Abstract: A method for reducing write latency in a distributed file system. A write request that includes a volume identifier is received at a data management subsystem deployed on a node within a distributed storage system. The data management subsystem maps the volume identifier to a file system volume and maps the file system volume to a set of logical block addresses in a logical block device in a storage management subsystem deployed on the node. The storage management subsystem maps the logical block device to a metadata object for the logical block device on the node that is used to process the write request. The mapping of the file system volume to the set of logical block addresses in the logical block device enables co-locating the metadata object with the file system volume on the node, which reduces the write latency associated with processing the write request.

Abstract: Plant-based microfibrous scaffolds and a method for producing these scaffolds for culturing meat, providing a biocompatible, edible, and scalable 3D structure that supports high cell yield. The scaffold includes plant-based proteins, polysaccharides, and carbohydrates, eliminating the need for synthetic polymers and toxic solvents. The method for producing these scaffolds includes dissolving the components, creating a homogeneous solution, spinning the fibers using air volume and centrifugal forces, and heating to achieve crosslinking. The resulting scaffolds have controlled fiber diameters, thicknesses, and area densities, enhancing cell growth, nutrient diffusion, and structural integrity and provide an efficient and sustainable solution for large-scale cultured meat production.

Abstract: A distributed storage management system comprising nodes that form a cluster, a distributed block layer that spans the nodes in the cluster, and file system instances deployed on the nodes. Each file system instance comprises a data management subsystem and a storage management subsystem disaggregated from the data management subsystem. The storage management subsystem comprises a node block store that forms a portion of the distributed block layer and a storage manager that manages a key-value store and virtualized storage supporting the node block store. A file system volume hosted by the data management subsystem maps to a logical block device hosted by the virtualized storage in the storage management subsystem. The key-value store includes, for a data block of the logical block device, a key that comprises a block identifier for the logical block device and a value that comprises the data block.

Abstract: Systems and methods for scaling application and/or storage system functions of a distributed storage system based on a heterogeneous resource pool are provided. According to one embodiment, the distributed storage system has a composable, service-based architecture that provides scalability, resiliency, and load balancing. The distributed storage system includes a cluster of nodes each potentially having differing capabilities in terms of processing, memory, and/or storage. The distributed storage system takes advantage of different types of nodes by selectively instating appropriate services (e.g., file and volume services and/or block and storage management services) on the nodes based on their respective capabilities. Furthermore, disaggregation of these services, facilitated by interposing a frictionless layer (e.g.

Abstract: A method for reducing write latency in a distributed file system. A write request that includes a volume identifier is received at a data management subsystem deployed on a node within a distributed storage system. The data management subsystem maps the volume identifier to a file system volume and maps the file system volume to a set of logical block addresses in a logical block device in a storage management subsystem deployed on the node. The storage management subsystem maps the logical block device to a metadata object for the logical block device on the node that is used to process the write request. The mapping of the file system volume to the set of logical block addresses in the logical block device enables co-locating the metadata object with the file system volume on the node, which reduces the write latency associated with processing the write request.

Abstract: In various examples, data storage is managed using a distributed storage management system that is resilient. Data blocks of a logical block device may be distributed across multiple nodes in a cluster. The logical block device may correspond to a file system volume associated with a file system instance deployed on a selected node within a distributed block layer of a distributed file system. Each data block may have a location in the cluster identified by a block identifier associated with each data block. Each data block may be replicated on at least one other node in the cluster. A metadata object corresponding to a logical block device that maps to the file system volume may be replicated on at least another node in the cluster. Each data block and the metadata object may be hosted on virtualized storage that is protected using redundant array independent disks (RAID).

Abstract: Systems and methods for scaling application and/or storage system functions of a distributed storage system based on a heterogeneous resource pool are provided. According to one embodiment, the distributed storage system has a composable, service-based architecture that provides scalability, resiliency, and load balancing. The distributed storage system includes a cluster of nodes each potentially having differing capabilities in terms of processing, memory, and/or storage. The distributed storage system takes advantage of different types of nodes by selectively instating appropriate services (e.g., file and volume services and/or block and storage management services) on the nodes based on their respective capabilities. Furthermore, disaggregation of these services, facilitated by interposing a frictionless layer (e.g.

Abstract: Described herein are systems and techniques to facilitate efficient determination of operator sentiment based on unstructured textual data exchanged between computing devices via communications channels. Unstructured textual data may be preprocessed to remove extraneous data and prepare the textual content for input to a machine-learned model trained to determine one or more sentiment scores based on textual data. The output of the model may be used to determine sentiment data and/or trends and to determine one or more subsequent actions.

Abstract: A distributed storage management system comprising nodes that form a cluster, a distributed block layer that spans the nodes in the cluster, and file system instances deployed on the nodes. Each file system instance comprises a data management subsystem and a storage management subsystem disaggregated from the data management subsystem. The storage management subsystem comprises a node block store that forms a portion of the distributed block layer and a storage manager that manages a key-value store and virtualized storage supporting the node block store. A file system volume hosted by the data management subsystem maps to a logical block device hosted by the virtualized storage in the storage management subsystem. The key-value store includes, for a data block of the logical block device, a key that comprises a block identifier for the logical block device and a value that comprises the data block.

Abstract: In various examples, data storage is managed using a distributed storage management system that is resilient. Data blocks of a logical block device may be distributed across multiple nodes in a cluster. The logical block device may correspond to a file system volume associated with a file system instance deployed on a selected node within a distributed block layer of a distributed file system. Each data block may have a location in the cluster identified by a block identifier associated with each data block. Each data block may be replicated on at least one other node in the cluster. A metadata object corresponding to a logical block device that maps to the file system volume may be replicated on at least another node in the cluster. Each data block and the metadata object may be hosted on virtualized storage that is protected using redundant array independent disks (RAID).

Abstract: A method for allocating resources of fog nodes is disclosed, wherein the fog nodes are organized into at least one fog network. The method includes receiving a request from a client node, identifying requirements for fulfillment of the request, determining a location of the client node for fulfillment of the request, and identifying, from the identified requirements and the determined location, a cluster of fog nodes operable to fulfill the request. The method further includes selecting, from the identified cluster, fog nodes the resources of which are to be allocated to fulfilling the request by minimizing at least one of a number of clusters required to fulfill the request, a total time to fulfill requests received by the fog nodes, and/or a number of unfulfilled requests received by the fog nodes.

Abstract: A method for managing data storage using a distributed file system. A file system volume associated with a write request received at a data management subsystem is identified. A logical block device associated with the file system volume is identified. A plurality of data blocks is formed based on the write request. The plurality of data blocks is distributed across a plurality of node block stores in a distributed block layer of a storage management subsystem of the distributed file system. Each of the plurality of node block stores corresponds to a different node of a plurality of nodes in the distributed storage system. The storage management subsystem operates separately from but in communication with the data management subsystem.

Abstract: A method obtains service request information identifying computing device nodes invoked by users. Based on the service request information, sets of computing device nodes are identified, each set of computing device nodes includes computing device nodes invoked simultaneously or sequentially by one of the users. Communities are further identified based on a probability measure that is a measure of a probability of co-occurrence of two sets of computing device nodes. Each community has sets of computing device nodes each having the probability measure over a probability threshold in relation to at least one other set of computing device nodes in the community. Solutions are predicted for provision of services of the sets of computing device nodes of the communities. Each predicted solution for provision of services relates to a community and is determined based on shared knowledge of predicted solutions for provision of services relating to other communities.

Abstract: Techniques are provided for key-value store and file system integration to optimize key value store operations. A key-value store is integrated within a file system of a node. A log structured merge tree of the key-value store may be populated with a key corresponding to a content hash of a value data item stored separate from the key. A random distribution search may be performed upon a sorted log of the log structured merge tree to identify the key for accessing the value data item. A starting location for the random distribution search is derived from key information, a log size of the sorted log, and/or a keyspace size of a keyspace associated with the key.

Abstract: A method for managing data storage using a distributed file system. A file system volume associated with a write request received at a data management subsystem is identified. A logical block device associated with the file system volume is identified. A plurality of data blocks is formed based on the write request. The plurality of data blocks is distributed across a plurality of node block stores in a distributed block layer of a storage management subsystem of the distributed file system. Each of the plurality of node block stores corresponds to a different node of a plurality of nodes in the distributed storage system. The storage management subsystem operates separately from but in communication with the data management subsystem.

Abstract: Systems and methods for managing data storage using a distributed file system are provided. In one example, a file system instance is deployed virtually in a node of a distributed storage system. The file system instance has a dynamic configuration including a set of services corresponding to a cluster management subsystem and a storage management subsystem. The storage management subsystem operates independently of a data management subsystem of the distributed storage system as a result of disaggregation from the data management subsystem. The data management subsystem performs storage and block management functions based on requests received from an application layer. An additional service corresponding to either the data management subsystem or the storage management subsystem is deployed virtually to meet the demand for the additional service in response to determining the presence of a demand for the additional service and availability a set of resources corresponding to the additional service.

Abstract: Systems and methods for managing data storage using a distributed file system are provided. In one example, a relocation event is detected that indicates a relocation is to be initialized. The relocation is initialized by identifying a destination node of a distributed storage system for the relocation of a set of objects in a cluster database, including a logical block device, a corresponding logical aggregate, and a corresponding file system volume. A state of each of the set of objects is changed to offline. The set of objects are then relocated from an originating node of the distributed storage system to the destination node in which the corresponding logical aggregate is relocated after the logical block device and the corresponding file system volume is relocated after the logical aggregate. Finally, the state of each of the set of objects is changed to online.

Abstract: In various examples, data storage is managed using a distributed storage management system that is resilient. Data blocks of a logical block device may be distributed across multiple nodes in a cluster. The logical block device may correspond to a file system volume associated with a file system instance deployed on a selected node within a distributed block layer of a distributed file system. Each data block may have a location in the cluster identified by a block identifier associated with each data block. Each data block may be replicated on at least one other node in the cluster. A metadata object corresponding to a logical block device that maps to the file system volume may be replicated on at least another node in the cluster. Each data block and the metadata object may be hosted on virtualized storage that is protected using redundant array independent disks (RAID).

Abstract: Techniques are provided for key-value store and file system integration to optimize key value store operations. A key-value store is integrated within a file system of a node. A log structured merge tree of the key-value store may be populated with a key corresponding to a content hash of a value data item stored separate from the key. A random distribution search may be performed upon a sorted log of the log structured merge tree to identify the key for accessing the value data item. A starting location for the random distribution search is derived from key information, a log size of the sorted log, and/or a keyspace size of a keyspace associated with the key.

Abstract: A method, performed by communications system (100), for a prediction of an event. The first node (111) determines (603) a first set of nodes (121) and sends (604), a first indication of it. The second node (112) determines (606), a vote for one of the nodes in the first set of nodes (121) to be leader, and sends (607) a second indication indicating the determined vote. The second node (112) exchanges (610) a third indication indicating that the third node (113) is the leader. The third node (113) receives (612) from the other nodes in the first set of nodes (121), a respective fourth indication of a respective prediction on the event. The third node (113) updates (613) a machine-learning model of the event based on the received fourth indications and an own prediction, and sends (614), an indication of the updated machine-learning model to another node (114).