Abstract: Data protection operations including replication operations that dynamically adapt a topology of replica virtual machines. A data protection system may implement a machine model that is trained using, as input, characteristics of virtual machines. When a failure is predicted, a topology of the replica virtual machines is changed. The topology may also change when changes in the environment are detected. The changes may include redistributing the protected applications to the replica virtual machines and/or scaling the replica virtual machines.

Abstract: One example method includes contacting, by a client, a service, receiving a credential from the service, obtaining trust information from a trust broker, comparing the credential with the trust information, and either connecting to the service if the credential and trust information match, or declining to connect to the service if the credential and the trust information do not match. Other than by way of the trust information obtained from the trust broker, the client may have no way to verify whether or not the service can be trusted.

Abstract: One example method includes performing delta operations to protect data. Each delta generated by a data protection operation includes data. The deltas are stored in a delta queue, when moving a current replica to another point in time represented by the selected delta in the delta queue, the deltas are processed so that all relevant data can be applied in a batch. This ensures that when the same extents are represented in multiple deltas, only the oldest version is applied to the replica volume to move the current replica to the selected point in time.

Abstract: Methods and systems for managing data collection in a distributed system are disclosed. The system may include a data aggregator and a data collector. The data aggregator may aggregate data collected by the data collector. To reduce the computing resources used to aggregate data, 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, transmitting a portion of the collected data to the data aggregator based on acceptable level of error for use of the collected data, and reconstructing the un-transmitted portion of the collected data at the aggregator. The reconstructed portion of the collected data may include the acceptable level of error.

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: A live image of a virtual machine is disclosed. When generating a live image, an image of a virtual machine is generated. A journal is also generated that starts before a start time of the image and ends after the start time of the image. A live image is then generated by synchronizing the start time of the image with the journal. This includes evaluating or analyzing the journal relative to the backup to determine or synchronize the start time relative to the entries in the journal.

Abstract: One example method includes encoding data as a polysaccharide structure, synthesizing the polysaccharide structure to create polysaccharide storage media that comprises the data, and storing the polysaccharide storage media. The example method may also include compressing the polysaccharide and storing the compressed data as a polysaccharide.

Abstract: Methods and systems for managing data collection are disclosed. To manage data collection, a system may include a data aggregator. The data aggregator may utilize inference models to predict the future operation of data collectors. To validate these inferences, the data aggregator may compare a data statistic (a reduced-size representation of a series of measurements) to a complementary data statistic based on a set of inferences. If the complementary data statistic is determined accurate, the data aggregator may store the inferences as validated data and operate as though it has access to the measurements from the data collector. By doing so, the system may be able to transmit less data, consume less network bandwidth, and consume less energy throughout a distributed system.

Abstract: Methods and systems for managing data collection are disclosed. To manage data collection, a system may include a data aggregator and a data collector. The data aggregator and data collector may utilize identical copies of a twin inference model to predict the future operation of the data collector. To minimize data transmission, the data collector may transmit a difference to the data aggregator. The data aggregator may reconstruct data from the data collectors using the difference from the data collector, and an inference generated by the copy of the twin inference model hosted by the data aggregator.

Abstract: Methods and systems for managing generalization of inference models throughout a distributed environment are disclosed. To manage generalization of inference models, a system may include a data aggregator and one or more data collectors. The data aggregator may obtain a similarity graph in order to determine the relationship between data obtained by one or more data collectors. The similarity graph may be used to obtain grouping for the data collectors. The data aggregator may train inference models to facilitate data collection by the data collectors included in the grouping.

Abstract: Methods and systems for managing data collection are disclosed. To manage data collection, a system may include a data aggregator and a data collector. The data aggregator may utilize complex inference models to predict the future operation of the data collector, while the data collector may host simpler inference models. The data collector may access inferences from the complex models by obtaining a difference between complex and simple inferences from the data aggregator and locally reconstructing the complex differences. To reduce data transmission, the data collector may transmit a data difference (e.g., a reduced-size representation of a measurement) to the data aggregator using the reconstructed complex inferences. The data aggregator may reconstruct data from the data collectors using the data difference from the data collector and inferences from the complex inference model.

Abstract: Methods and systems for managing data collection in a distributed system are disclosed. To manage data collection, the system may include a data aggregator and a data collector. The data aggregator and may utilize an inference model to predict data based on future measurements performed by data collectors throughout a distributed system without having access to the measurements. The data collectors may be mobile, and the data aggregator may direct the data collectors to various locations. To select paths for the data collectors to follow, the aggregator may utilize the level of uncertainty in predictions, the sensitivities in ranges of data to downstream consumers of the data collected by the data collectors, and/or other types of information. The data aggregator may select the paths for varying goals over time.

Abstract: Methods and systems for managing data collection throughout a distributed environment are disclosed. To manage data collection, a system may include a data aggregator and a data collector. The data collector may utilize a consensus sequence to generate reduced-size data transmissions. The consensus sequence may be made up of patterns of data that occur frequently in data collected by the data collector. Therefore, data collected by the data collector may be condensed by replacing segments of the data with pointer pairs, pointer pairs being indicators of a portion of the consensus sequence that matches a segments of data. The data collector may transmit these pointer pairs, along with any additional segments of data, to the data aggregator instead of transmitting full data sets. The data aggregator may reconstruct data from the data collectors using the reduced-size data and the consensus sequence.

Abstract: Methods and systems for managing data collection are disclosed. To manage data collection, a system may include a data aggregator and data collectors. The data aggregator may utilize an inference model to predict the future operation of data collectors, and a pattern selection model to sample data from data collectors at a specific frequency and sequence. The pattern may specify that some data collectors are not to be sampled at various points in time. By doing so, the system may be able to transmit less data, consume less network bandwidth, and consume less energy throughout a distributed system while still providing access to aggregated data.

Abstract: The system can determine complexity data representative of a complexity of changes to computer code that is executable to operate at least one microservice that is part of a group of microservices. The system can generate a progressive deployment plan for the at least one microservice based on the complexity of changes. The system can progressively direct traffic to the at least one microservice based on the progressive deployment plan.

Abstract: One example method includes performing delta operations to protect data. During a delta operation, a primary bitmap and a secondary bitmap are processed using bit logic. The delta generated by the delta operation is transmitted to a receiver. The receiver enqueues the delta into a delta queue configured to allow the replica volume at the target site to be moved to any point in time represented by the deltas in the delta queue.

Abstract: One example method includes performing, as part a planned failover procedure, operations that include connecting a replica OS disk to a replica VM, powering up the replica VM, booting an OS of the replica VM, disconnecting a source VM from a network, and connecting replica data disks to the replica VM. IOs issued by an application at the source VM continue to be processed by the source VM while the replica OS disk is connected, the replica VM is powered up, and the OS of the replica VM is booted.

Abstract: Data protection operations including replication operations from a production site to a replica site are disclosed. An example method assessing applications operating on a production virtual machine based on a replication strategy. The replication strategy is configured to identify related applications and ensure that the related applications are replicated to different replica virtual machines. The applications are then replicated from the production virtual machines to the replica virtual machines according to the replication strategy. The replication strategy can improve performance of the recovery 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: Compressing files is disclosed. An input file to be compressed is first aligned. When the file has multiple axes or dimensions, the file is aligned along each of the axes. Aligning the file includes splitting the file into sequences that can be aligned along each of the axes or dimensions. Aligning the file generates a compression tensor, where each row or dimensional space of the compression tensor corresponds to part of the file. A consensus tensor is determined from the compression tensor. Using the consensus tensor, pointer lists are generated. Each pointer lists identifies a subsequence or portion of the consensus tensor. The compressed file includes the pointer lists and the consensus tensor.