Abstract: Embodiments for dynamically allocating journal space for Do streams across multiple applications. A shared Do stream process has a dynamic block allocation component that provides a certain amount of buffering of a data flush for an application, using space that would normally be allocated for, but unused by, other applications, thus preventing the need for one or more of the applications to move to fast-forward mode when possible. Certain machine learning techniques are used in order to predict the required Do stream for each application according to past experience with the application, and this prediction is used to intelligently allocate Do Streams between the different applications.

Abstract: One example method includes monitoring performance of an element of a runtime environment, where the monitoring includes collecting performance information concerning the element, analyzing the collected information, detecting, based on the analysis of the collected information, an anomaly in the performance of the element and, in response to detection of the anomaly, automatically marking a snapshot of the runtime environment element, and the marking of the snapshot overrides a retention policy applicable to the snapshot.

Abstract: Techniques are provided for implementing data management policies for various components of an Internet of Things (IoT) system. An exemplary method performed by an IoT component comprises: obtaining sensor data; obtaining a data management policy that specifies a data transmission policy, a data retention policy, a data retirement policy and/or a data processing policy for a processing of the sensor data by a plurality of IoT components; and processing the sensor data based on the obtained data management policy. Data policy operators are optionally provided to (i) adjust a resolution of the sensor data; (ii) aggregate the sensor data; and/or (iii) apply a learning algorithm to the sensor data, based on the data management policy.

Abstract: Systems, apparatus, and methods for any point in time replication to the cloud. Data is replicated by replicating data to a remote storage or a data bucket in the cloud. At the same time, a metadata stream is generated and stored. The metadata stream establishes a relationship between the data and offsets of the data in the production volume. This allows continuous replication without having to maintain a replica volume. The replica volume can be generated during a rehydration operation that uses the metadata stream to construct the production volume from the cloud data.

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.

Abstract: Data reduction techniques are provided for a multi-sensor IoT environment. An exemplary method comprises: dynamically determining, by a device within a distributed network comprised of a plurality of sensors, an amount of sensor data to be collected by and/or transmitted by a sensor within the distributed network based on at least one predefined spatial-based rule and/or at least one predefined temporal-based rule; and processing the sensor data based on the dynamically determined amount of sensor data. A percentage of the plurality of sensors within the distributed network that collect and/or transmit the sensor data can optionally be specified. One or more sensors optionally collect the sensor data at a default resolution and a predefined spatial-based rule and/or a predefined temporal-based rule specifies a predefined trigger for at least one sensor to collect and/or transmit the sensor data at a higher resolution.

Abstract: Data provenance techniques are provided using distributed ledgers.

Abstract: Embodiments for providing compare and swap (CAS) functionality to key value storage to allow multi-threaded applications to share storage devices and synchronize multiple concurrent threads or processes. A key-value application programming interface (API) is modified to include a CAS API in addition to the standard Put and Get APIs. The CAS function uses a key, expected old value, and new value to compare and swap an existing key value only if its current value equals the expected old value. Hash values of the key value and expected old value may be used by the CAS function to improve performance and reduce bandwidth.

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: Predicting large data flushes by collecting usage data for system assets, analyzing the data using machine learning on each asset and the whole system to determine usage trends, predicting a next large data flush using a time-series model, and determining if a size of the predicted next flush size is too large relative to journal storage space in order to advance fast forward mode. Further, protecting history information by pausing distribution of data from journal volumes to replica volumes, taking storage-level snapshots of the replica and the journal volumes, storing a snapshot timestamp for each of the storage-level snapshots in a snapshot database prior to advancing the fast forward mode or un-pausing distribution.

Abstract: A replication system for data of mobile devices is disclosed. The data of a mobile device is uploaded to stations in an area. Metadata associated with the objects is stored in a centralized or decentralized system. The metadata can be accessed to identify the stations storing the device's objects and the data of the mobile device can then be retrieved from the stations and reconstructed.

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: Systems and methods for providing any point in time functionality. With a storage system such as a VSAN, any point in time protection is achieved by combining a metadata stream with snapshots of the storage system. This allows snapshots to be generated in hindsight such that any point in time functionality is provided.

Abstract: Systems, apparatus, and methods for any point in time replication to the cloud. Data is replicated by replicating data to a remote storage or a data bucket in the cloud. At the same time, a metadata stream is generated and stored. The metadata stream establishes a relationship between the data and offsets of the data in the production volume. This allows continuous replication without having to maintain a replica volume. The replica volume can be generated during a rehydration operation that uses the metadata stream to construct the production volume from the cloud data.

Abstract: A tracing mechanism is provided for analyzing session-based attacks. An exemplary method comprises: detecting a potential attack associated with a session from a potential attacker based on predefined anomaly detection criteria; adding a tracing flag identifier to a response packet; sending a notification to a cloud provider of the potential attack, wherein the notification comprises the tracing flag identifier; and sending the response packet to the potential attacker, wherein, in response to receiving the response packet with the tracing flag identifier, the cloud provider: determines a source of the potential attack based on a destination of the response packet; forwards the response packet to the potential attacker based on the destination of the response packet; and monitors the determined source to evaluate the potential attack. The response packet is optionally delayed by a predefined time duration and/or until the cloud provider has acknowledged receipt of the notification.

Abstract: Embodiments for detecting malicious modification of data in a network, by: setting, by a first layer of network resources, a number of markers associated with input/output (I/O) operations of the network; saving the markers, location, and associated metadata in a marker database; reading, by a second layer of the network resources, the markers corresponding to relevant I/O operations; and verifying each scanned I/O operation against a corresponding marker to determine whether or not data for a scanned specific I/O operation has been improperly modified for the first and second layers and any intermediate layer resulting in a fault condition, and if so, taking remedial action to flag or abort the specific I/O operation.

Abstract: Predicting large data flushes by collecting usage data for system assets, analyzing the data using machine learning on each asset and the whole system to determine usage trends, predicting a next large data flush using a time-series model, and determining if a size of the predicted next flush size is too large relative to journal storage space in order to advance fast forward mode. Further, protecting history information by pausing distribution of data from journal volumes to replica volumes, taking storage-level snapshots of the replica and the journal volumes, storing a snapshot timestamp for each of the storage-level snapshots in a a snapshot database prior to advancing the fast forward mode or un-pausing distribution.

Abstract: Methods and apparatus are provided for consistency group replication with reduced resource consumption. An exemplary method comprises: obtaining, by one or more first replication appliances at a first location, one of (i) data generated by a monitored application; and (ii) metadata for each write operation generated by the monitored application, wherein a splitter function provides the data or metadata to the one or more first replication appliances based on a dynamic determination of whether the monitored application requires replication based on the data or the metadata based on predefined application consistency group type criteria; obtaining, by one or more of the first replication appliances, a snapshot comprising the data associated with each of the write operations since a prior snapshot based on the one of the data and the corresponding metadata; and providing the snapshot to one or more second replication appliances at a different location for storage in a replication journal.

Abstract: Image combination techniques are provided for a multi-sensor Internet of Things environment. An exemplary method comprises: dynamically determining an image resolution for at least a portion of an image to be collected by and/or transmitted by a plurality of image sensors within a distributed network based on one or more predefined image resolution rules with respect to an available bandwidth; and combining the image portions from the plurality of image sensors to generate at least one higher resolution image, such as a super-resolution image. The predefined image resolution rules specify, for example, that the portions of an image that have changed should be transmitted with a higher resolution relative to portions of the image that have not changed; and/or that multiple versions of a given image should be combined when the given image is degraded by noise.

Abstract: A replication system for data of mobile devices is disclosed. The data of a mobile device is uploaded to stations in an area. Metadata associated with the objects is stored in a centralized or decentralized system. The metadata can be accessed to identify the stations storing the device's objects and the data of the mobile device can then be retrieved from the stations and reconstructed.