Abstract: Described herein are systems, methods, and software to provide ransomware detection using variable levels of encryption. In one implementation, a computing device identifies a set of files, wherein the set of files each comprise a label indicative of whether the file is representative of a safe file or a file attacked by ransomware, and wherein the set of files comprises unencrypted files, partially encrypted files, and fully encrypted file. The computing device further identifies features associated with the set of files and generates a machine learning model that outputs a determination of whether a new file has been attacked based at least on the features in relation to whether a file in the set of files was labeled as attacked.

Abstract: Described herein are systems, methods, and software to implement cloud ransomware detection. In one example, a computing device receives features of a file from a second computing device remote from the cloud environment, the features comprising at least a measure of randomness for the file and an identifier for a user associated with a modification to the file. The computing device further user information associated with a user of the modified the file and applies a machine learning model to determine whether the file was attacked based on the features and the user information. The computing device also communicates a notification to the second computing device indicating whether the file was attacked.

Abstract: Described herein are systems, methods, and software to implement ransomware detection by varying chunk size in files. In one example, a computing device extracts a first set of chunks from a file, the first set of chunks each representing a first sized portion of the file. The computing device further first features in association with the first set of chunks, the first features comprising a measure of randomness associated with the first set of chunks. The computing device also inputs the first features to a machine learning model that outputs a determination of whether the file has been attacked and determines whether to reduce the first sized portion based on the determination.

Abstract: Described herein are systems, methods, and software to implement multi-level ransomware detection via file processing. In one example, a computing device conducts a first level of ransomware detection on a file, wherein the first level of ransomware detection comprises identifying features of the file that include a measure of randomness in the file. The computing device further inputs the features to a machine learning model that outputs a determination of whether the file has been attacked. The computing device further determines whether to conduct a second level of ransomware detection based on the determination.

Abstract: In implementations of systems for generating digital content, a computing device implements a generation system to receive a user input specifying a characteristic for digital content. The generation system generates input text based on the characteristic for processing by a first machine learning model. Output text generated by the first machine learning model based on processing the input text is received. The output text describes a digital content component. The generation system generates the digital content component by processing the output text using a second machine learning model. The generation system generates the digital content including the digital content component for display in a user interface based on the characteristic.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that generate a dynamic cross-platform ask interface and utilize a cross-platform language processing model to provide platform-specific, contextually based responses to natural language digital text queries. In particular, in one or more embodiments, the disclosed systems utilize machine learning models to extract registered intents from digital text queries to identify platform-specific configurations associated with the registered intents. Utilizing the platform-specific configurations, the disclosed systems can generate tailored platform-specific requests for information, as well as customized end-user search results that cause client devices to efficiently, accurately, and flexibly render platform-specific search results.

Abstract: In addition to caching I/O operations at a host, at least some data management can migrate to the host. With host side caching, data sharing or deduplication can be implemented with the cached writes before those writes are supplied to front end storage elements. When a host cache flush to distributed storage trigger is detected, the host deduplicates the cached writes. The host aggregates data based on the deduplication into a “change set file” (i.e., a file that includes the aggregation of unique data from the cached writes). The host supplies the change set file to the distributed storage system. The host then sends commands to the distributed storage system. Each of the commands identifies a part of the change set file to be used for a target of the cached writes.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that generate a dynamic cross-platform ask interface and utilize a cross-platform language processing model to provide platform-specific, contextually based responses to natural language digital text queries. In particular, in one or more embodiments, the disclosed systems utilize machine learning models to extract registered intents from digital text queries to identify platform-specific configurations associated with the registered intents. Utilizing the platform-specific configurations, the disclosed systems can generate tailored platform-specific requests for information, as well as customized end-user search results that cause client devices to efficiently, accurately, and flexibly render platform-specific search results.

Abstract: In addition to caching I/O operations at a host, at least some data management can migrate to the host. With host side caching, data sharing or deduplication can be implemented with the cached writes before those writes are supplied to front end storage elements. When a host cache flush to distributed storage trigger is detected, the host deduplicates the cached writes. The host aggregates data based on the deduplication into a “change set file” (i.e., a file that includes the aggregation of unique data from the cached writes). The host supplies the change set file to the distributed storage system. The host then sends commands to the distributed storage system. Each of the commands identifies a part of the change set file to be used for a target of the cached writes.

Abstract: In addition to caching I/O operations at a host, at least some data management can migrate to the host. With host side caching, data sharing or deduplication can be implemented with the cached writes before those writes are supplied to front end storage elements. When a host cache flush to distributed storage trigger is detected, the host deduplicates the cached writes. The host aggregates data based on the deduplication into a “change set file” (i.e., a file that includes the aggregation of unique data from the cached writes). The host supplies the change set file to the distributed storage system. The host then sends commands to the distributed storage system. Each of the commands identifies a part of the change set file to be used for a target of the cached writes.

Abstract: In addition to caching I/O operations at a host, at least some data management can migrate to the host. With host side caching, data sharing or deduplication can be implemented with the cached writes before those writes are supplied to front end storage elements. When a host cache flush to distributed storage trigger is detected, the host deduplicates the cached writes. The host aggregates data based on the deduplication into a “change set file” (i.e., a file that includes the aggregation of unique data from the cached writes). The host supplies the change set file to the distributed storage system. The host then sends commands to the distributed storage system. Each of the commands identifies a part of the change set file to be used for a target of the cached writes.

Abstract: In addition to caching I/O operations at a host, at least some data management can migrate to the host. With host side caching, data sharing or deduplication can be implemented with the cached writes before those writes are supplied to front end storage elements. When a host cache flush to distributed storage trigger is detected, the host deduplicates the cached writes. The host aggregates data based on the deduplication into a “change set file” (i.e., a file that includes the aggregation of unique data from the cached writes). The host supplies the change set file to the distributed storage system. The host then sends commands to the distributed storage system. Each of the commands identifies a part of the change set file to be used for a target of the cached writes.

Abstract: Technology is disclosed for managing data in a distributed file system (“the technology”). The technology can gather metadata information associated with the data stored within the distributed file system, create a secondary namespace within a local file system of a local host using the gathered metadata information and store the gathered metadata information as files within the secondary namespace. Further, when a request to create a PPI of the distributed file system is received, the technology can create a PPI of the secondary namespace using a PPI creation feature of the local file system.

Abstract: In addition to caching I/O operations at a host, at least some data management can migrate to the host. With host side caching, data sharing or deduplication can be implemented with the cached writes before those writes are supplied to front end storage elements. When a host cache flush to distributed storage trigger is detected, the host deduplicates the cached writes. The host aggregates data based on the deduplication into a “change set file” (i.e., a file that includes the aggregation of unique data from the cached writes). The host supplies the change set file to the distributed storage system. The host then sends commands to the distributed storage system. Each of the commands identifies a part of the change set file to be used for a target of the cached writes.

Abstract: In addition to caching I/O operations at a host, at least some data management can migrate to the host. With host side caching, data sharing or deduplication can be implemented with the cached writes before those writes are supplied to front end storage elements. When a host cache flush to distributed storage trigger is detected, the host deduplicates the cached writes. The host aggregates data based on the deduplication into a “change set file” (i.e., a file that includes the aggregation of unique data from the cached writes). The host supplies the change set file to the distributed storage system. The host then sends commands to the distributed storage system. Each of the commands identifies a part of the change set file to be used for a target of the cached writes.

Abstract: A system and method for planning and configuring the components of a modular computing system is provided. In some embodiments, the method for planning an implementation of a modular computing system comprises presenting a user interface at a display device, the user interface including a plurality of user-selectable objects, each of the user-selectable objects representing a component of the modular computing system. A user selection is received via a user input device. The user selection is from among the user-selectable objects and specifies one of an enclosure, an existing component, and a future component of the modular computing system. A representation of the specified one of an enclosure, an existing component, and a future component is displayed at a display device. The user selection is verified with respect to an implementation guideline. An indicator of whether the user selection meets the implementation guideline is displayed at the display device.

Abstract: Embodiments described herein provide an object store that efficiently manages and services objects for use by clients of a distributed data processing system. Illustratively, the object store may be embodied as a quasi-shared storage system that interacts with nodes of the distributed data processing system to service the objects as blocks of data stored on a plurality of storage devices, such as disks, of the storage system. To that end, an architecture of the object store may include an on-disk layout, e.g., of the storage system, and an incore layout, e.g., of the nodes, that cooperate to illustratively convert the blocks to objects for access by the clients.

Abstract: Technology is disclosed for managing data in a distributed file system (“the technology”). The technology can gather metadata information associated with the data stored within a first file system, store the metadata information in association with a data identifier within a second file system, retrieve the stored metadata information using the data identifier from within the second file system and locate and retrieve the data associated with the metadata information from within first file system.

Abstract: Described herein is a system and method for a scalable crash-consistent snapshot operation. Write requests may be received from an application and a snapshot creation request may further be received. Write requests received before the snapshot creation request may be associated with pre-snapshot tags and write requests received after the snapshot creation request may be associated with post-snapshot tags. Furthermore, in response to the snapshot creation request, logical interfaces may begin to be switched from a pre-snapshot configuration to a post-snapshot configuration. The snapshot may then be created based on the pre-snapshot write requests and the post-snapshot write requests may be suspended until the logical interfaces have switched configuration.

Abstract: In addition to caching I/O operations at a host, at least some data management can migrate to the host. With host side caching, data sharing or deduplication can be implemented with the cached writes before those writes are supplied to front end storage elements. When a host cache flush to distributed storage trigger is detected, the host deduplicates the cached writes. The host aggregates data based on the deduplication into a “change set file” (i.e., a file that includes the aggregation of unique data from the cached writes). The host supplies the change set file to the distributed storage system. The host then sends commands to the distributed storage system. Each of the commands identifies a part of the change set file to be used for a target of the cached writes.