Comm Vault Systems, Inc.

Pavan Kumar Reddy Bedadala

Prakash Varadharajan

Manas Bhikchand Mutha

Gopikannan Venugopalsamy

Manoj Kumar Pradhan

Hemant Mishra

Vinit Dilip Dhatrak

Dmitriy Borisovich Zakharkin

Hetalkumar N Joshi

Divakar Radhakrishnan

Rohit Sivadas

Jayasree Yakkala

Paramasivam Kumarasamy

Deepak Raghunath Attarde

Satish Chandra Kilaru

Sanath Kumar

Sunil Babu Telagamsetti

Anand Prahlad

Rahul S Pawar

Christopher A Alonzo

Marcelo Dos Reis Mansano

Prosenjit Sinha

Pavlo Mazur

Rajiv Kottomtharayil

Mrityunjay Upadhyay

Hetal Kapadia

Parag Gokhale

Jun H Ahn

Anand Vibhor

Bheemesh R Dwarampudi

Praveen Veeramachaneni

Vibhor Mishra

Ankur Varsheny

Jatin Kirtikumar Sanghvi

Jie Chen

Bhavyan Bharatkumar Mehta

Rajesh Polimera

Vinay K Pamarthi

Priya Sundaresan

Prasanna Kumar Thoppe Ravindran

Supreeth Sanur

Jugpreet Singh Talwar

Ganesh Kumar Subramanian

N Robert Hammer

Hari Prasad Ravula

Surya Prakash Ramachandran

Neha Mathur

Aakash Chakravarthy Vijayakumar

Shilpa Nagendra

Certain embodiments disclosed herein reduce or eliminate a communication bottleneck at the storage manager by reducing communication with the storage manager while maintaining functionality of an information management system. In some implementations, a client obtains information for enabling a secondary storage job (e.g., a backup or restore) from a storage manager and stores the information (which may be referred to as job metadata) in a local cache. The client may then reuse the job metadata for multiple storage jobs reducing the frequency of communication with the storage manager. When a configuration of the information management system changes, or the availability of resources changes, the storage manager can push updates to the job metadata to the clients. Further, a client can periodically request updated job metadata from the storage manager ensuring that the client does not rely on out-of-date job metadata.

Network storage backup using distributed media agents

This application relates to targeted search of backup data. A data storage system can provide a targeted search of backup data based on events associated with the backup data. Upon receiving a search query that identifies an event stored in an event database, the data storage system can determine an event location and an event time associated with the identified event by accessing the event database and output a search result including a portion of the backup data that is associated with the event location and the event time.

Targeted search of backup data using calendar event data

Indexing preferences generally associate each data source with a type of indexing technology and/or with an index/catalog and/or with a computing device that hosts the index/catalog for tracking backup data generated from the source data. Indexing preferences govern which index/catalog receives transaction logs for a given storage operation. Thus, indexing destinations are defined granularly and flexibly in reference to the source data. Load balancing without user intervention assures that the various index/catalogs are fairly distributed in the illustrative backup systems by autonomously initiating migration jobs. Criteria for initiating migration jobs are based on past usage and going-forward trends. An illustrative migration job re-associates data sources with a different destination media agent and/or index/catalog, including transferring some or all relevant transaction logs and/or indexing information from the old host to the new host.

Heterogeneous indexing and load balancing of backup and indexing resources

Certain embodiments described herein relate to an improved disk usage growth prediction system. In some embodiments, one or more components in an information management system can determine usage status data of a given storage device, perform a validation check on the usage status data using multiple prediction models, compare validation results of the multiple prediction models to identify the best performing prediction model, generate a disk usage growth prediction using the identified prediction model, and adjust the available space of the storage device according to the disk usage growth prediction.

Disk usage growth prediction system

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

Systems and methods for storage pruning can enable users to delete, edit, or copy backed up data that matches a pattern. Storage pruning can enable fine-grain deletion or copying of these files from backups stored in secondary storage devices. Systems and methods can also enable editing of metadata associated with backups so that when the backups are restored or browsed, the logical edits to the metadata can then be performed physically on the data to create a custom restore or a custom view. A user may perform operations such as renaming, deleting, modifying flags, and modifying retention policies on backed up items. Although the underlying data in the backup may not change, the view of the backup data when the user browses the backup data can appear to include the user's changes. A restore of the data can cause those changes to be performed on the backup data.

Secondary storage editor

An improved information management system is described herein that can use artificial intelligence to classify data files (e.g., emails, documents, audio files, video files, etc.) and/or surface in a user interface the classification assigned to the data files. For example, a lightweight training or a heavyweight training can be employed to train classifiers to classify the data files. Use of artificial intelligence to classify data files may reduce the amount of computing resources that the information management system allocates to the data file review process because an auditor may be able to quickly identify those data files that meet the desired criteria using the classification and only request access to those data files. Thus, the information management system may be able to allocate more computing resources to normal or routine tasks, ensuring that such tasks are completed and/or completed in an appropriate amount of time.

Automated email classification in an information management system

Techniques for enabling user search of content stored in a file archive include providing a search interface comprising a search rules portion and an action rules portion, receiving a file archive search criterion comprising at least one search rule, and searching the file archive using the search criterion. The techniques also include generating a set of files filtered using the search criterion and performing an action specified in the action rules portion on a file included in the set of files.

Search filtered file system using secondary storage, including multi-dimensional indexing and searching of archived files

Failover systems and methods for performing backup operations, including heterogeneous indexing and load balancing of backup and indexing resources

A method and system described herein for classifying data of virtual machines in a heterogeneous computing comprising virtual machines and non-virtual machines. The system may access a secondary copy of data stored by a virtual machine, create metadata associated with that data, store the metadata in an index that comprises metadata associated with data stored on non-virtual machines, using a journal file, determine modified data objects within the data stored by the virtual machine, access or create metadata associated with modified data objects, and update the index accordingly.

Classification of virtualization data

Multi-tiered backup indexing

Certain embodiments disclosed herein reduce or eliminate a communication bottleneck at the storage manager by reducing communication with the storage manager while maintaining functionality of an information management system. In some implementations, operations performed as part of a backup process may be stored in transaction logs. These transaction logs may include information about a transaction performed between the client computing system and the network storage that hosts the backup of the client computing system. The transaction logs may be provided to a secondary storage system that can be used to form a backup index. The backup index may be used to facilitate accessing the data stored at the network storage. Advantageously, generating the transaction logs and separating the generation of the backup index from the backup process can reduce resource usage during performance of the backup and speed up the backup process while further reducing interaction with the storage manager.

Transaction log index generation in an enterprise backup system

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Piscataway, NJ, United States of America

Pavan Kumar Reddy Bedadala