Abstract: A method for time sensitive network (TSN) operation includes: in the case of data traffic stoppage in a communication channel of TSN due to at least one of down time of a user plane (UP) connection and a reset of the UP connection, performing the following: generating, by a user plane function (UPF), an audit report about the at least one of the down time and the reset; sending, by the UPF, the audit report to a first element in control plane (CP), without waiting for communication from the CP; and determining, by one of the first element or a second element in the CP, survival time of the communication channel. The audit report is sent along with a report type indicating the down time of the UP connection due to connection fluctuation or a switch-over of the UP connection from a primary UPF to a back-up UPF.

Abstract: Contents of a full snapshot for storage in one or more cloud storage volumes are received. The contents of the full snapshot is stored in the one or more cloud storage volumes. A snapshot of a virtual machine data volume and a snapshot of a virtual machine boot volume are generated based on the contents of the full snapshot stored in the one or more cloud storage volumes. An image of the virtual machine boot volume is generated based on the snapshot of the virtual machine boot volume. The snapshot of the virtual machine data volume, the snapshot of the virtual machine boot volume, and the image of the virtual machine boot volume are stored in a cloud object storage.

Abstract: An instruction to generate a cloud instantiation of a secondary storage system is provided. One or more secondary storage clusters are virtually rebuilt in the cloud instantiation of the secondary storage system. A new cloud instance of a user virtual machine is deployed based on at least a portion of data stored in the one or more rebuilt secondary storage clusters of the cloud instantiation of the secondary storage system. A version of at least the portion of the data of the one or more rebuilt secondary storage clusters is provided to a cloud deployment server.

Abstract: Contents of a virtual machine of a primary storage are received at a first point in time. A full snapshot of the virtual machine is stored on a secondary storage using the received contents of the virtual machine. One or more cloud storage volumes of the virtual machine are caused to be created. Contents of the full snapshot for storage on the one or more cloud storage volumes are provided from the secondary storage. Changed data of the virtual machine of the primary storage is received at a second point in time. An incremental snapshot of the virtual machine is stored using the received changed data. The one or more cloud storage volumes are updated using the incremental snapshot of the virtual machine. An image of at least one of the cloud storage volumes is caused to be created for standby deployment of another instance of the virtual machine.

Abstract: Described herein are techniques for using supervised machine learning to determine whether a content posting posted to a feed of an online service, has been posted with the intent that the content posting is a poll or survey. Upon making a determination that a content posting is or includes a poll, the content posting is further analyzed to identify within the content posting a question and/or answers to the question. The identified question and answers are then used to populate data fields associated with a formal or structured poll, and the end-user who posted the content posting is provided an option to convert the poll from a first content posting format to a second content posting format that is specifically for a formal or structured poll.

Abstract: Application-level data in a storage system are evaluated. For example, a backup analysis tool retrieves a backup object stored in the storage system. The backup analysis tool reconstructs an application object from the backup object. The backup analysis tool accesses the first application object according to a native application format associated with the first object.

Abstract: An instruction to generate a cloud instantiation of a secondary storage system is provided. One or more secondary storage clusters are virtually rebuilt in the cloud instantiation of the secondary storage system. A new cloud instance of a user virtual machine is deployed based on at least a portion of data stored in the one or more rebuilt secondary storage clusters of the cloud instantiation of the secondary storage system. A version of at least the portion of the data of the one or more rebuilt secondary storage clusters is provided to a cloud deployment server.

Abstract: Described herein are systems and methods for generating an embedding—a learned representation—for an image. The embedding for the image is derived to capture visual aspects, as well as textual aspects, of the image. An encoder-decoder is trained to generate the visual representation of the image. An optical character recognition (OCR) algorithm is used to identify text/words in the image. From these words, an embedding is derived by performing an average pooling operation on pre-trained embeddings that map to the identified words. Finally, the embedding representing the visual aspects of the image is combined with the embedding representing the textual aspects of the image to generate a final embedding for the image.

Abstract: Application-level data in a storage system are evaluated. For example, a backup analysis tool retrieves a backup object stored in the storage system. The backup analysis tool reconstructs an application object from the backup object. The backup analysis tool accesses the first application object according to a native application format associated with the first object.

Abstract: Described herein are techniques for using supervised machine learning to determine whether an image that has been posted to a feed of an online service includes a quotation. In some instance, supervised machine learning techniques are used to infer or predict an intent of a content poster in posting a content item to a feed of an online service. By better understanding the nature of the content being posted, various recommendations can be made during the time when an end-user is posting content, and thereafter.

Abstract: An instruction to generate a cloud instantiation of a secondary storage system is provided. One or more secondary storage clusters are virtually rebuilt in the cloud instantiation of the secondary storage system. A new cloud instance of a user virtual machine is deployed based on at least a portion of data stored in the one or more rebuilt secondary storage clusters of the cloud instantiation of the secondary storage system. A version of at least the portion of the data of the one or more rebuilt secondary storage clusters is provided to a cloud deployment server.

Abstract: A processor may launch a common layer including a browser and a shell. The processor may identify a request for data required by the software platform. The processor may determine a context in which the request was generated by detecting, by the shell, whether the software platform is an on-premise solution or a cloud solution; examining, by the shell, the request to determine whether it requires local resources or cloud-based resources; and designating, by the shell, the context as a cloud-based context or an on-premise context based on the detecting and the examining. In response to the determining, the processor may perform processing related to a request generated in the on-premise context using at least one local resource stored in a local data store; and communicate, by the browser, with at least one cloud-based resource to perform processing related to a request generated in the cloud-based context.

Abstract: Contents of a virtual machine of a primary storage are received at a first point in time. A full snapshot of the virtual machine is stored on a secondary storage using the received contents of the virtual machine. One or more cloud storage volumes of the virtual machine are caused to be created. Contents of the full snapshot for storage on the one or more cloud storage volumes are provided from the secondary storage. Changed data of the virtual machine of the primary storage is received at a second point in time. An incremental snapshot of the virtual machine is stored using the received changed data. The one or more cloud storage volumes are updated using the incremental snapshot of the virtual machine. An image of at least one of the cloud storage volumes is caused to be created for standby deployment of another instance of the virtual machine.

Abstract: Techniques are disclosed for managing a high performance, fault-tolerant, strongly consistent, distributed key-value store system. The key-value store may store information, such as metadata for a distributed file system. Fault-tolerance means that the distributed key-value store continues to provide access to values in the key-value store in spite of a certain number of node failures. To provide this capability, the key-value store may store copies of (key, value) pair on N+1 nodes in order to provide fault tolerance for the failure of up to N nodes. In addition, metadata describing which nodes store a given value is stored on 2N+1 nodes and the distributed key-value store is sized such that there are 3N+1 nodes in a cluster. Doing so allows the key, value store to tolerate a failure of N nodes, while still maintaining a consistent and available key-value store.

Abstract: Contents of a virtual machine of a primary storage are received at a first point in time. A full snapshot of the virtual machine is stored on a secondary storage using the received contents of the virtual machine. One or more cloud storage volumes of the virtual machine are caused to be created. Contents of the full snapshot for storage on the one or more cloud storage volumes are provided from the secondary storage. Changed data of the virtual machine of the primary storage is received at a second point in time. An incremental snapshot of the virtual machine is stored using the received changed data. The one or more cloud storage volumes are updated using the incremental snapshot of the virtual machine. An image of at least one of the cloud storage volumes is caused to be created for standby deployment of another instance of the virtual machine.

Abstract: Application-level data in a storage system are evaluated. For example, a backup analysis tool retrieves a backup object stored in the storage system. The backup analysis tool reconstructs an application object from the backup object. The backup analysis tool accesses the first application object according to a native application format associated with the first object.

Abstract: Techniques are disclosed for managing a high performance, fault-tolerant, strongly consistent, distributed key-value store system. The key-value store may store information, such as metadata for a distributed file system. Fault-tolerance means that the distributed key-value store continues to provide access to values in the key-value store in spite of a certain number of node failures. To provide this capability, the key-value store may store copies of (key, value) pair on N+1 nodes in order to provide fault tolerance for the failure of up to N nodes. In addition, metadata describing which nodes store a given value is stored on 2N+1 nodes and the distributed key-value store is sized such that there are 3N+1 nodes in a cluster. Doing so allows the key, value store to tolerate a failure of N nodes, while still maintaining a consistent and available key-value store.

Abstract: Application-level data in a storage system are evaluated. For example, a backup analysis tool retrieves a backup object stored in the storage system. The backup analysis tool reconstructs an application object from the backup object. The backup analysis tool accesses the first application object according to a native application format associated with the first object.

Abstract: Technologies for evaluating, scoring, and determining whether to present a caption of an image are provided. The disclosed techniques include receiving an image with associated metadata. Contextual data is identified from the image and the metadata. A generated caption for the image is received from an image caption generator. A first vector representation is generated based on the contextual image data and a second vector representation is generated based on the generated caption. A machine learned model generates a score for the generated caption using the first vector representation and the second vector representation. The score represents a confidence value defining how accurately the caption describes the image. Based on the score, the caption may be presented along with the image on a client device.

Abstract: Methods, systems, and computer programs are presented for detecting near-duplicate profile images of the users in a social network. One method includes operations for identifying an image in a profile of a user of the social network, determining a query feature vector for the image, the query feature vector comprising a set of features, and determining a dominant feature from the features, the dominant feature having a highest value from the values of the features. Further, the method includes operations for determining a bucket in a database of feature vectors based on the dominant feature, determining if the query feature vector is a near duplicate of any feature vector in the determined bucket, and determining if the profile of the user is a duplicate profile or a fake profile based on whether the query feature vector is a near duplicate of any feature vector in the determined bucket.