Abstract: Replication operations of replicating data from a production site to a replica site. The replication is performed using independent Merkle trees. The Merkle trees are updated asynchronously using Merkle trees that have been augmented with a time-based value. The synchronization is verified by comprising root hashes of the independent Merkle trees at certain points in time. The replication and Merkle trees are self-healing and trigger a resynchronization when a discrepancy is discovered.

Abstract: One example method includes ingesting data to a data protection system, separating, by the data protection system, the ingested data into groups according to Recovery Point Objective (RPO) such that each group is associated with a different respective RPO, storing the groups in respective storage pools, and each of the storage pools is associated with a respective one of the RPOs, and performing a respective garbage collection (GC) process at each storage pool.

Abstract: Systems and methods for protecting data including user data. A recommendation service is disclosed that receives verifiable claims from a user. A verifiable output is generated from the verifiable claims. The verifiable output can be provided to service providers and allows the service providers to personalize their services to the user. The user's data is protected and exposure of the user's data is controlled by the user.

Abstract: Any point in time backups for distributed consistency is disclosed. IOs from a consistency group are received by multiple aggregators and stored in corresponding journals. In response to a bookmark, the multiple journals are synthesized to create a do stream or to add the multiple journals to the do stream. A full synchronization operation can be performed simultaneously with replication operations.

Abstract: Run-time selection of optimal commands for accessing serial access resources is described. A system receives a request to access a serial access resource. The system identifies a first section to access in the serial access resource and a second section to access in the serial access resource which is separated from the first section to access by an intermediate section in the serial access resource. The system generates combinations of access commands associated with accessing the first and the second sections to access. The system estimates target values, based on locations of the first and the second sections to access in the serial access resource, corresponding to the combinations of access commands. The system selects a combination of access commands which corresponds to an optimal target value. The system enables performance of the selected combination of access commands on the first and the second sections in the serial access resource.

Abstract: One example method includes receiving input concerning a mobile IoT device, and the input includes information about a location of the mobile IoT device, information about whether the mobile IoT device is moving, and, when the mobile IoT device is moving, information about the range, speed, and bearing of the mobile IoT device. Next, the method includes generating a predicted location of the mobile IoT device based on the inputs received, using the predicted location of the mobile IoT device and a map of nodes in an environment where the mobile IoT device is located to make a migration decision concerning an application used by the mobile IoT device, and migrating the application from a present location to a node expected to be accessible by the mobile IoT device when the mobile IoT device reaches the predicted location, and the node and present location are physically separated by a distance.

Abstract: One example method includes performing delta operations to protect data. During a delta operation, a primary map and a secondary map are processed using bit logic. The bit logic determines how to handle data stored at a location on the volume associated with an entry in the primary map and included in the current delta operation when a new write for the same location is received as the corresponding entry in the primary map is processed.

Abstract: One example method includes performing delta operations to protect data. A delta queue is provided that allows a replica volume to be rolled forwards and backwards in time. When rolling the replica volume forward, an undo delta is created such that the replica volume can be moved backwards after being moved forward. When rolling the replica volume backwards, a forward delta is created such that the replica volume can be moved forwards after being moved backwards.

Abstract: A data protection system includes a splitter configured to reduce latencies when splitting writes in a computing environment. The splitter captures a write and adds metadata to augment the write with virtual related information. The augmented data is provided to a smartNIC while the write is then processed in the IO stack. The smartNIC may have a volume only visible to the splitter. The smartNIC also includes processing power that allows data protection operations to be performed at the smartNIC rather than with the processing resources of the host.

Abstract: Data protection operations including replication operations are disclosed. Virtual machines, applications, and/or application data are replicated according to at least one strategy. The replication strategy can improve performance of the recovery operation.

Abstract: On-the-fly point-in-time recovery operations are disclosed. During a recovery operation, the PiT being restored can be changed on-the-fly or during the existing recovery operation without restarting the recovery process from the beginning. In one example, this improves recovery time operation (RTO) and prevents aspects of the recovery operation to be avoided when changing to a different PiT.

Abstract: One example method includes intercepting an IO issued by an application, writing the IO and IO metadata to a splitter journal in NVM, forwarding the IO to storage, and asynchronous with operations occurring along an IO path between the application and storage, evacuating the splitter journal by sending the IO and IO metadata from the splitter journal to a replication site. In this example, sending the IO and IO metadata from the journal to the replication site does not increase a latency associated with the operations on the IO path.

Abstract: One example method includes intercepting an IO issued by an application, writing the IO and IO metadata to a splitter journal in NVM, forwarding the IO to storage, and asynchronous with operations occurring along an IO path between the application and storage, evacuating the splitter journal by sending the IO and IO metadata from the splitter journal to a replication site. In this example, sending the IO and IO metadata from the journal to the replication site does not increase a latency associated with the operations on the IO path.

Abstract: Methods and systems for managing distribution of inference models throughout a distributed system are disclosed. To manage distribution of inference models, a system may include a data aggregator and one or more data collectors. The data aggregator may obtain a threshold, the threshold indicating an acceptable inference error rate for an inference model. The data aggregator may obtain an inference model based on the threshold by training an inference model, performing a lookup in an inference model lookup table, or via other methods. The data aggregator may optimize the inference model to determine the minimum quantity of computing resources consumed by an inference model in order to generate inferences accurate within the threshold. In order to do so, the data aggregator may simulate the operation of more computationally-costly inference models and less computationally-costly inference models.

Abstract: Methods and systems for managing data collection are disclosed. A data aggregator may aggregate data collected by a data collector. To reduce computing resources used for aggregation, the data aggregator and data collector may use inferences provided by a twin inference model in place of data collected by the data collector rather than receiving copies of data from the data collector. Over time, the aggregated data may be revised using revised inference models that are revised using subsequently obtained data from the data collector. The revised inference models may be used to obtain revised inferences that may replace original inferences in the aggregated data. The revised inferences may be of higher accuracy due to differences in the data upon which the inference and revised inference models are based.

Abstract: Methods and systems for managing data collection are disclosed. A data aggregator may aggregate data collected by a data collector. To reduce computing resources used for aggregation, the data aggregator and data collector may implement a multi-stage data reduction processes to reduce the quantity of data transmitted for data aggregation purposes. The multi-stage data reduction process may include implementing twin inference models at the aggregator and collector, identifying relationships in the data collected by the data collector using feature relationship inference models, transmitting a portion of the collected data to the data aggregator and withholding a second portion of the collected data based on acceptable level of error for use of the collected data, and reconstructing the withheld portion of the collected data at the aggregator. The reconstructed portion of the collected data may include the acceptable level of error when compared to a corresponding portion of the collected data.

Abstract: Methods and systems for managing data aggregation in a distributed environment are disclosed. The data may be aggregated using twin inference models which may be used to reduce a quantity of data transmitted to aggregate the data. To obtain twin inference models, models may be trained which may consume computing resources. A computing resource cost for training the twin inference models may be estimated based on an estimated number of twin inferences models necessary to meet inference accuracy goals. A model training device that has an available quantity of computing resources sufficient to meet the computing resource cost may be obtained. The model training device may be used to train and distribute inference models for data aggregation purposes.

Abstract: Methods and systems for managing aggregation of data throughout a distributed environment are disclosed. To manage aggregation of data, a system may include a data aggregator and one or more data collectors. The data aggregator may obtain a threshold, the threshold indicating an acceptable error level associated with a downstream consumer of the aggregated data. The data aggregator may obtain the acceptable error level by simulating operation of the downstream consumer using synthetic data sets. The synthetic data sets may include different levels of error and, therefore, the data aggregator may determine a level of error that may impact the operation of the downstream consumer to an acceptable degree. In order to facilitate data aggregation, an inference model may be implemented that meets the threshold while consuming a minimum quantity of computing resources during operation.

Abstract: A primary storage array calculates signatures of chunks of production device data that are sent to a target device on a secondary storage array. The chunk signatures are sent to a signature device on the secondary storage array, where the chunk signatures are stored within the same LBA range on the signature device as their corresponding chunks are stored on the target device. Snaps of the target and signature device are created and associated as a snap pair. Later, the primary storage array calculates signatures of changed chunks of production device data that are sent to the target device. The changed chunk signatures are sent to the signature device. New snaps of the target and signature device are created and associated as a new snap pair. Chunk data is validated by calculating signatures of the chunks from the target device and comparing those signatures with the chunk signatures from the signature device.

Abstract: A data protection system configured to backup a time series database is provided. The data protection system may be integrated with or have access to consolidation policies of the time series database. The backup policy and backup retention policy are set by monitoring the consolidation policy and adjusting the backup policy to ensure that the data in the time series database is protected prior to being downscaled, discarded or otherwise consolidated.