Comm Vault Systems, Inc.

Prasanna Kumar Thoppe Ravindran

Rajesh Polimera

Supreeth Sanur

Henry Wallace Dornemann

Jie Chen

Pavan Kumar Reddy Bedadala

Santhosh Sanakkayala

Priya Sundaresan

Vinay K Pamarthi

Sanket Vijay Amrutkar

Aakash Chakravarthy Vijayakumar

Neha Mathur

Surya Prakash Ramachandran

Hari Prasad Ravula

Ganesh Kumar Subramanian

Jugpreet Singh Talwar

Shilpa Nagendra

A data storage management system comprises features for initiating failover orchestration jobs that invoke recovery resources on demand in a cloud computing environment. Backed up data that is stored persistently in the cloud computing environment may be rapidly restored within the cloud computing environment for use in disaster recovery and/or in test and verification scenarios. This approach may be contrasted to systems where a failover system is 'always on' at the failover destination, such as having failover resources always up and running in the cloud computing environment. Such resources typically include a failover virtual machine (VM), a virtual machine datastore for the restored data, and one or more computing resources for restoring an auxiliary copy to the VM's datastore. The cloud-based failover resources are deactivated or taken down once the failover event ends.

Cloud-based recovery of backed up data using auxiliary copy replication and on-demand failover resources

Systems described herein may dynamically add one or more proxy data protection agents to a cloud data storage system to process a data protection job. Upon completion of the job or at some other appropriate interval, the system can power down and decommission the proxy data protection agents and/or the virtual machines on which the data protection proxies reside according to a cleanup schedule (e.g., at hourly or minute intervals). In order to improve the allocation of computing resources, the system takes into account currently existing proxies or virtual machines when processing a backup request to determine the need for new proxies to service the backup request. In this manner the system can save costs and computing resources through efficient virtual machine deployment and retirement.

Data protection component scaling in a cloud- based data storage system

Data protection component scaling in a cloud-based data storage system

The disclosed technology can receive a voice query or text query in a natural language and translate it from natural language to a native database management language to respond to the query. For example, a human can ask his or her computer to 'show large emails from December 2016', and a data agent on computer can receive the voice request, convert audio associated with the voice to words in natural language, convert natural language into a SQL query, and convert the SQL query into a database management query. The data agent is trained with a corpus of technical documents and rules to determine the intent or keywords for answering the query. In some implementations, the disclosed technology can also include a chatbot and/or administrative assistant to enable a human to interface with a database management software using voice or text.

Natural language processing integrated with database and data storage management

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Eatontown, NJ, United States of America

Prasanna Kumar Thoppe Ravindran