Comm Vault Systems, Inc.

Amit Mitkar

Paramasivam Kumarasamy

Sumedh Pramod Degaonkar

Henry Wallace Dornemann

Rahul S Pawar

Andrei Erofeev

Sanjay Kumar

Ajay Venkat Nagrale

Satish Chandra Kilaru

Amit Bhaskar Ausarkar

Brahmaiah Vallabhaneni

Ashwin Gautamchand Sancheti

Jianwei Chen

Rajiv Kottomtharayil

Deepak Raghunath Attarde

Jun H Ahn

Amey Vijaykumar Karandikar

Dmitriy Borisovich Zakharkin

Sumer Dilip Deshpande

Muthusamy Senthilnathan

Pratik S Rana

Vikash Kumar

Sri Karthik Bhagi

Varghese Devassy

Sunil Kumar Gutta

Durga Prasad Chedalavada

Suma Seshadri

Shankarbabu Bhavanarushi

Manas Bhikchand Mutha

Vijay H Agrawal

Unmil Vinay Tambe

Vinit Dilip Dhatrak

Rajesh Kumar Singh

Rahil Mohmed Husein Mansuri

Srikanth Hejamadi Tati

Arun Prasad Amarendran

Gopikannan Venugopalsamy

Srinivas Govind Kundaram

Rohit Himmatrao Dikkar

This application describes a process where a system receives a cloning request to replicate the functionality of a primary computing device onto a virtual machine. Initially, the system generates or identifies a non-production, point-in-time copy of the primary device's data and metadata. Utilizing this copy, it assesses the primary device's current configuration and resource usage metrics. Subsequently, it identifies an appropriate hosting destination for the virtual machine, equipped with necessary hardware processors. The process further involves defining an optimal configuration for the virtual machine, which may be distinct from the original device, based on various parameters. The virtual machine is then provisioned at the chosen location according to this tailored configuration. The system ensures the virtual machine has access to the original device's data and metadata through specially provisioned virtual disks. Additionally, it implements specific information management policies on the virtual disks' data and metadata to maintain integrity and security.

Replicating functionality and data of a primary computing device onto a virtual machine

Data protection resources are automatically scaled to the needs of data source(s) in an application orchestrator computing environment, such as a cluster in a Kubernetes deployment. The approach is adaptable to data sources in production clusters or application suites that are not application orchestrator deployments, such as a cloud-based database-as-a-service (DBaaS). A data storage management system protects cluster-based data with an elastic number of data protection resources (e.g., data agents, media agents), which are deployed on demand. The number of data protection resources deployed for a particular job are appropriate to the workload(s) at present and depend on a variety of scaling factors. In some embodiments, data protection resources are deployed within the same cluster as the data sources. In other embodiments, a separate infrastructure cluster provides the data protection resources on demand, and connects to any number and types of data sources, whether cloud-based or otherwise, without limitation.

Scaled deployment of data protection resources for data originating in an application orchestrator

During a data protection operation, a data storage system can collect computing attributes associated with hosting of an application generating data being backed up and may also obtain information relating to physical characteristics of the computing device hosting the application and the data. At the time of migration to a cloud, the system can use the collected attributes and/or information to provision cloud resources. The cloud storage system may accept provisioning requests, and may provide several templates which each specify a cloud resource that can be provisioned upon request. The system may compare the collected attributes with the attributes of the available templates to determine the best match. Then, the system may issue a provisioning request to the cloud provider according to the best-matched template. After provisioning the resources, the system may restore backup data of the application to the cloud storage system to migrate the data.

Migration to cloud storage

Recovery points can be used for replicating a virtual machine and reverting the virtual machine to a different state. A filter driver can monitor and capture input/output commands between a virtual machine and a virtual machine disk. The captured input/output commands can be used to create a recovery point. The recovery point can be associated with a bitmap that may be used to identify data blocks that have been modified between two versions of the virtual machine. Using this bitmap, a virtual machine may be reverted or restored to a different state by replacing modified data blocks and without replacing the entire virtual machine disk.

Time-based virtual machine reversion

A 'backup services container' comprises “backup toolkits,” which include scripts for accessing containerized applications plus enabling utilities/environments for executing the scripts. The backup services container is added to Kubernetes pods comprising containerized applications without changing other pod containers. For maximum value and advantage, the backup services container is “over-equipped” with toolkits. The backup services container selects and applies a suitable backup toolkit to a containerized application to ready it for a pending backup. Interoperability with a proprietary data storage management system provides features that are not possible with third-party backup systems. Some embodiments include one or more components of the proprietary data storage management within the illustrative backup services container. Some embodiments include one or more components of the proprietary data storage management system in a backup services pod configured in a Kubernetes node. All configurations and embodiments are suitable for cloud and/or non-cloud computing environments.

Backup of containerized applications using a backup services container and a backup services container-orchestration pod

A streamlined approach analyzes block-level backups of VM virtual disks and creates both coarse and fine indexes of backed up VM data files in the block-level backups. The indexes (collectively the 'content index') enable granular searching by filename, by file attributes (metadata), and/or by file contents, and further enable granular live browsing of backed up VM files. Thus, by using the illustrative data storage management system, ordinary block-level backups of virtual disks are “opened to view” through indexing. Any block-level copies can be indexed according to the illustrative embodiments, including file system block-level copies. The indexing occurs offline in an illustrative data storage management system, after VM virtual disks are backed up into block-level backup copies, and therefore the indexing does not cut into the source VM's performance. The disclosed approach is widely applicable to VMs executing in cloud computing environments and/or in non-cloud data centers. The illustrative content indexing is accomplished without restoring the VM data files being indexed to a staging location.

Content indexing of files in virtual disk block-level backup copies

A method and system for communicating with IoT devices to gather information related to device failure or error(s) is disclosed. The system makes a copy of at least a portion of the device's non-volatile memory and/or receives IoT device data (e.g., sensor data and/or log files etc.) from an IoT device that recently failed. The system determines which log files and/or sensor data, for example, the IoT device created before and/or after a failure. After gathering this information, the system stores the information in a database, sends it to the IoT device manufacturer, for further analysis and diagnostics to troubleshoot the failure and send a fix or software update to the IoT device.

Management of internet of things devices

A method and system for communicating with IoT devices to gather information related to device operation or performance is disclosed. The system makes a copy of at least a portion of the device's non-volatile memory and/or receives IoT device data (e.g., sensor data and/or log files etc.) from an IoT device. The system determines which log files and/or sensor data, for example, the IoT device created. After gathering this information, the system stores the information, or may send it to a storage destination for further analysis. The information may also be placed into secondary storage to comply with regulatory, insurance, or legal purposes.

Managing data from internet of things (IoT) devices

According to certain aspects, a system may include a data agent configured to: process a database file residing on a primary storage device(s) to identify a subset of data in the database file for archiving, the database file generated by a database application; and extract the subset of the data from the database file and store the subset of the data in an archive file on the primary storage device(s) as a plurality of blocks having a common size; and at least one secondary storage controller computer configured to, as part of a secondary copy operation in which the archive file is copied to a secondary storage device(s): copy the plurality of blocks to the secondary storage devices to create a secondary copy of the archive file; and create a table that provides a mapping between the copied plurality of blocks and corresponding locations in the secondary storage device(s).

Restoring archived database data

An illustrative approach accelerates file indexing operations for block-level backup copies in a data storage management system. A cache storage area is maintained for locally storing and serving key data blocks, thus relying less on retrieving data on demand from the backup copy. File indexing operations are used for populating the cache storage area for speedier retrieval during subsequent live browsing of the same backup copy, and vice versa. The key data blocks cached while file indexing and/or live browsing an earlier backup copy help to pre-fetch corresponding data blocks of later backup copies, thus producing a beneficial learning cycle. The approach is especially beneficial for cloud and tape backup media, and is available for a variety of data sources and backup copies, including block-level backup copies of virtual machines (VMs) and block-level backup copies of file systems, including UNIX-based and Windows-based operating systems and corresponding file systems.

Cache management for faster live browsing and file indexing of backup copies, including block-level backup copies of virtual machines and/or file systems

An illustrative file indexing approach enhances what was previously possible with hypervisor-free live browsing of virtual machine (VM) block-level backup copies. Capabilities are described for indexing files discovered in VM block-level backup copies, including file content. The illustrative file indexing functionality activates a live-browse session to discover files present within VM block-level backup copies and indexes file names and directory structures as created by an original source VM, resulting in an illustrative file index. The illustrative file indexing functionality optionally indexes file contents within VM block-level backup copies, resulting in an illustrative content index. The file index and content index are retained in persistent data structure(s) stored apart from the VM block-level backup copies. The indexes are searchable without mounting or live-browsing the VM block-level backup copies. In some embodiments the file index and the content index are consolidated. An enhanced storage manager is also disclosed.

File indexing for virtual machine backups in a data storage management system

Protecting configuration data in a clustered container system may include, in some embodiments, protecting an ETCD data store in a Kubernetes cluster. A data storage management system addresses the unique needs of protecting an ETCD data store of a target Kubernetes cluster, as well as protecting non-ETCD data payloads. The illustrative data storage management system defines ETCD as a unique kind of workload. ETCD protection is integrated within the data storage management system, which automatically creates data structures and resources within the system for, and provides special-purpose features to protect, ETCD contents and associated security certificates. One of the special-purpose features deploys a temporary data transfer agent within the target Kubernetes cluster to safeguard an ETCD snapshot and transmit its contents, along with the security certificates, to a backup infrastructure that operates outside of the target Kubernetes cluster. The backup infrastructure comprises components deployed by the data storage management system.

Protecting configuration data in a clustered container system

Container images may be generated from a backup system that includes a backup of one or more applications from a computing system of an entity. During a backup process, an application can be identified and its storage location in a secondary storage can be tracked or saved in a backup index. Configuration information and data or files created by user interaction with the application can be backed up and the location of the backed up data or files may be stored in the backup index along with the location of the configuration information. Using the backup index, a container image can be created that includes a selected application, its configuration information, and data, if any, created by the application. The container image can be generated from the backup stored in the secondary storage.

Backup index generation process

Content indexing of files in block-level backup copies of virtual machine data

During a data protection operation, a data storage system can collect computing attributes associated with hosting of an application generating data being backed up and may also obtain information relating to physical characteristics of the computing device hosting the application and the data. At the time of migration to a cloud, the system can use the collected attributes and/or information to provision cloud resources. For instance, the cloud storage system may accept provisioning requests, and may provide several templates which each specify a cloud resource that can be provisioned upon request, such as a cloud computing resource. The system can compare the collected attributes with the attributes of the available templates to determine the best match. Then, the system can issue a provisioning request to the cloud provider according to the best-matched template. After provisioning the resources, the system can restore backup data of the application to the cloud storage system to migrate the data.

Growing community of inventors

Manalapan, NJ, United States of America

Amit Mitkar