Abstract: A method includes receiving, by a producer thread of a plurality of producer threads, an offer request associated with an item. The producer thread increases a sequence and determines (i) a chunk identifier of a memory chunk from a pool of memory chunks and (ii) a first slot position in the memory chunk to offer the item. The producer thread also writes the item into the memory chunk at the first slot position. Then, a first consumer thread of a plurality of consumer threads determines the first slot position of the item and consumes the item at the first slot position. A second consumer thread consumes another item at a second slot position in the memory chunk and recycles the memory chunk.

Abstract: A method and a device for operating instance resources are provided. The method includes receiving an operation request, acquiring an instance resource associated with the target resource according to an instance arranging property, executing the operation on the instance resource associated with the target resource, and transmitting an operation response. The operation request includes a type of an operation and a target resource.

Abstract: A system and method for use with a cloud computing environment, for determining a cloud score associated with a resource configuration, limits, or shape, for example that of a virtual machine or host provided within the environment. The described approach provides a set of infrastructure workloads, for use in assessing a cloud infrastructure and resources provided thereby, so that a full spectrum of aspects of the cloud infrastructure can be covered by workload testing. The workloads can be used to generate metrics associated with resource usage. The system can then consider one or more metrics that are associated with performance of a particular resource configuration or shape, for example that of a virtual machine or (e.g., bare metal) host hosted by a cloud provider, and determine a score that is indicative of the relative performance of that configuration or shape for a particular workload configuration.

Abstract: The system obtains a first acyclic graph including multiple nodes and edges connecting the multiple nodes. A process to create a weave of the first acyclic graph produces a matching weave when executed on the first acyclic graph by different computing devices. An addition of a node to the first acyclic graph produces a second acyclic graph. The addition of the node to the first acyclic graph changes the weave of the first acyclic graph. The system obtains a process to reach a global consensus among the multiple computing devices. The process indicates a criterion to satisfy prior to reaching the global consensus and determines whether the multiple computing devices in the network satisfy the criterion. Upon determining that the criterion is satisfied, the system adds a finalize node to the first acyclic graph to obtain a third acyclic graph. A weave of the third acyclic graph cannot change.

Abstract: Certain embodiments described herein are directed to methods and systems for adding one or more nodes to a first cluster including a first node in a computer system. A method performed by the first node comprises receiving a first request from a second node to join the first cluster. The method also comprises retrieving a first cluster configuration associated with the first cluster from a distributed database through a first database server (DBS) and creating a second cluster configuration using the first cluster configuration and information received from the second node as part of the request. The method further comprises populating a first one or more local trust stores of a first one or more processes executing on the first node with a second one or more security certificates of a second one or more processes executing on the second node. The method further comprises writing the second cluster configuration to the distributed database and returning the second cluster configuration to the second node.

Abstract: There is provided a method and apparatus for vectorized resource scheduling using multi-dimensional hierarchical arrays with a uniform data type. According to embodiments, the hierarchy of resources may be represented as a multi-dimensional array of a uniform data type in a scheduling context. This data structure is known as a tensor. In various embodiments, a tensor may be used to model each of resource usage, requested resources, resource capabilities, tenant resource usage, etc. There is provided tensor scheduling operations (e.g. tensor scheduling algorithms) to make use of this resource model. Some embodiments may exploit specialized hardware, such as GPUs and tensor-processing units, in order to accelerate the tensor scheduling operations.

Abstract: A load balancing method, apparatus and device for a parallel model training task, and a computer-readable storage medium, includes: acquiring data traffic and a theoretical computational amount of each network layer in a target model; determining a theoretical computing capability of each computing device, and obtaining an initial computational amount corresponding to each computing device according to the theoretical computing capability and the theoretical computational amount; performing a load balancing operation according to the initial computational amount by using multiple device critical layer position division rule, so as to obtain a plurality of initial balancing schemes; compiling statistics on time performance parameters corresponding to the initial balancing schemes, and determining an intermediate balancing scheme from the initial balancing schemes according to the time performance parameters; and adjusting the intermediate balancing scheme according to the data traffic, so as to obtain a final

Abstract: Techniques are described for efficiently distributing across multiple computing resources satisfiability modulo theories (SMT) queries expressed in propositional logic with string variables. As part of the computing-related services provided by a cloud provider network, many cloud providers also offer identity and access management services, which generally help users to control access and permissions to the services and resources (e.g., compute instances, storage resources, etc.) obtained by users via a cloud provider network. By using resource policies, for example, users can granularly control which identities are able to access specific resources associated with the users' accounts and how those identities can use the resources. The ability to efficiently distribute the analysis of SMT queries expressed in propositional logic with string variables among any number of separate computing resources (e.g., among separate processes, compute instances, containers, etc.

Abstract: A non-contact method of characterizing the isostatic strength of a ceramic member or article includes capturing a digital image of the ceramic article, and then forming a two-dimensional representation of the ceramic article and the web therein based on the captured digital image. The method also includes performing finite-element analysis on the two-dimensional representation of the ceramic article using a select amount of simulated isostatic pressure to determine a maximum stress value within the two-dimensional representation of the web. The method further includes using the maximum stress value to characterize the isostatic strength of the ceramic article.

Abstract: Examples are disclosed that relate to a disaggregated memory pool. One example provides a memory system comprising a memory controller and memory attached to the memory controller and forming at least a portion of a disaggregated memory pool, the disaggregated memory pool including a plurality of slices that are each dynamically assigned to a respective compute node. The memory system is configured to receive a request to adjust an assignment of the memory pool to a requesting compute node, where the portion of the memory pool includes an unassigned slice that can satisfy the request, assign at least part of the unassigned portion to the requesting compute node, and where the portion of the memory pool does not include an unassigned slice that can satisfy the request, cause a request to be directed to another compute node to free at least one slice to the such compute node.

Abstract: A process and device for assigning a digital-assistant task to a mobile computing device in response to an incident. A mobile computing device determines digital-assistant tasks to be executed in response to the incident. Further the mobile computing device transmits a request for digital-assistant capability information to other mobile computing devices implementing respective digital assistants. Based on the digital-assistant capability received from one or more of the other mobile computing devices, the mobile computing device maps at least one of the digital-assistant tasks to the one or more of the other mobile computing devices. The mobile computing device assigns the at least one of the digital-assistant tasks to the mapped one or more of the other mobile computing devices and further transmits an instruction to execute the assigned digital-assistant tasks to the mapped one or more of the other mobile computing devices.

Abstract: A computing device receives a target value for a design quality metric. The target value indicates a desired quality of a design of an experiment and is related to an input parameter by a response curve. The computing device also validates the target value as being in a feasibility range for the design quality metric, and if so, determines a candidate value for the input parameter that yields a calculated value for the design quality metric. To determine the calculated value, the computing device iteratively adjusts the candidate value until the calculated value is within a predetermined tolerance of the target value. The computing device then updates an interactive graph visually representing the calculated value for the design quality metric as a function of the candidate value for the input parameter and outputs a graph visually representing the relationship that exists between the candidate value and the calculated value.

Abstract: A device, that provides serverless computing, receives a request to execute multiple jobs, and determines criteria for each of the plurality of jobs, wherein the criteria for each of the multiple jobs includes at least one of job posting criteria, job validation criteria, job retry criteria, or a disaster recovery criteria. The device stores information associated with the multiple jobs in a repository, wherein the information associated with the multiple jobs includes the criteria for each of the multiple jobs. The device provides a particular job, of the multiple jobs, to a cluster computing framework for execution, determines modified criteria for the particular job, and provides the modified criteria for the particular job to the cluster computing framework. The device receives, from the cluster computing framework, information indicating that execution of the particular job is complete, and validates a success of completion of the execution of the particular job.

Abstract: A digital environment includes multiple computing nodes and a scheduling system that assigns workloads to computing nodes. The scheduling system includes an equivalence-class-based resource usage prediction system that receives a workload request and predicts an equivalence class for that workload request based on resource usage over time by the workload request or metadata associated with the workload request. The scheduling system also includes a workload assignment system that assigns the workload request to one or more of the computing nodes based on the predicted equivalence class. The number of equivalence classes is small relative to the total number of workloads that are scheduled (as an example, 10 to 15 equivalence classes for a total number of workloads in the tens or hundreds of thousands).

Abstract: In a computer-implemented method for allocating a host of a pre-configured hyper-converged computing device to a workload domain, a pre-configured hyper-converged computing device including a plurality of hosts is managed, wherein the plurality of hosts is allocable to workload domains. A pool of unallocated hosts of the plurality of hosts is managed within the pre-configured hyper-converged computing device, wherein hosts of the pool of unallocated hosts have associated hypervisor versions. An allocation request to allocate at least one host of the pool of unallocated hosts to a workload domain is received, the allocation request including a requested hypervisor version of at least one host upon allocation. The at least one host is updated to the requested hypervisor version of the allocation request while the at least one host is in the pool of unallocated hosts.

Abstract: A computerized efficient data processing management method for imaging applications first performs a data flow graph generation by computing means using at least one image data and at least one requested task to generate a data flow graph. The method then applies a task execution scheduling using the data flow graph generated, a caching system configuration, the at least one image data and at least one requested task to schedule execution of the at least one requested task to generate task execution output. In addition, an adaptive data processing method performs caching system update and an optimal data processing method further performs data flow graph update.

Abstract: A cloud management system can be configured to provide a cloud computing environment in response to a request for an execution platform. The cloud management system can be configured to determine a set of resources from non-dedicated cloud controlled resources or third-party resources that meet specifications of the requested execution platform. The cloud management system can be configured to create the on-demand cloud from the determined set of resources to serve as the execution platform.

Abstract: A container management utility tool may deploy an object model that may persist one or more container dependencies, relationships, or a collection of containers that may represent a system function. Through a web front-end interface, for example, the containers may be started, stopped, or restarted in a specific order, and the tool automatically determines the additional containers that need to be started in order to maintain the integrity of the environment. Through the web interface, for example, the object model may be managed, and start-up orders, container dependencies, or collection maps of containers that represent a system function may be updated. For containers that may not start under load, the object model may block access to the containers until the containers are fully initialized.

Abstract: A control device executes a step of starting a computation processing of a prediction model; a step of computing a remaining processing time until the computation processing is completed after starting the computation processing of the prediction model; a step of determining whether the determination of the command value based on an output obtained from the prediction model is made within a control timing for controlling the operation of manufacturing by the manufacturing device, on the basis of a computed remaining processing time; and a step of stopping, when it is determined that the determination of the command value is not made within the control timing, the computation processing of the prediction model, determining the command value on the basis of a value of an intermediate result of the computation processing, and controlling the operation of the manufacturing device on the basis of the determined command value.

Abstract: Systems, methods and computer-readable media are provided for determining the modality for transferring, lifting, or repositioning (TLR) a human patient in a health care setting contexts. In some cases, a model-based recursive partitioning and Bradley-Terry regression is applied, which may be optionally parallelized so as to determine statistical associations with various factors, such as caregiver attributes, care venue, and patient attributes. One embodiment determines a Bradley-Terry regression model from the recursive partitioning which may be incorporated into a TLR selection decision-support tool or otherwise utilized to identify the optimal modality.