Abstract: One example method includes extracting an expected communication specification of a service that is in development in a build pipeline, stimulating the service to exercise communication variations of the service, capturing communication traffic involving the service and one or more endpoints, comparing the captured communication traffic to the expected communication specification, and based on the comparing, detecting, in the captured communication traffic, and flagging, any anomalous communication behavior of the service.

Abstract: One example method includes determining a spatial distribution of devices in a three dimensional physical environment, determining a polling frequency for each of the devices, receiving data generated by the devices, evaluating the data, and assigning a trust score to the data. The devices may take the form of respective sensors operable to sense a physical attribute of the three dimensional physical environment. A Fourier transform may be employed to determine the polling frequency.

Abstract: A method for task scheduling includes, if it is determined that there is an idle task scheduling token, causing a first computing resource in a computing system to acquire the idle task scheduling token as a first task scheduling token to execute a first task in a task queue. If it is determined that a second task having a higher priority than the first task is to be executed, the first computing resource is to discard the first task scheduling token to switch from executing the first task to executing the second task. If it is determined that the execution of the second task is finished, the first computing resource is to retrieve the first task scheduling token to continue the execution of the first task.

Abstract: One example method includes accessing a dataset, selecting a list of attributes of the dataset, each of the attributes being selected based on a determination that the attribute is affecting growth of the dataset and affecting an amount of data storage space consumed by the dataset, assigning a SHAP score to each attribute, using the SHAP scores to assign respective weights to each attribute, deriving drift and shock information for the dataset, and based on the drift and shock information, calculating a risk score that a storage capacity of an asset where the dataset is stored will be exhausted within a particular time interval.

Abstract: One example method includes collecting information concerning respective data access patterns of one or more customers, using the information, and work window information, to calculate a respective data retrieval frequency for each of the customers, and enabling the customers to retrieve data according to their respective data retrieval frequency. The collected information may be weighted prior to calculation of the data retrieval frequency, and the data retrieval frequency may be updated automatically in response to changes in customer data.

Abstract: A memory tier including persistent memory (PMEM) devices is established in nodes of a cluster system having a deduplicated file system. At least a portion of metadata generated by the deduplicated file system is persisted to the memory tier. The portion of metadata includes an index of fingerprints corresponding to data segments stored by the deduplicated file system to a storage pool. A determination is made that an instance of the deduplicated file system has failed. A new instance of the deduplicated file system is started to recover file system services by loading the index of fingerprints from the memory tier.

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: 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: One example method includes determining that local resources at an edge site are inadequate to support performance of a function needed by software running on the edge site, invoking a client agent, in response to invoking the client agent, receiving an execution manifest, determining, by the client agent, where to execute the function, wherein the determining comprises identifying a target execution environment for the function and the determining is based in part on information contained in the execution manifest, and transmitting, by the client agent, the execution manifest to a server agent of the target execution environment, and the execution manifest facilitates execution of the function in the target execution environment.

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: A method for managing a file system includes obtaining, by a compression optimizing manager, a compression algorithm selection request for the file system, determining a set of selection inputs based on a set of file system parameters of the file system, applying a compression selection model to the set of selection inputs to obtain a compression algorithm selection, and initiating a file system compression implementation of the file system using the compression algorithm selection.

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: 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: 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 system can train a neural network model at a first edge device regarding respective amounts of time to process data at the first edge device compared to corresponding amounts of time to process the data at cloud computing equipment that is connected to the first edge device via a communications network, wherein the data is generated at the first edge device. The system can update the neural network model to produce an updated neural network model based on information received from a second edge device regarding a performance of the cloud computing equipment in processing the data, wherein the first edge device and the second edge device having respective different processing capabilities. The system can determine whether to process first data, generated at the first edge device, locally at the first edge device.

Abstract: Techniques described herein relate to a method for distributed data management. The method may include obtaining, by a data management agent of a data host and from an application executing on the data host, a request to access data; obtaining, by the data management agent, an information set associated with the data; making a determination, by the data management agent, that at least a portion of the data is not ready to be used by the application; and performing, by the data management agent and based on the determination, a data preparation action set.

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: 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: 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.