Abstract: Example implementations relate to updating an inference graph absent node locking. For example, a processor executing a first thread may receive a first task for updating a node of an inference graph stored by a storage device accessible to a second thread, the first task being assigned during a first iteration of a graph update loop. Absent locking the node from access by the second thread, the processor may generate a value for the node and update the node with the value. Based on detecting that each node of the inference graph has been updated, the processor may continue with a second iteration of the graph update loop.

Abstract: This application relates in general to a method, apparatus, and article of manufacture for providing secure and redundant communications and processing for a collection of Internet of Things having multi-state and programmable IoT devices. These multi-state edge devices typically operate in a default state with a default network configuration. When a scheduled or external event is detected by a IoT host server, this server reconfigures the operating state of the multi-state edge devices and supporting network components to support the needs corresponding to the detected event.

Abstract: Example implementations relate to updating an inference graph absent node locking. For example, a processor executing a first thread may receive a first task for updating a node of an inference graph stored by a storage device accessible to a second thread, the first task being assigned during a first iteration of a graph update loop. Absent locking the node from access by the second thread, the processor may generate a value for the node and update the node with the value. Based on detecting that each node of the inference graph has been updated, the processor may continue with a second iteration of the graph update loop.

Abstract: Disclosed herein is a commodity infrastructure operating system that manages and implements the resources and services found in the heterogeneous components of the common infrastructure using a fabric manager. A fabric manager managing computing resources in one or more platforms and one or more partitions residing on the platform by monitoring each platform and partitions, and issuing instructions to a hypervisor or other management agent on a platform to execute one or more platform management commands, such as commission a new partition onto a platform.

Abstract: One embodiment of a computer-implemented method for managing computing resources may include determining, by a computer, target computing resources to be configured with a platform. A determination, by the computer, may be made as to whether the target computing resources includes a management agent for managing the platform. The computer may cause a management agent to be installed on the target computing resources if the target computing resources are determined to not include a management agent, otherwise, the computer may not cause a management agent to be installed on the target computing resources. The computer may instruct the management agent to commission the platform on the target computing resources.

Abstract: Disclosed herein is a commodity infrastructure operating system that manages and implements the resources and services found in the heterogeneous components of the common infrastructure using a fabric manager. A fabric manager managing computing resources in one or more platforms and one or more partitions residing on the platform by monitoring each platform and partitions, and issuing instructions to a hypervisor or other management agent on a platform to execute one or more platform management commands, such as commission a new partition onto a platform.

Abstract: Disclosed herein is a method and a system for data storage and retrieval that is agnostic to the database models of any of the databases in embodiments of the common infrastructure. Data may be stored via one database, according to a corresponding model, and then stored such that the data may be retrieved by a second database with a different database model. The data is stored in key-value pairs, and is thus not stored according to any database model. However, metadata perspectives construct a set of retrieved data into a format expected by the database retrieving the set of data.

Abstract: Disclosed herein is a commodity infrastructure operating system that manages and implements the resources and services found in the heterogeneous components of the common infrastructure. The infrastructure operating system managing one or more services residing within an operating system image of a partition. The infrastructure operating system capable of providing a service of a first partition's operating system to a second partition's operating system when the second partition is in need of the service.

Abstract: A robust method for solving in a computer, optimization problems under uncertainty including the steps of: specifying the uncertainty as a hierarchical series of sets of constraints on parameters, with the parameters restricted to each constraint set forming an ensemble, and the hierarchy of constraints, represented as mathematical sets forming a hierarchy of ensembles, said hierarchy being based on subset, intersection or disjoint relationships amongst them; utilizing optimization techniques to create effective identify minimum and maximum bounds on the each objective function, said bounds depending on the constraints comprising each ensemble of parameters and being computed for each of the assumptions about the future; estimating a volume of candidate ensembles and relating the volume to one or more information theoretic measures; and utilizing information theoretic measures to analyze and improve the candidate iteratively refine the ensembles and associated by changing a specificity of the bounds on the ob

Abstract: A robust method for solving in a computer, optimization problems under uncertainty including the steps of: specifying the uncertainty as a hierarchical series of sets of constraints on parameters, with the parameters restricted to each constraint set forming an ensemble, and the hierarchy of constraints, represented as mathematical sets forming a hierarchy of ensembles, said hierarchy being based on subset, intersection or disjoint relationships amongst them; utilizing optimization techniques to create effective identify minimum and maximum bounds on the each objective function, said bounds depending on the constraints comprising each ensemble of parameters and being computed for each of the assumptions about the future; estimating a volume of candidate ensembles and relating the volume to one or more information theoretic measures; and utilizing information theoretic measures to analyze and improve the candidate iteratively refine the ensembles and associated by changing a specificity of the bounds on the ob