Abstract: An autonomous compute storage device system includes a computing device and a storage device that is coupled to the computing device. The storage device receives a write instruction from a host processing system in the computing device that includes data for storage in a storage subsystem that is included in the storage device and, in response, performs a write operation to provide the data in a memory subsystem that is accessible to the storage device and store the data in the storage subsystem. If the storage device determines that an autonomous compute signature matches the data that was provided in the memory subsystem during the performance of the write operation, it executes an autonomous compute application to perform compute operations that are associated with the data that was provided in the memory subsystem during the performance of the write operation and generate at least one compute operation result.

Abstract: A method of determining an average duration for a time period that the at least one first asset, or a second asset operatively connected to the at least one first asset, is inoperable is described. The method comprises obtaining a location of at least one first asset associated with energy generation, transmission or storage. The method comprises obtaining a first probability of a natural adverse event occurring at the location of the at least one first asset. The method comprises obtaining a second probability of a non-natural adverse event occurring at the location of the at least one first asset. The method comprises determining a third probability of at least one damage state of the at least one first asset from the first and second probabilities.

Abstract: A microservice storage device system includes a computing device. A storage device is coupled to the computing device and includes storage device compute hardware coupled to a storage subsystem that is configured to store data. The storage device uses the storage device compute hardware to provide a storage device operating system. The storage device then uses the storage device operating system to provide a storage device management microservice that it uses to manage the storage subsystem. The storage device also uses the storage device operating system to provide at least one compute microservice that it uses to perform at least one compute operation. The storage device management microservice and the at least one compute microservice may each be provided in a respective container generated by the storage device operating system for each of the storage device management microservice and the at least one compute microservice.

Abstract: A reconfigurable microservice storage device system includes a computing device coupled to a plurality of storage devices that each include storage device compute hardware coupled to a storage subsystem that is configured to store data. The computing device configures the storage device compute hardware in each of a first subset of the plurality of storage devices to provide a respective first storage device operating system that includes a respective first storage device management microservice that manages the storage subsystem in that storage device. The computing device also configures the storage device compute hardware in each of a second subset of the plurality of storage devices to provide a respective second storage device operating system that includes a respective second storage device management microservice that manages the storage subsystem in that storage device, and at least one respective second compute microservice that performs at least one second compute operation.

Abstract: A data storage transformation system includes a data storage management device that is coupled to a data provisioning device and a storage system. The data storage management device receives first format data that includes a first data format from the data provisioning device, and predicts at least one processing operation that will be performed on the first format data. The data storage management device then determines a second data format for performing the at least one processing operation, and transforms the first format data to second format data that includes the second data format. The data storage management device then transmits the second format data for storage in the storage system.

Abstract: A tiered memory fabric workload performance optimization system includes a workload management device coupled to a processing fabric and a memory fabric. The workload management system receives a workload request to perform a workload including sub-workloads, and identifies a respective processing system in the processing fabric for performing each of the sub-workloads. The workload management device then determines, for use by each respective processing system identified for performing the sub-workloads, a respective memory system in the memory fabric to provide memory systems in different memory tiers in the memory fabric that optimize characteristic(s) of a workload performance pipeline provided by the respective processing systems identified for performing the sub-workloads.

Abstract: A method for allocating stranded computing resources, the method that includes obtaining, by a virtual machine manager, virtual machine parameters for a virtual machine, identifying, in a computing resource database, a stranded computing resource satisfying the virtual machine parameters, allocating, to the virtual machine, the stranded computing resource, and initiating the virtual machine using the stranded computing resource.

Abstract: A method for generating a domain topology for a system, the method that includes accessing a functional model database, identifying, in a computing resource database, a configuration of components matching a functional model in the functional model database, generating a domain for the configuration of components, in a domain topology, and generating a graphical hierarchy of the domain topology and providing it to a user.

Abstract: A method for executing a process in an enclave virtual machine, the method that includes receiving, by a primary virtual machine, a process request specifying a process, making a determination that the primary virtual machine is not configured to execute the process, and based on the determination, sending the process request to an enclave virtual machine, where the enclave virtual machine is configured to execute the process, and initiating execution of the process on the enclave virtual machine.

Abstract: A resource-capability-and-connectivity-based workload performance system includes a resource management system that is coupled to a plurality of processing systems and a plurality of memory systems. The resource management system determines resource capabilities provided by each of the plurality of processing systems, each of the plurality of memory systems, and connectivity between the plurality of processing systems and the plurality of memory systems.

Abstract: An autonomous compute storage device system includes a computing device and a storage device that is coupled to the computing device. The storage device receives a read instruction from a host processing system in the computing device that identifies data stored in a storage subsystem included in the storage device and, in response, performs a read operation to copy the data from the storage subsystem to a memory subsystem accessible to the storage device and provide the data to the host processing system. If the storage device determines that an autonomous compute signature matches the data that was copied to the memory subsystem during the performance of the read operation, it executes an autonomous compute application to perform compute operations that are associated with the data that was copied to the memory subsystem during the performance of the read operation and generate compute operation result(s).

Abstract: A shared memory fabric workload performance system includes a resource orchestrator device coupled to processing systems and memory systems that are configured to provide a shared memory fabric to each of the processing systems. The resource orchestrator device receives a request to perform a workload, identifies functions for performing the workload, and generates a DAG that identifies a respective processing resource type and a respective memory requirement for performing each of the functions. For each of the functions, the resource orchestrator device determines a respective processing resource provided by the processing systems that includes the processing resource type identified in the DAG for performing that function, and a respective memory resource provided by the memory systems that is accessible to that respective processing resource, and maps that respective memory resource to that respective processing resource based on the memory requirement identified in the DAG for performing that function.

Abstract: An autonomous compute storage device system includes a computing device and a storage device that is coupled to the computing device. The storage device receives a write instruction from a host processing system in the computing device that includes data for storage in a storage subsystem that is included in the storage device and, in response, performs a write operation to provide the data in a memory subsystem that is accessible to the storage device and store the data in the storage subsystem. If the storage device determines that an autonomous compute signature matches the data that was provided in the memory subsystem during the performance of the write operation, it executes an autonomous compute application to perform compute operations that are associated with the data that was provided in the memory subsystem during the performance of the write operation and generate at least one compute operation result.

Abstract: Methods and systems for managing data access based threats are disclosed. To manage the data access based threats, a data processing system may include a network interface controller (NIC). The network interface controller may present emulated storages that may be used for data storage. The emulated storage devices may utilize storage resources of storage devices. The NIC may actively screen for access patterns in use of the emulated storage devices that indicate compute complexes may be compromised. When doing so, the processing may be done locally on the NIC.

Abstract: A microservice storage device system includes a computing device. A storage device is coupled to the computing device and includes storage device compute hardware coupled to a storage subsystem that is configured to store data. The storage device uses the storage device compute hardware to provide a storage device operating system. The storage device then uses the storage device operating system to provide a storage device management microservice that it uses to manage the storage subsystem. The storage device also uses the storage device operating system to provide at least one compute microservice that it uses to perform at least one compute operation. The storage device management microservice and the at least one compute microservice may each be provided in a respective container generated by the storage device operating system for each of the storage device management microservice and the at least one compute microservice.

Abstract: A resource-capability-and-connectivity-based workload performance improvement system includes a resource management device coupled to processing systems and memory systems. The resource management device receives a request to perform a first workload, identifies a DAG that includes functions for performing the first workload, and uses first parameters in the DAG to configure the processing systems and the memory subsystems to perform the functions. Based on performance of the functions, the resource management device determines function modification(s) for at least one of the functions and, based on the function modification(s), modifies the first parameters included in the DAG to provide modified parameters. When the resource management device receives a request to perform a second workload, it identifies the DAG that includes the functions for performing the second workload, and uses the modified parameters in the DAG to configure the processing systems and the memory subsystems to perform the functions.

Abstract: A reconfigurable microservice storage device system includes a computing device coupled to a plurality of storage devices that each include storage device compute hardware coupled to a storage subsystem that is configured to store data. The computing device configures the storage device compute hardware in each of a first subset of the plurality of storage devices to provide a respective first storage device operating system that includes a respective first storage device management microservice that manages the storage subsystem in that storage device. The computing device also configures the storage device compute hardware in each of a second subset of the plurality of storage devices to provide a respective second storage device operating system that includes a respective second storage device management microservice that manages the storage subsystem in that storage device, and at least one respective second compute microservice that performs at least one second compute operation.

Abstract: A method is described which included receiving (S1) a number of object images (IMn) of an object (1). Each object image (IMn) corresponds to a different view direction (n). The object images include first (IM1) and second (IM2) object images corresponding to first (n1) and second (n2) directions. The method also includes determining (S2) a mesh (20) corresponding to the target region (5) of the object (1) surface (2) based on a first subset (MESH) of the number of object images (IMn) which includes two or more object images (IMn) of the number of object images (IMn). The method also includes determining (S3) diffuse (DFUV) and specular (SPUV) maps corresponding to the target region (5) of the object (1) surface (2) based on processing a second subset (REFLECT) of the object images (IMn) using a deep learning neural network model trained to estimate diffuse (DFn) and specular (SPn) albedo components based on an input image (IMn).

Abstract: A tiered memory fabric workload performance optimization system includes a workload management device coupled to a processing fabric and a memory fabric. The workload management system receives a workload request to perform a workload including sub-workloads, and identifies a respective processing system in the processing fabric for performing each of the sub-workloads. The workload management device then determines, for use by each respective processing system identified for performing the sub-workloads, a respective memory system in the memory fabric to provide memory systems in different memory tiers in the memory fabric that optimize characteristic(s) of a workload performance pipeline provided by the respective processing systems identified for performing the sub-workloads.

Abstract: A data transformation/processing system includes a data transformation/processing management device coupled to a client device, processing systems, memory systems, and storage systems. The data transformation/processing management device receives a request from the client device to process first data to generate second data, determines a first subset of the processing systems for performing processing operations to process the first data, and identifies a data path for the processing operations that includes storage locations provided by subsets of the memory systems and the storage systems.