Abstract: Performing changed sector backups on a volume to improve the throughput of incremental backups for block-based backups by overwriting only modified data on each changed block. Latency reduction is achieved by not overwriting the changed block as a whole. The previous backup is cloned and the batch file in the clone is updated by considering the incremental block extents. The incremental data write is performed only on the changed data of each incremental block from a couple of offset values from the previous backup's clone. Dynamic disk rather than differencing disk containers are used for snapshots. Use of a previous backup clone eliminates the need for temporary virtual disk containers, and fixed-size segmentation at the target reduces fragmentation and eliminates the need to enforce variable-sized segmentation.

Abstract: A vulnerability tagging process helps prioritize backups of datasets in a network by monitoring events that affect containers in the network. The monitored events are processed by an AI-based event analyzer to characterize each event in terms of a potential for destruction or damage to the data by each event. A vulnerability measure is calculated as the product of the number of occurrences of each event based on the severity associated with each event. Once the events are analyzed and the vulnerability scores are calculated, the scores are tagged on each workload. The vulnerability tags can then be utilized by the backup server to modify protection policies and/or prioritize backup schedules for the container workloads.

Abstract: A data protection system protects the data with the appropriate level of security and tiering for storage in data pools. The system includes a content awareness feature that is utilized to classify and analyze the data to be backed up and assigns the classified data to the appropriate data pool dynamically. It uses a classifying software development kit (SDK) or application program interface (API) to classify and analyze the contents. Based on this classification, data is segregated using specific data properties and is compared with a list of data pool properties. Then system then ranks and predicts the pool that is best suited for the classified data and dynamically assigns the data to the pool during backup.

Abstract: A policy level controller coordinates a scheduling and policy engine using a data change metric to dynamically schedule or re-define policies in response to data change rates in data assets in a current backup session. A supervised learning process trains a model using historical data of backup operations of the system to establish past data change metrics for corresponding backups processing the saveset, and modifies policies dictating the backup schedule by determining a data change rate of received data, as expressed as a number of bytes changed per unit of time. In response to input from backup targets regarding present usage, it then modifies the backup schedule to minimize the impact on backup targets that may be at or close to overload conditions.

Abstract: A dynamic data policy creation process utilizes certain supervised learning processes to classify data criticality for tagged clients to provide dynamic policy definitions and process adhoc (special) backup requests. Such embodiments prevent the need to manually determine criticality and create protection policies whenever a new data object into the system. A data tagger and KNN-based classifier provide an intelligent solution to data protection ecosystems to meet the dynamic request of data objects through a dynamic backup policy creation system and method that uses certain artificial intelligence (AI) and machine learning (ML) based solutions.

Abstract: A container load balancer process helps schedule backups of containerized data based on defined attributes and historical data. Containers are classified using a KNN-based classifier based on attributes. A tagger component assigns a priority tag to each container. A monitor monitors an assignment of backup tasks to proxies for the backing up step, and a load balancer determines if the assignment distributes backup loads within a defined performance tolerance, and adjusts the assignment if not. A backup server then backs up the container data in an order determined by the classifying and the assignment or adjusted assignment.

Abstract: A container prioritization process helps schedule backups of containerized data based on defined attributes and historical data. Containers are classified using a KNN-based classifier based on attributes. A tagger component assigns a priority tag to each container. Containers are backed up by a backup server through a schedule based on the priority tags of the containers. New containers are automatically classified and tagged within the prioritization schedule using the KNN-based classifier.

Abstract: The backup schedule for a group of routine or known clients and data objects is generally defined by default backup policies and priorities, and such policies and data assets demand resources of the system. The resource allocation among data assets being processed is analyzed and reallocated (tuned) as necessary to ensure that critical data assets are provided with the necessary resources for their protection operations. Embodiments use a machine learning (ML) model that would identify the resource consumption for the all the running policies to smartly allocate the optimal number of resources based on the criticality of the data object and their policies.

Abstract: A data protection system utilizes certain supervised learning processes to implement a granular level prioritized protection scheme for critical data based on decision tree processing. This process provides intelligent backup protection not only at the asset or sub-asset level, but also at the molecular level to meet the desired level of priority-based operation. Such embodiments overcome disadvantages of current systems that provide this level protection only upon management by an administrator by providing automated intelligence to provide granular protection at any appropriate data object level.

Abstract: A dynamic replication service using a data change metric to select an optimum cloning method that reduces latency of data copying. A model is trained using historical data of backup operations of the saveset to establish past data change metrics for corresponding replication services processing the saveset. The best cloning method for the replication service is selected by using a calculated data change rate of the data saveset, as expressed as a number of bytes changed per unit of time, from among a plurality of different cloning methods based on the data change rate. The service executes the selected cloning method for the replication service to copy the data for storage or further processing.

Abstract: A data protection system implements a Naïve Bayes classifier-based server health resiliency process that greatly helps the amount of time needed to resolve any health-based issue in the server. The Naive Bayes is an example of a simple classifier that classifies based on probabilities of problematic or potential failure causing events. This helps empower vendor applications to intelligently identify automatically resolve these flaws without the need for vendor personnel on the customer environment. Such a process uses historical cases and trains machine learning models in such a way that troubleshooting, log analysis and recommendations will be done proactively to identify root causes of issue and identify and apply available and appropriate fixes and workarounds.

Abstract: A conflict resolution component uses data change measure to resolve conflicts when data assets with the same priority tags arrive simultaneously for protection processing. The data change measure quantifies the extent of modifications or updates made to the asset since a last backup. Assets with a higher data change measure are assigned a higher priority and processed ahead of others with the same priority tag. This reprioritization ensures that backup objects with more significant data changes are handled first. Such a system overcomes the issues associated with present methods backup queueing methods including random scheduling of data having the same priority tags or classifications.

Abstract: A data protection system uses machine learning to detect the cyber-attacks on a data protection system in advance to notify the user of possible attacks and also instigate any counter attacks to the best possible extent. The system trains a support vector machine model (SVM) to recognize a malware, or other type of attack before it comes into action against the system. This model learns the parameters of hazardous files or code to prepare the best model of attributes of such files to help block the malware proactively. It uses several independent variables as features to gain more accuracy to the threat detection. Some of the parameters include: rate of data change (Drastic/High/Low), attack vulnerability history, resource usage history, performance metrics, application hit ratio, and the like.

Abstract: A vulnerability tagging process helps prioritize backups of datasets in a network by monitoring events that affect containers in the network. The monitored events are processed by an AI-based event analyzer to characterize each event in terms of a potential for destruction or damage to the data by each event. A vulnerability measure is calculated as the product of the number of occurrences of each event based on the severity associated with each event. Once the events are analyzed and the vulnerability scores are calculated, the scores are tagged on each workload. The vulnerability tags can then be utilized by the backup server to modify protection policies and/or prioritize backup schedules for the container workloads.

Abstract: Systems and methods for determining retention periods or policies for backups are disclosed. A rule book stores relationships between rules and recommended retention periods. Data related to a backup is collected and organized. A query is generated from the organized data and used to identify a rule from the rule book. The retention period corresponding to the identified rule in the rule book is then applied to the corresponding backup.

Abstract: A backup agent for generating backups includes a persistent storage and a backup manager. The persistent storage stores backup/restoration policies. The backup manager obtains production host computing resource characteristics associated with production hosts; performs a computing resource analysis of the production host computing resource characteristics to obtain resource profiles for each of the production hosts; performs an availability analysis of the obtained resource profiles to determine an application-level computing resources distribution for generating the backups; coordinates generating the backups using the application-level computing resource distribution and the backup/restoration policies to obtain the backups; and stores the obtained backups in backup storage.

Abstract: Systems and methods for determining retention periods or policies for backups are disclosed. A rule book stores relationships between rules and recommended retention periods. Data related to a backup is collected and organized. A query is generated from the organized data and used to identify a rule from the rule book. The retention period corresponding to the identified rule in the rule book is then applied to the corresponding backup.

Abstract: A backup agent for generating backups includes a persistent storage and a backup manager. The persistent storage stores backup/restoration policies. The backup manager obtains production host computing resource characteristics associated with production hosts; performs a computing resource analysis of the production host computing resource characteristics to obtain resource profiles for each of the production hosts; performs an availability analysis of the obtained resource profiles to determine an application-level computing resources distribution for generating the backups; coordinates generating the backups using the application-level computing resource distribution and the backup/restoration policies to obtain the backups; and stores the obtained backups in backup storage.

Abstract: A decoupled backup solution for distributed databases across a failover cluster. Specifically, a method and system disclosed herein improve upon a limitation of existing backup mechanisms involving distributed databases across a failover cluster. The limitation entails restraining backup agents, responsible for executing database backup processes across the failover cluster, from immediately initiating these aforementioned processes upon receipt of instructions. Rather, due to this limitation, these backup agents must wait until all backup agents, across the failover cluster, receive their respective instructions before being permitted to initiate the creation of backup copies of their relative distributed database. Subsequently, the limitation imposes an initiation delay on the backup processes, which the disclosed method and system omit, thereby granting any particular backup agent the capability to immediately (i.e., without delay) initiate those backup processes.