Comm Vault Systems, Inc.

Tinton Falls, Inc.

Rajiv Kottomtharayil

Parag Gokhale

Anand Prahlad

Manoj Kumar Vijayan

Rahul S Pawar

Marcus S Muller

Srinivas Kavuri

Deepak Raghunath Attarde

Varghese Devassy

Manoj Kumar Vijayan Retnamma

David Ngo

Amey Vijaykumar Karandikar

Jun Lu

Ho-Chi Chen

Henry Wallace Dornemann

Paramasivam Kumarasamy

Prakash Varadharajan

Yu Albert Wang

Arun Amarendran

Anand Vibhor

Sumer Dilip Deshpande

Manoj Vijayan Retnamma

Sri Karthik Bhagi

Jaidev Oppath Kochunni

Duncan Alden Littlefield

Jun H Ahn

Brian Brockway

Ashwin Gautamchand Sancheti

Ananda Venkatesha

Arun Prasad Amarendran

Bhavyan Bharatkumar Mehta

Bheemesh R Dwarampudi

Robert Keith Brower, Jr

Suresh Parpatakam Reddy

Chong Liu

Zahid Ilkal

Jared Meade

Amit Mitkar

Andreas May

Vimal Kumar Nallathambi

Unmil Vinay Tambe

Satish Chandra Kilaru

Sanjay Harakhchand Kripalani

Mrityunjay Upadhyay

Paul Puthur Ignatius

Mahesh Tyagarajan

Ramachandra Reddy Ankireddypalle

Parminder Singh

William Katcher

Jeremy Alan Schwartz

Pavan Kumar Reddy Bedadala

Tirthankar Chatterjee

Kuldeep Kumar

Srikant Viswanathan

Manoj Kumar

Douglas Cary Swallow

David M Cunningham

Marcelo Dos Reis Mansano

Chitra Ramaswamy

Yanhui Lu

Michael Fasulo

Manas Bhikchand Mutha

Michael F Klose

Kamleshkumar K Lad

N Robert Hammer

Zahid Iikal

Rama Naga Bheemeswara Reddy Dwarampudi

Marcus B Muller

Manoj Kumar Retnamma Vijayan

Arun Prasad

Data storage operations, including content-indexing, containerized deduplication, and policy-driven storage, are performed within a cloud environment. The systems support a variety of clients and cloud storage sites that may connect to the system in a cloud environment that requires data transfer over wide area networks, such as the Internet, which may have appreciable latency and/or packet loss, using various network protocols, including HTTP and FTP. Methods are disclosed for content indexing data stored within a cloud environment to facilitate later searching, including collaborative searching. Methods are also disclosed for performing containerized deduplication to reduce the strain on a system namespace, effectuate cost savings, etc. Methods are disclosed for identifying suitable storage locations, including suitable cloud storage sites, for data files subject to a storage policy. Further, systems and methods for providing a cloud gateway and a scalable data object store within a cloud environment are disclosed, along with other features.

Data object store and server for a cloud storage environment

Virtual machine (VM) proliferation may be reduced through the use of Virtual Server Agents (VSAs) assigned to a group of VM hosts that may determine the availability of a VM to perform a task. Tasks may be assigned to existing VMs instead of creating a new VM to perform the task. Furthermore, a VSA coordinator may determine a grouping of VMs or VM hosts based on one or more factors associated with the VMs or the VM hosts, such as VM type or geographical location of the VM hosts. The VSA coordinator may also assign one or more VSAs to facilitate managing the group of VM hosts. In some embodiments, the VSA coordinators may facilitate load balancing of VSAs during operation, such as during a backup operation, a restore operation, or any other operation between a primary storage system and a secondary storage system.

Virtual server agent load balancing

A data protection scheduling system provides a flexible or rolling data protection window that analyzes various criteria to determine an optimal or near optimal time for performing data protection or secondary copy operations. While prior systems may have scheduled backups at an exact time (e.g., 2:00 a.m.), the system described herein dynamically determines when to perform the backups and other data protection storage operations, such as based on network load, CPU load, expected duration of the storage operation, rate of change of user activities, frequency of use of affected computer systems, trends, and so on.

Data protection scheduling, such as providing a flexible backup window in a data protection system

Certain embodiments described herein relate to an improved selective data backup system. In some embodiments, one or more components in an information management system can determine that a portion of the primary data scheduled for backup was previously backed up or is scheduled to be backed up as part of another backup operation. For example, a data agent performing a cluster-level backup operation for an entire cluster of storage servers may check whether any part of the primary data was previously backed up by a prior server-level backup operation for one of the storage servers in the cluster. If so, the data agent may skip, in the cluster-level backup operation, any portion of the primary data stored in the storage server previously backed up as part of the prior server-level backup operation.

Skipping data backed up in prior backup operations

A control application facilitates interactions with, and configurations of, the information management system that manages one or more client computing devices of an information management system. The control application may be implemented using a variety of modules that allow a user to interact with the information management system services in various ways. Using the control application, a user may view a backup status of the information management system, and view the backup status of specific client computing devices that are managed by the information management system. The user may also search for various entities and/or objects within the information management system using the control application. In addition, a user may view the status of one or more backup jobs configured within the information management system, as well as backup plans that have been established for one or more of the client computing devices.

Mobile control application for managing an information management system

Multiple data paths may be available to a data management system for transferring data between a primary storage device and a secondary storage device. The data management system may be able to gain operational advantages by performing load balancing across the multiple data paths. The system may use application layer characteristics of the data for transferring from a primary storage to a backup storage during data backup operation, and correspondingly from a secondary or backup storage system to a primary storage system during restoration.

Using different target storage devices in a backup storage system

An illustrative pseudo-file-system driver uses deduplication functionality and resources in a storage management system to provide an application and/or a virtual machine with access to a locally-stored file system. From the perspective of the application/virtual machine, the file system appears to be of virtually unlimited capacity. The pseudo-file-system driver instantiates the file system in primary storage, e.g., configured on a local disk. The application/virtual machine requires no configured settings or limits for the file system's storage capacity and may thus treat the file system as 'infinite.' The pseudo-file-system driver intercepts write requests and may use the deduplication infrastructure in the storage management system to offload excess data from local primary storage to deduplicated secondary storage, based on a deduplication database. The pseudo-file-system driver also intercepts read requests and in response may restore data from deduplicated secondary storage to primary storage, also based on the deduplication database.

Accessing a file system using tiered deduplication

Data object store and server for a cloud storage environment, including data deduplication and data management across multiple cloud storage sites

Described herein are techniques for better understanding problems arising in an illustrative information management system, such as a data storage management system, and for issuing appropriate alerts and reporting to data management professionals. The illustrative embodiments include a number of features that detect and raise awareness of anomalies in system operations, such as in deduplication pruning operations. Such anomalies can include delays in the processing of archive files to be deleted and/or delays in the generation of the list of archive files to delete. Anomalies are characterized by frequency anomalies and/or by occurrence counts. Utilization is also of interest for certain key system resources, such as deduplication databases, CPU and memory at the storage manager, etc., without limitation. Predicting low utilization periods for these and other key resources is useful for scheduling maintenance activities without interfering with ordinary deduplication pruning operations and/or other data protection jobs.

Anomaly detection in deduplication pruning operations

An information management system according to certain aspects can implement application archiving. The system may archive one or more applications on computing devices to make more storage space available on these devices. The system can determine which applications on client computing device to archive based on various factors. Some examples of factors can include frequency of use, application type, amount of application data and/or storage, user and/or device location, etc. The data to be archived can include one or more executable file(s), metadata, actual data, etc. After an application is archived, the system can generate a placeholder for the application; a placeholder can include information for restoring the archived application.

Archiving applications in information management systems

Exemplary systems and methods for archiving data objects using secondary copies are disclosed. The system creates one or more secondary copies of primary data that contains multiple data objects. The system may maintain a first data structure that tracks the data objects for which the system has created secondary copies and the locations of the secondary copies. To archive data objects in the primary data, the system identifies data objects to be archived, verifies that previously-created secondary copies of the identified data objects exist, and replaces the identified data objects with stubs. The system may maintain a second data structure that both tracks the stubs and refers to the first data structure, thereby creating an association between the stubs and the locations of the secondary copies.

Archiving data objects using secondary copies

An information management cell health monitoring system is provided herein that can monitor one or more information management systems, identify any performance issues that are occurring within an information management system, and automatically, or in response to a user input, transmit an instruction to the information management system to execute a workflow to resolve the performance issue(s). For example, the information management cell health monitoring system receives operational data, secondary copy policies, and/or similar data from an information management cells via a network. The information management cell health monitoring system analyzes the received information to identify any issues. If an issue is detected, the information management cell health monitoring system retrieves workflows and determines whether any of the workflows can be used to resolve the detected issue. The information management cell health monitoring system transmits a workflow that resolves the detected issue to the information management system for execution.

Information management cell health monitoring system

Virtual machine (VM) proliferation may be reduced by determining the availability of existing VMs to perform a task. Tasks may be assigned to existing VMs instead of creating a new VM to perform the task. Furthermore, a coordinator may determine a grouping of VMs or VM hosts based on one or more factors associated with the VMs or the VM hosts, such as VM type or geographical location of the VM hosts. The coordinator may also assign one or more Virtual Server Agents (VSAs) to facilitate managing the group of VM hosts. In some embodiments, the coordinators may facilitate load balancing of VSAs during operation, such as during a backup operation, a restore operation, or any other operation between a primary storage system and a secondary storage system.

Virtual machine load balancing

Described in detail herein are systems and methods for single instancing blocks of data in a data storage system. For example, the data storage system may include multiple computing devices (e.g., client computing devices) that store primary data. The data storage system may also include a secondary storage computing device, a single instance database, and one or more storage devices that store copies of the primary data (e.g., secondary copies, tertiary copies, etc.). The secondary storage computing device receives blocks of data from the computing devices and accesses the single instance database to determine whether the blocks of data are unique (meaning that no instances of the blocks of data are stored on the storage devices). If a block of data is unique, the single instance database stores it on a storage device. If not, the secondary storage computing device can avoid storing the block of data on the storage devices.

Block-level single instancing

Growing community of inventors

Marlboro, NJ, United States of America

Rajiv Kottomtharayil