A method, apparatus, and system for controlling an aircraft. A target state for the aircraft is identified. A current mission state is determined for the aircraft. A sequence of actions is selected from a pool of potential actions to reach the target state from the current mission state for the aircraft. The sequence of actions is selected based on the current mission state. The actions in the sequence of actions for which preconditions for the actions that have been met are performed. The actions are performed in an order defined by the sequence of actions.

Systems and methods relating to leveraging inactive computing resources are discussed. An example system may include one or more computing nodes having an active state and an inactive state, one or more processors, and a memory. The memory may contain instructions therein that, when executed, cause the one or more processors to identify a task to be performed by the one or more computing nodes based upon a received request. The instructions may further cause the one or more processors to create one or more sub-tasks based upon the task and schedule the one or more sub-tasks for execution on the one or more computing nodes during the inactive state. The instructions may further cause the one or more processors to collate the one or more sub-tasks into a completed task, and generate a completed task notification based upon the completed task.

A host Virtual Machine Monitor (VMM) operates “blindly,” without the host VMM having the ability to access data within a guest virtual machine (VM) or the ability to access directly control structures that control execution flow of the guest VM. Guest VMs execute within a protected region of memory (called a key domain) that even the host VMM cannot access. Virtualization data structures that pertain to the execution state (e.g., a Virtual Machine Control Structure (VMCS)) and memory mappings (e.g., Extended Page Tables (EPTs)) of the guest VM are also located in the protected memory region and are also encrypted with the key domain key. The host VMM and other guest VMs, which do not possess the key domain key for other key domains, cannot directly modify these control structures nor access the protected memory region. The host VMM, however, using VMPageIn and VMPageOut instructions, can build virtual machines in key domains and page VM pages in and out of key domains.

A system and method for dynamically and automatically sharing resources of a coprocessor AI accelerator based on workload changes during training and inference of a plurality of neural networks. The method comprising steps of receiving a plurality of requests from each neural network and high-performance computing applications (HPCs) through a dynamic adaptive scheduler module. The dynamic adaptive scheduler module morphs the received requests into threads, dimensions and memory sizes. The method then receives the morphed requests from the dynamic adaptive scheduler module through client units. Each of the neural network applications is mapped with at least one of the client units on a graphics processing unit (GPU) hosts. The method then receives the morphed requests from the plurality of client units through a plurality of server units. Further, the method receives the morphed request from the plurality of server units through one or more coprocessors.

Systems and methods for processing computing jobs of a managed network are disclosed. Each of one or more worker nodes may implement a scheduler thread and a pool of worker threads. Upon waking up from a sleep state, the scheduler thread may determine a current number of jobs in an in-memory job queue that are waiting for processing by a worker thread, and may compute a job-completion rate of jobs processed by threads of the pool. Based on the job-completion rate, the scheduler thread may perform one or more of retrieving more jobs from a centralized database job queue and adding them to the in-memory job queue; removing one or more jobs from the in-memory job queue and returning them to the database job queue; leaving the in-memory job queue unchanged; or adjusting the duration of the sleep-interval timer. The scheduler thread may then return to a sleep state.

Embodiments of the present disclosure relate to requirement-based resource sharing in a computing environment. In an embodiment, a computer-implemented method is disclosed. According to the method, it is determined whether a plurality of services to be provisioned in a computing environment match with each other in resource consumption based on respective resource requirements of the plurality of services. In response to determining that the plurality of services match with each other, a resource pool is allocated in the computing environment to be shared by the plurality of services. The resource pool has a resource quota determined based on at least one of the resource requirements. The plurality of services is caused to be provisioned in the computing environment using the resource pool. In other embodiments, a computer system and a computer program product are disclosed.

In-flight operations in an inbound data path from a source memory to a convolution hardware circuit increase computational throughput when performing convolution calculations, such as pooling and element-wise operations. Various operations may be performed in-line within an outbound data path to a target memory. Advantageously, this drastically reduces extraneous memory access and associated read-write operations, thereby, significantly reducing overall power consumption in a computing system.

A method for scheduling services in a computing environment includes receiving a service scheduling request corresponding to the computing environment and identifying a resource pool and a set of compliance requirements corresponding to the computing environment. The method continues by identifying target resources within the resource pool, wherein target resources are resources which meet the set of compliance requirements, and subsequently identifying a set of available target resources, wherein available target resources are target resources with scheduling availability. The method further includes analyzing the set of available target resources to determine a risk score for each available target resource and selecting one or more of the set of available target resources according to the determined risk scores. The method continues by scheduling a service corresponding to the service scheduling request on the selected one or more available target resources.

A data processing system is disclosed that includes one or more processors that can perform producer processes to produce work and consumer processes that can consume work produced by a producer process. The system includes a pool of plural communication resources that may be used for communications between a producer process and a consumer process. The system tracks the usage of communication resources of the pool of communication resources, and allocates a communication resource from the pool of communication resources based on the tracking.

The disclosure herein describes management of distribution of resources between a global pool and an associated plurality of local pools using a flush threshold. A request for resources is received at the global pool from a local pool, the request indicating a requested quantity of resources. Based on the received request, it is determined that available resources in the global pool are below a flush threshold of the global pool. Based on this determination, flush instructions are sent to the local pools, wherein the flush instructions instruct each local pool to release unused resources (e.g., available to be released) to the global pool. Based on the available resources of the global pool then exceeding the requested quantity of resources and/or the flush threshold, resources of the global pool are allocated to the requesting local pool, whereby the local pool is enabled to use the allocated resources.