search protocol and proxy the search requests to a search engine by converting the search requests to another search protocol compatible with the search engine. The search engine may then use the central index for performing the search request.

Proxying search requests for files to a search engine

Methods and system for securely capturing workloads at a live network for replaying at a test network. The disclosed system captures file system states and workloads of a live server at the live network. In one embodiment the captured data is anonymized to protect confidentiality of the data. A file system of a test server at the test network is mirrored from a captured state of the live server. An anonymized version of the captured workloads is replayed as a request to the test server. A lost or incomplete command is recreated from the states of the live server. An order of the commands during replay can be based on an order in the captured workload, or based on a causal relationship. Performance characteristics of the live network are determined based on the response to the replayed command.

Method and system for securely capturing workloads at a live network for replaying at a test network

Growing community of inventors

Sunnyvale, CA, United States of America

Shankar Pasupathy