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 generic point of care based portable device and method thereof as a platform technology for detecting pathogenic infection via nucleic acid based testing achieving sample-to-result integration, comprising the following interconnected stand-alone modules: a thermal unit for executing piece-wise isothermal reactions in a pre-programmable concomitant fashion without necessitating in-between operative intervention; a colorimetric detection unit seamlessly interfaced with smartphone-app based analytics for detecting the target analyte.

Abstract: A generic point of care based portable device and method thereof as a platform technology for detecting pathogenic infection via nucleic acid based testing achieving sample-to-result integration, comprising the following interconnected stand-alone modules: a thermal unit for executing piece-wise isothermal reactions in a pre-programmable concomitant fashion without necessitating in-between operative intervention; a colorimetric detection unit seamlessly interfaced with smartphone-app based analytics for detecting the target analyte.

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: 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 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: 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).

Abstract: A method for managing computation load of a fog node is disclosed, wherein a computation capacity of the fog node is predicted to become unavailable to a fog network. The method comprises identifying a candidate set of nodes for computational load transfer from the fog node. The method further comprises obtaining a computation graph representing computation in the fog network, and using a learning model to identify a morphism from the obtained computation graph to a new computation graph, in which the fog node is not included. The identified morphism comprises a sequence of one or more morphing operations that replaces the fog node in the obtained computation graph with a topology of one or more nodes selected from the candidate set. The method further comprises causing computation performed at the fog node to be transferred to one or more nodes of the candidate set.

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 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: In one aspect, a method performed by a network node for predicting a probability of state change of a node (e.g., a fog node) in a network is provided. The network node determines a set of weights based on attributes of the node. The network node estimates the probability of state change of the node using the determined set of weights and a set of one or more attribute values related to the node where determining the set of weights includes maximizing an evaluation value associated to the node.

Abstract: Techniques are provided for hosting a key value store. A persistent storage backend is used to centrally host a key value store as disaggregated storage shared with a plurality of clients over a network fabric. A network storage appliance is connected to the plurality of clients over the network fabric, and is configured with a key value store interface. The key value store interface is configured to receive a key value command from a client. The key value store interface parses the key value command to identify a translation layer binding for a key value store targeted by the key value command. The key value store interface translates the key value command into a key value operation using the translation layer binding, and executes the key value operation upon the key value store.

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 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: 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: 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.