Abstract: One example method includes receiving a set of database parameters, creating one or more simulated databases based on the database parameters, receiving a set of target characteristics for the database, based on the target characteristics, slicing a datastream into a grouping of data slices, populating the simulated database(s) with the data slices to create the database collection and forward or reverse morphing the database from one generation to another without rewriting the entire database collection.

Abstract: A method, computer program product, and computing system for receiving a first set of input/output (IO) requests for one or more storage objects. One or more IO properties may be extracted from the first set of IO requests. Metadata may be associated with the one or more storage objects using one or more machine learning models based upon, at least in part, the one or more IO properties extracted from the first set of IO requests, thus defining storage object metadata. One or more IO processing rules may be enabled based upon, at least in part, the storage object metadata. A subsequent set of IO requests may be received. Processing of the subsequent set of IO requests on the one or more storage objects may be optimized based upon, at least in part, the storage object metadata and the one or more IO processing rules associated with the one or more storage objects.

Abstract: A method, computer program product, and computer system for receiving, by a computing device, a plurality of IO requests. A portion of the plurality of IO requests may be aggregated based upon a block size. The portion of the plurality of IO requests may be committed to persistent storage in a batch based upon, at least in part, aggregating the portion of the plurality of IO requests based upon the block size.

Abstract: A data deduplication process is performed in a storage system which includes storage nodes, and storage control nodes which can access data directly from each storage node. A first storage control node sends a message to a second storage control node to initiate a deduplication process with respect to a given data block and an original data block owned by the second storage control node. The second storage control node increments a reference counter associated with the original data block, and sends a message to the first storage control which includes metadata. The first storage control node uses the metadata to read the original data block from a given storage node, performs a data compare process to determine whether the given data block matches the original data block, and creates a reference to the original data block, if the given data block matches the original data block.

Abstract: A method, computer program product, and computing system for deploying an agent configured to communicate with a centralized database and a plurality of remote databases. The plurality of remote databases may be polled, via the agent, for data for storage in the centralized database. The data may be consolidated from the plurality of remote databases to the centralized database.

Abstract: One example method includes obtaining hop information of a metadata path for backup metadata, obtaining content sensitivity information indicating a relative sensitivity of backup data to an attack, based on the hop information and the content sensitivity information, selecting an encryption to be applied to the backup metadata, and applying the encryption to the backup metadata. The backup data may be encrypted with the same encryption algorithm that was used to encrypt the backup metadata.

Abstract: Techniques for performing processing to recover metadata may include: shadow top structures, and performing processing that uses the shadow top structures to recover information for an index node associated with an object of a file system having a file system logical address space. One of the shadow top structures is created for each metadata (MD) top node of a MD mapping structure used to determine storage locations of data stored at corresponding logical addresses in the file system logical address space. Each MD top node is used in determining storage locations for a specified subrange of logical addresses of the file system logical address space. Each shadow top structure corresponding to a MD top node describes each file system object mapped to a logical address included in the specified subrange of logical addresses of the file system address space associated with the corresponding MD top node.

Abstract: Techniques are provided for access control of protected data using storage system-based multi-factor authentication. One method comprises obtaining, in a storage system, an input/output request for data; determining, by the storage system, whether a multi-factor authentication is required for the requested data; initiating, by the storage system, a multi-factor authentication of a user associated with the input/output request, in response to a result of the determining, to obtain a verification result; and processing, in the storage system, the input/output request for the data based at least in part on the verification result. The data may be marked as protected data using a manual process and/or an automated process that processes one or more smart tags associated with the data. The marking of the data as protected data may comprise marking a partition comprising the data, marking a protected folder comprising the data, and/or marking a protected file comprising the data.

Abstract: Embodiments are described for a method and system of applying data protection software mechanisms to network devices to auto-discover the networking equipment, save changes from memory (TCAM) to local storage, backup changes to protection storage, provide auditing and tracking history of changes, and provide the ability to deploy test/development copies of changes using software defined networking techniques. Embodiments include an efficient visual mapping aspect provided through a GUI to display the topography and backup/protection configuration of network devices in a system.

Abstract: Embodiments of the present disclosure relate to a method, a device, and a computer program product for user behavior prediction. In some embodiments, at a client, a first user behavior embedding engine in the client generates behavior prediction information of a target user based on feature information of the target user. The client sends the behavior prediction information of the target user to a server, and receives information about a target item recommended for the target user from the server. Such method enables user privacy-related information to be processed only locally, thereby not only ensuring user privacy and security, but also significantly reducing overall resource overhead.

Abstract: One example method includes receiving, by a first computing entity from a second computing entity, a request for data, providing, by the first computing entity, a compliance API (Application Program Interface) to the second computing entity, receiving, by the first computing entity from the second computing entity, location information and/or data compliance information, by way of the compliance API, consulting, by the first computing entity, a mapping, and determining, based on information in the mapping and the location information and/or data compliance information, whether or not the data is permitted to be transmitted by the first computing entity to the second computing entity, and either transmitting the data to the second computing entity, or not transmitting the data to the second computing entity, based on data tags, the information in the mapping and the location information and/or data compliance information.

Abstract: Techniques are provided for acceptance status classification of product-related data structures using models with multiple training periods. One method comprises obtaining data for a given product-related data structure; evaluating first features related to the given product-related data structure using the obtained data; applying the first features related to the given product-related data structure to one or more models trained using multiple different training periods to obtain a plurality of second features, wherein each of the second features indicates a prediction related to an acceptance status of the given product-related data structure by at least one model for a respective training period; and aggregating at least the second features to obtain a classification related to an aggregate acceptance status of the given product-related data structure. A weighting of at least some of the first and second features can be learned during a training phase.

Abstract: Techniques are provided for multi-cloud data protection using threshold-based file reconstruction. One method comprises obtaining a file comprising metadata and data for storage in a cloud environment; generating a plurality of encrypted file portions from the data; and uploading each of the encrypted file portions with the metadata as cloud objects to multiple different cloud environments. A threshold number of the encrypted file portions are needed from at least two different cloud environments to reconstruct the file. For file reconstruction, the threshold number of encrypted file portions can be validated, merged and decrypted.

Abstract: One example method includes gathering, by a domain adversarial neural network model deployed in an autonomous vehicle operating in a domain, a dataset that comprises unsegmented and unlabeled image data about the domain, sampling the dataset to create an adapted domain dataset, detaching a domain classifier from the domain adversarial neural network, using the domain classifier as a domain change detector model to predict a class of the unsegmented and unlabeled image data in the adapted domain dataset, and based on the class, either: determining that the domain is changed or is unknown; or, determining that the domain has not changed.

Abstract: One example method includes generating a fingerprint:tag dictionary that includes a group of fingerprints and a group of tags, and the fingerprint:tag dictionary identifies, for each fingerprint, the tag or tags which include that fingerprint, computing a similarity matrix based on the fingerprint:tag dictionary, and the similarity matrix identifies, for each pair of tags in the fingerprint:tag dictionary, a relative similarity of the tags in the pair to each other, running a clustering algorithm to identify groups of similar tags in the similarity matrix, and deduplicating, based on the groups of similar tags, respective data associated with the fingerprints.

Abstract: Techniques for managing resources of a virtualized system involve acquiring historical distribution data about a virtualized system, the historical distribution data indicating a historical distribution of resources occupied by workloads on a plurality of host machines of the virtualized system over a predetermined historical time period. The techniques further involve generating predicted distribution data based on the historical distribution data, the predicted distribution data indicating an estimated distribution of resources occupied by the workloads on the plurality of host machines over a predetermined future time period. The techniques further involve performing workload migration at least once based on the predicted distribution data, the workload migration including migrating a workload of a first host machine whose first estimated quantity of occupied resources exceeds a high threshold to a second host machine whose second estimated quantity of occupied resources is below a low threshold.

Abstract: One example method includes receiving at a dedupe system, from a client, a request that comprises a set of fingerprints, where each fingerprint in the set corresponds to a particular data segment, filtering, at the dedupe system, the set of fingerprints into a set of unique fingerprints and a set of non-unique fingerprints, reading, at the dedupe system, from a container where copies of the non-unique fingerprints are stored, an additional set of non-unique fingerprints, sending, from the dedupe system to the client, a single response that comprises both the set of unique fingerprints and the additional set of non-unique fingerprints, and receiving from the client, at the dedupe system, data segments that respectively correspond to the unique fingerprints in the set of unique fingerprints, but no data segments corresponding to the non-unique fingerprints in the set of non-unique fingerprints are received by the dedupe system from the client.

Abstract: Techniques are disclosed for edge node data gathering. One example method includes receiving probability distributions from edge nodes; using the probability distributions to identify a set of distribution cliques of the edge nodes; selecting one or more representative edge nodes from each clique; receiving feature data from the edge nodes, the feature data comprising resource information that includes a resource availability and a utilization status of the edge node at a first time, t?1; training a ML-based model using a portion of the feature data; associating the feature data with the corresponding clique for the edge node at the first time; using the probability distributions, cliques, and feature data to obtain episode data for each clique for the first time; and training a ML-based divergence model using a portion of the episode data to update a divergence threshold value for the clique for a second time, t.

Abstract: Described is a system (and method) that maintains deduplication efficiency when storing data within a clustered data storage environment that implements a global namespace. To provide such a capability, the system may obtain granular data source identifying information from a client system that provides data to be backed-up by a backup component. The data source identifying information may take the form of a placement tag that is associated with the received data. The backup component may then provide such placement tags when providing the backup data to the clustered storage system. The placement tags may then be used to intelligently distribute backup files to particular storage nodes of the clustered storage system to improve deduplication efficiency.

Abstract: Described is a system for distributing data amongst storage components using data sensitivity (or security) classifications. The system may define categories for classifying data files and assign a sensitivity (or security) rating to each of the defined categories. The categories and/or associated sensitivity ratings may be determined using machine learning components that may leverage industry-specific information or data sensitivity information used by other clients. The system may then continuously reevaluate (or reclassify) data files to determine whether they are stored on a storage component that meets the necessary data sensitivity requirements. If the system determines particular data files are stored on a corresponding storage component that does not meet certain data sensitivity requirements, the system may perform an action to secure the particular data files.