Abstract: A conditional locking system receives, from a request source, a request to execute an operation on a target resource on behalf of a client thread. The system generates a unique key based on an attribute of the request and searches a concurrency dictionary for an entry identifying the unique key. In response to determining that the concurrency dictionary does include an entry identifying the unique key, the system identifies a lock object associated in the entry with the unique key and transmits a request to acquire the lock object. Once the lock object associated with the unique key has been acquired on behalf of the client thread, the system transmits a lock acquisition confirmation to the request source.

Abstract: Described herein are systems and methods for sharing vitals among service replicas to enable processing of long running workflows within a container orchestration system. A method can provide a container orchestration system that provides within one or more container orchestration environments, a runtime for containerized workloads and services. The method can provide a healthbus within the container orchestration system, the healthbus comprising a memory. The method can deploy a plurality of pods within the container orchestration system, each pod comprising a memory. The method can periodically publish, by each pod, a health message to the healthbus, the health message comprising at least an indication of an identification of the pod and an indication of a time interval in which the pod has been active. The method can periodically query, by each pod, the healthbus to determine a world view of the container orchestration system.

Abstract: A method includes obtaining an agentic workflow from an artificial intelligence (AI) agent execution framework. An execution graph of the agentic workflow is generated by an LLM, including a multitude of nodes corresponding to a multitude of tasks. The tasks are partitioned by complexity into first complexity level tasks and second complexity level tasks. A first set of candidate models performs the first complexity level tasks. A first candidate model is assigned to a first task, based on an optimization function value of performance metrics of the first candidate model. A second set of candidate models performs the second complexity level tasks. A second candidate model is further assigned to a second task. The candidate models are assigned to respective nodes of the execution graph, to modify the execution graph. An updated agentic workflow is generated from the modified execution graph and deployed to the AI agent execution framework.

Abstract: A method includes receiving (1) a request to perform of one or more analytics operations, and (2) data associated with an industrial automation system, wherein the industrial automation system comprises a plurality of devices configured to perform a plurality of automation operations, and wherein each of the plurality of devices comprises a compute surface configured to perform one or more software tasks, determining a plurality of data processing tasks included in the one or more analytics operations, identifying a portion of the plurality of devices to perform the plurality of data processing tasks based on the compute surface available for each of the plurality of devices, deploying one or more containers to each of the portion of the plurality of devices, and providing first instructions to a first container of the one or more containers to perform a first data processing task of the plurality of data processing tasks.

Abstract: Some embodiments provide a method for operating a physical server in a network. The method stores multiple copies of a virtual machine (VM) image at a network-accessible storage. The method uses a first copy of the VM image as a virtual disk to execute a VM on a hypervisor of a first physical computing device. The method uses a second copy of the VM image as a virtual disk accessible via a smart network interface controller (NIC) of a second physical computing device to execute an operating system of the second physical computing device.

Abstract: Some embodiments provide a method of providing distributed storage services to a host computer from a network interface card (NIC) of the host computer. At the NIC, the method accesses a set of one or more external storages operating outside of the host computer through a shared port of the NIC that is not only used to access the set of external storages but also for forwarding packets not related to an external storage. In some embodiments, the method accesses the external storage set by using a network fabric storage driver that employs a network fabric storage protocol to access the external storage set. The method presents the external storage as a local storage of the host computer to a set of programs executing on the host computer. In some embodiments, the method presents the local storage by using a storage emulation layer on the NIC to create a local storage construct that presents the set of external storages as a local storage of the host computer.

Abstract: A system and method for orchestrating distributed operations to be executed in a distributed computing system with multiple virtual infrastructures uses a distributed operation descriptor to find any Cloud-Native Network Function (CNF) entry in the distributed operation descriptor. For each found CNF entry, a CNF descriptor is retrieved from a CNF catalog and parsed to find an overridable property for a CNF described in the CNF descriptor for which a property override is defined. Then, a target virtual infrastructure is selected from the multiple virtual infrastructures to perform a lifecycle management operation of the CNF. Instructions are then transmitted to a local operator in the target virtual infrastructure with the property override so that information regarding the CNF is transmitted to a local orchestrator of the target virtual infrastructure to perform the lifecycle management operation of the CNF at the target virtual infrastructure using the property override.

Abstract: Disclosed are a parallel method and device for convolution computation and data loading of a neural network accelerator. The method needs two input feature maps and two convolution kernel cache blocks, and sequentially stores the input feature maps and 64 convolution kernels into cache sub-blocks according to a loading length, so as to execute convolution computation and simultaneously load data of a next group of 64 convolution kernels.

Abstract: An example methodology includes declaring a node in a cluster of multiple nodes as a lead node to perform a cluster upgrade. While the lead node is performing the upgrade operations, the remaining nodes in the cluster continue to provide the cluster services. The upgrade operations on the lead node include migrating applications on the lead node to one of the other nodes in the cluster and performing upgrade of the lead node. In some cases, the upgrade operations include rebooting the lead node with a new OS image and installing new versions of the applications or re-installing the same applications on the lead node. The upgrade operations on the lead node may also include, once the lead node is upgraded, stopping applications and cluster services on the other nodes in the cluster, creating a new cluster that includes the lead node, and starting the applications on the lead node.

Abstract: A request for completion by an artificial intelligence (AI) model is received. Tasks required to fulfill the request are identified. An AI model is selected based on the tasks and capabilities of the AI model. The request is transmitted to the selected AI model. A response is received from the AI model. The response is then transmitter to a requester.

Abstract: A method of collecting and reporting inventory of resources deployed in a data center that includes hardware resources, a virtualization management software executed to provision virtual resources from the hardware resources, and a cloud management server executed to provision the virtual resources for tenants of the data center, includes the steps of: executing a first API call to the virtualization management software to collect first inventory of virtual resources deployed in the data center and a second API call to the cloud computing management software to collect second inventory of virtual resources deployed in the data center; storing the first and second inventory; and in response to an inventory request from a central orchestrator, initially sending a subset of the stored first and second inventory to the central orchestrator in accordance with parameters included in the inventory request, and thereafter sending updates to the subset to the central orchestrator periodically.

Abstract: A first hypervisor running on a first processor cluster is provided. During operation, the first hypervisor can determine a first set of processing nodes and a first memory unit of the first processor cluster in response to the booting up of a first Basic Input/Output System (BIOS) of the first processor cluster. The first hypervisor can discover a second hypervisor running on a second processor cluster comprising a second set of processing nodes and a second memory unit. The first hypervisor can operate, with the second hypervisor, a distributed system comprising the first and second sets of processing nodes and the first and second memory units. The first hypervisor can then operate, with the second hypervisor, a global virtual machine on the distributed system. The virtual memory space of the global virtual machine can be mapped to respective memory spaces of the first and second processor clusters.

Abstract: A system can be provided for deploying bare metal clusters that satisfy custom resource requests. For example, the system can receive from a client device, a custom resource request. The custom resource request can include a set of requirements for a bare metal cluster. The set of requirements can include a number of nodes for the bare metal cluster. The system can determine a set of resources that satisfies the set of requirements. The set of resources can include virtual Internet Protocol (IP) addresses and a set of baseband management controller (BMC) IP addresses. A number of BMC IP addresses in the set of BMC IP addresses can be equal to the number of nodes for the bare metal cluster. Additionally, the system can generate, based on the set of resources, a configuration file for the bare metal cluster and deploy, based on the configuration file, the bare metal cluster.

Abstract: In one embodiment, an apparatus includes: a plurality of execution circuits to execute and instruct micro-operations (?ops), where a subset of the plurality of execution circuits are capable of execution of a fused ?op; a fusion circuit coupled to at least the subset of the plurality of execution circuits, wherein the fusion circuit is to fuse at least some pairs of producer-consumer ?ops into fused ?ops; and a fusion throttle circuit coupled to the fusion circuit, wherein the fusion throttle circuit is to prevent a first ?op from being fused with another ?op based at least in part on historical information associated with the first ?op. Other embodiments are described and claimed.

Abstract: Job execution environment control techniques are described to manage policy selection and implementation to control use of job executors by a computing device, automatically and without user intervention. These techniques are usable to select a policy from a plurality of policies that is then used to control lifecycles of job executors of a job execution environment of a computing device. Further, these techniques are usable to respond dynamically to change the selected policy during runtime of the application in response to changes in the job execution environment.

Abstract: A signal processing device and a display apparatus for vehicles including the same are disclosed. The signal processing device includes a processor configured to perform signal processing for a display located in a vehicle, wherein the processor is configured to execute first to third virtual machines on a hypervisor in the processor, the second virtual machine is operated for a first display, the third virtual machine is operated for a second display, and the first virtual machine in the processor is configured to share at least some of data with the second virtual machine and the third virtual machine for processing of divided data. Consequently, the plurality of virtual machines for the plurality of displays in the vehicle may divide and process data.

Abstract: A method, computer program product and computer system for predicting excessive resource usage in a distributed computing environment is provided. A processor retrieves a portion of code associated with a workload in a distributed computing environment. A processor retrieves account information associated with the workload. A processor determines a likelihood that the workload is indicative of excessive resource usage based on the portion of code and the account information associated with the workload. In response to the likelihood of excessive resource usage exceeding a threshold, a processor reschedules the workload in the distributed computing environment.

Abstract: A computer-implemented method of monitoring programmatic containers performed through executing an agent processor is disclosed. The method comprises transmitting, by a processor, one or more deployment configurations from a monitoring server related to an application hosted in a container to a backend device, the processor receiving, from the backend device, a plurality of monitoring configurations for the application, the processor merging the plurality of monitoring configurations for the application into a merged monitoring configuration for the application, the processor providing the merged monitoring configuration for the application to the monitoring server, and the processor periodically receiving, from the monitoring server, telemetry data that characterizes one or more instances of the application.

Abstract: Systems and methods are provided for lock-free thread scheduling. Threads may be placed in a ring buffer shared by all computer processing units (CPUs), e.g., in a node. A thread assigned to a CPU may be placed in the CPU's local run queue. However, when a CPU's local run queue is cleared, that CPU checks the shared ring buffer to determine if any threads are waiting to run on that CPU, and if so, the CPU pulls a batch of threads related to that ready-to-run thread to execute. If not, an idle CPU randomly selects another CPU to steak threads from, and the idle CPU attempts to dequeue a thread batch associated with the CPU from the shared ring buffer. Polling may be handled through the use of a shared poller array to dynamically distribute polling across multiple CPUs.

Abstract: Example embodiments relate generally to systems and methods for continuous data protection (CDP) and more specifically to an input and output (I/O) filtering framework and log management system to seek a near-zero recovery point objective (RPO).