Abstract: Detecting a trend in latency for storage underlying a deduplicated file system includes generating a set of data points by recording when input/output (IO) requests were issued to the storage and recording time required to receive success responses from the storage. Least squares regression is performed on the data points to find a best-fit line through the data points. A slope of the best-fit line is calculated. A determination is made as to whether the slope is positive, a positive slope thereby indicating a trend of increasing latency of the storage. When the slope is determined to be positive, clients accessing the deduplicated file system are throttled.

Abstract: Methods, apparatus, and processor-readable storage media for automated storage system performance validation are provided herein. An example computer-implemented method includes obtaining user input pertaining to performance testing associated with one or more storage systems deployed in at least one user environment; generating, based at least in part on the obtained user input, a storage system performance test deployment environment comprising at least one controller and at least one data collection mechanism; executing, using the storage system performance test deployment environment, at least one performance test on at least a portion of the one or more storage systems deployed in the at least one user environment; and performing one or more automated actions based at least in part on results from the at least one executed performance test.

Abstract: Embodiments of the present disclosure relate to method, device and computer program product for data backup. The method comprises: in response to receiving from a backup server a data stream to be backed up, dividing the data stream into a plurality of data segments; distributing the plurality of data segments to at least one computing node; in response to receiving an index of a corresponding data segment from a first computing node of the at least one computing node, looking up the index in a global index cache, the index being generated by the first computing node to uniquely identify the data segment, the global index cache storing indexes of data in a backup storage device; in response to the missing index in the global index cache, adding the index into the global index cache; and sending to the first computing node an indication to store the data segment in the backup storage device.

Abstract: One example method includes optimizing client-side deduplication. When backing up a client, a cadence and a change log resolution are determined. These values are evaluated alone or in combination with respect to various thresholds. Client-side deduplication is enabled or disabled based on whether any one or more of the thresholds are satisfied.

Abstract: One example method includes accessing an appearance history of a person, the appearance history including information concerning an appearance of a person at a particular time, generating, based on the appearance history, a forecast that comprises a probability that the person will appear again at some future point in time, determining that the probability meets or exceeds a threshold, and updating a high probability group database to include a facial image of a face of the person.

Abstract: One example method includes providing temporary access to a computing system and to providing temporary access as a service. The features of a temporary access can be defined by an entity and a user may be able to obtain a token that includes these features, which may be embedded in the token as claims. The user's access is then controlled in accordance with the embedded claims. The temporary access as a service can be federated. The token may include trust levels and tolerance limits. Further, aspects of the temporary access can be monitored and/or changed. Adjustments to trust levels can be automated or manually performed. Further trust for specific users can be gained or lost over time based on at least previous accesses.

Abstract: A method for performing a backup operation includes obtaining, by a backup server, a backup request, wherein the backup request specifies a virtual machine to be backed up, wherein the virtual machine is hosted by a production host, and in response to the backup request: obtaining classification data from the backup agent, initiating a backup classification on an unprocessed backup associated with the virtual machine based on the classification data to obtain a sensitivity tag, and initiating a data processing on the unprocessed backup based on the sensitivity tag.

Abstract: One example method includes deploying an application in a distributed computing environment. Telemetry data is collected that corresponds with the deployment of an application. The telemetry data is received by a machine learning model that was trained with test telemetry data to determine whether the deploying is successful or failed. A successful inference results in continued deployment and a fail inference results in a rollback of the application.

Abstract: A technique manages objects in a software development project. In particular, in response to receiving a commit request for committing a group of objects, a group of historical commit requests associated with the group of objects are acquired based on a commit history, wherein historical commit requests in the group of historical commit requests involve the group of objects. A candidate object is selected from at least one object involved in the historical commit requests that is different from the group of objects. An association relationship between the candidate object and the group of objects is determined. The candidate object is identified as a missing object in response to determining that the association relationship satisfies a predetermined condition, the missing object indicating an object associated with the commit request but not included in the commit request. Accordingly, objects that are missing in a commit request can be automatically checked.

Abstract: A method, an electronic device, and a computer program product for storage management are provided. The method includes: acquiring a lock attribute record in a lock attribute record chain from a data protection network for backing up data, data protection servers of the data protection network reaching a consensus on the lock attribute record chain, the lock attribute record including a first attribute value of an attribute of a lock operation, the lock operation being used for preventing a backup of the data stored in a storage server from being tampered with; acquiring, based on the lock attribute record, a second attribute value of the attribute of the lock operation from the storage server; and generating, based on determining that the first attribute value does not match the second attribute value, an alarm indicating that the backup is tampered with. This solution can better prevent data from being tampered with.

Abstract: The present disclosure relates to a method, an electronic device, and a computer program product for monitoring a field device. For example, a method for monitoring a field device is provided. The method may include receiving facility information data associated with locations of a group of field devices and a sensing data set acquired by a sensing apparatus arranged near the group of field devices. The method may further include determining, according to a determination that sensing data associated with at least one field device in the group of field devices in the sensing data set is abnormal, a target location of the at least one field device based on the facility information data. In addition, the method may further include generating navigation information from a source location where a user is located to the target location.

Abstract: Embodiments of the present disclosure relate to a multi-node storage system and a data deduplication method thereof. The method includes determining a similarity hash value of a super block, wherein the similarity hash value indicates similarity between super blocks The method further includes comparing the similarity hash value of the super block with a feature similarity hash value of a node of the multi-node storage system to determine whether the super block matches the node and, in response to determining that the super block matches the node, allocating the super block to the node.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for recommending a protection strategy. The method includes obtaining contents of attributes of a plurality of data assets adjusted. The method further includes generating a plurality of vector representations for the plurality of data assets based on the contents of the attributes. The method further includes dividing the plurality of data assets into at least one category based on the plurality of vector representations. The method further includes if it is determined that a protection strategy for one data asset in the at least one category exists, determining the protection strategy as a recommended strategy for another data asset in the at least one category.

Abstract: A peer-to-peer global namespace for storage system metadata federations. Specifically, the disclosure proposes a peer-to-peer metadata federation architecture, where enterprise storage metadata for any and all geo-distributed sites may be distributed across and continually reconciled by a network of nodes, instead of being maintained by a single node assigned to a given geographic location. In implementing a federation, the network of nodes may lack exploitable, centralized points of vulnerability, and therefore, may exhibit no central point of failure. Furthermore, the disclosure proposes the implementation of a global metadata namespace for the enterprise storage metadata spanning the entirety of an enterprise (across all geographic locations). The global metadata namespace may be maintained on each node through event-based synchronization.

Abstract: Techniques are provided for caching of machine learning model training parameters. One method comprises training a machine learning model using a given training dataset; and caching a parameter of the machine learning model from the training with the given training dataset. The cached parameter of the machine learning model is used for a subsequent training of the machine learning model. The caching may be performed after each of multiple iterations of the training of the machine learning model. A given cached iteration of the training of the machine learning model may be identified using a key based on: (i) a hash of the given training dataset, (ii) a hash of the machine learning model parameter, and/or (iii) hyperparameters of the machine learning model. The caching of a given iteration of the machine learning model may occur when the given cached iteration is not found in a cache memory.

Abstract: A system can receive, from user input, request data indicative of a request to create a file with a first filename. The system can, based on the request data, determining a second filename for the file. The system can store an association between the first filename and the second filename. The system can create the file in a file system with the second filename.

Abstract: Techniques for providing effective utilization of different drive capacities in storage appliances. The techniques include providing a storage drive array that has a first set of storage drives and a second set of storage drives. Each storage drive in the first set has a first drive capacity and each storage drive in the second set has a second drive capacity. The first drive capacity is higher than the second drive capacity. The techniques include allocating, within the first drive capacity, at least a first sub-capacity and a second sub-capacity. The first sub-capacity is equal to the second drive capacity. The techniques include placing blocks of hot data in the first sub-capacities of the storage drives in the first set and/or the second drive capacities of the storage drives in the second set, and placing blocks of cold data in the second sub-capacities of the storage drives in the first set.

Abstract: Techniques are provided for secure data management in a network computing environment. A security management system receives data from a device which operates in a device network that is managed by the security management system. The security management system performs a data classification process to determine a data sensitivity level of the received data. The security management system determines a type of encryption to apply to the received data based on the determined data sensitivity level. The type of encryption is determined from a plurality of different types of encryption that are supported by a cloud system. The security management system sends the received data to the cloud system to at least one of store the data and perform secured data analytic processing of the data, in a format according to the determined type of encryption.

Abstract: A method, computer program product, and computer system for identifying, by a computing device, a number of extents needed for a create snapshot operation to create a snapshot. The number of extents may be added to an in-memory cache. The number of extents needed for the create snapshot operation may be allocated from the in-memory cache to execute the create snapshot operation. Freed extents may be added to the in-memory cache based upon, at least in part, executing a delete snapshot operation to delete the snapshot.

Abstract: Workloads, e.g., synthetic workloads, on one or more storage systems in an dynamic, automated manner, for example, to load test the one or more storage systems. A distributed system may be employed in which a workload information server (WIS) serves one or more clients referred to herein as workload control components (WCCs) that analyze workload information of the one or more storage systems, and control the modification of workloads thereon based on this analysis, through the WIS. The WIS also may serve one or more clients referred to herein as workload generation controllers (WGCs) that monitor workloads on the one or more storage systems, report workload information to the WIS and generate, modify or remove workloads on the one or more storage systems according to instructions received from the WIS in response to requests (e.g., hints) from the one or more WGCs.