Abstract: A request to execute a workload that utilizes an amount of resources to be executed is received from a client device. Corresponding resources that are available at multiple non-uniform memory access (NUMA) nodes are received from one or more host systems. A particular NUMA node of the multiple NUMA nodes is identified in view of the particular NUMA node having available resources that are greater than the amount of resources to execute the workload. A scheduling hint is assigned to the workload that indicates that the particular NUMA node is to be used to execute the workload.

Abstract: Techniques of scheduling workload(s) on partitioned resources of host systems are described. The techniques can be used, for example, in a container-orchestration system. One technique includes retrieving information characterizing at least one schedulable partition and determining an availability and a suitability of one or more of the schedulable partition(s) for executing a workload in view of the information. Each of the schedulable partition(s) includes resources of one or more host systems. The technique also includes selecting one or more of the schedulable partition(s) to execute the workload in view of the availability and the suitability.

Abstract: A system and method for aggregating host machines into a single cloud node for workloads requiring excessive resources. The method includes providing a plurality of computing devices in association with a cloud service system. The method includes defining an aggregated node of the cloud service system corresponding to at least two computing devices of the plurality of computing devices. The method includes exposing an application programming interface (API) that is indicative of combined resources of the at least two computing devices of the plurality of computing devices. The method includes receiving a query to perform a workload requiring a set of resources that exceed the resources provided by each of the computing devices of the cloud service system. The method includes forwarding, to the aggregated node, the query to cause the at least two computing devices to perform the workload using the combined resources of the least two computing device.

Abstract: Examples described herein relate to a management system that determines which services to redeploy on one or more platforms. A platform can receive a configuration to perform during a failure of connectivity with a management system. The platform can monitor activity of one or more services. The platform can, based on failure of connectivity with the management system and recovery of connectivity with the management system, provide the monitored activity of one or more services to the management system to influence services re-deployed by the management system. In some examples, based on failure to re-establish a connection with the management system within an amount of time, the platform can connect with the management system using a secondary management interface.

Abstract: A system and method for aggregating host machines into a single cloud node for workloads requiring excessive resources. The method includes providing a plurality of computing devices in association with a cloud service system. The method includes defining an aggregated node of the cloud service system corresponding to at least two computing devices of the plurality of computing devices. The method includes exposing an application programming interface (API) that is indicative of combined resources of the at least two computing devices of the plurality of computing devices. The method includes receiving a query to perform a workload requiring a set of resources that exceed the resources provided by each of the computing devices of the cloud service system. The method includes forwarding, to the aggregated node, the query to cause the at least two computing devices to perform the workload using the combined resources of the least two computing device.

Abstract: An example system includes a processor and a node agent executing on the processor. The node agent is configured to receive a message indicative of a workload, a processor policy of the workload, and a number of processor threads requested for the workload. The node agent is also configured to allow simultaneous allocation of a processor core to the workload and another workload based on the processor policy being a first policy. The node agent is also configured to prevent simultaneous allocation of the processor core to the workload and the other workload based on the processor policy being a second policy or a third policy. The node agent is also configured to allow simultaneous allocation of the processor core for two or more of the requested processor threads based on the processor policy being the second policy.

Abstract: A request to execute a workload that utilizes an amount of resources to be executed is received from a client device. Corresponding resources that are available at multiple non-uniform memory access (NUMA) nodes are received from one or more host systems. A particular NUMA node of the multiple NUMA nodes is identified in view of the particular NUMA node having available resources that are greater than the amount of resources to execute the workload. A scheduling hint is assigned to the workload that indicates that the particular NUMA node is to be used to execute the workload.

Abstract: Systems and methods for intelligently scheduling a pod in a cluster of worker nodes are described. A scheduling service may account for previous scheduling attempts by considering the time and node (scheduling data) on which a preceding attempt to schedule a node were made, and factoring this information into the scheduling decision. Upon making a determination of a node on which to attempt to schedule the pod, the scheduling data may be updated with the time and node ID of the determined node and the pod may be scheduled on the determined node. In response to determining that the pod has been evicted from the determined node, the above process may continue iteratively until the pod has been successfully scheduled.

Abstract: Techniques of scheduling workload(s) on partitioned resources of host systems are described. The techniques can be used, for example, in a container-orchestration system. One technique includes retrieving information characterizing at least one schedulable partition and determining an availability and a suitability of one or more of the schedulable partition(s) for executing a workload in view of the information. Each of the schedulable partition(s) includes resources of one or more host systems. The technique also includes selecting one or more of the schedulable partition(s) to execute the workload in view of the availability and the suitability.

Abstract: Examples described herein relate to a management system that determines which services to redeploy on one or more platforms. A platform can receive a configuration to perform during a failure of connectivity with a management system. The platform can monitor activity of one or more services. The platform can, based on failure of connectivity with the management system and recovery of connectivity with the management system, provide the monitored activity of one or more services to the management system to influence services re-deployed by the management system. In some examples, based on failure to re-establish a connection with the management system within an amount of time, the platform can connect with the management system using a secondary management interface.

Abstract: Embodiments are generally directed to personalized smart navigation for motor vehicles. An embodiment of a smart navigation system for a motor vehicle includes a plurality of sensors of the motor vehicle to detect internal conditions, external conditions, or both for a motor vehicle; a controller to receive sensor data from the plurality of sensors, the sensor data including vehicle condition data, external conditions data, or both; and a navigation system to determine a route for an operator of the motor vehicle. Determination of the route by the navigation system is based at least in part on current conditions data received from a plurality of sources, the current conditions data including the navigation data for the navigation system and sensor data from the plurality of sensors.

Abstract: Methods and apparatus for workload feedback mechanisms facilitating a closed loop architecture. Platform telemetry data is collected from a server platform including one or more hardware components and running one or more virtual network functions (VNFs). A workload performance associated one or more VNFs or one or more applications associated with the one or more VNFs is monitored to detect whether the performance of a VNF or application fails to meet a performance criteria, such as a Service Level Agreement (SLA) metric, and corresponding performance indicia is generated by the VNF. Based on the platform telemetry data and the performance indicia, an operational configuration of one of more of the hardware components is adjusted to increase the workload performance to meet or exceed the performance criteria.

Abstract: Embodiments are generally directed to personalized smart navigation for motor vehicles. An embodiment of a smart navigation system for a motor vehicle includes a plurality of sensors of the motor vehicle to detect internal conditions, external conditions, or both for a motor vehicle; a controller to receive sensor data from the plurality of sensors, the sensor data including vehicle condition data, external conditions data, or both; and a navigation system to determine a route for an operator of the motor vehicle. Determination of the route by the navigation system is based at least in part on current conditions data received from a plurality of sources, the current conditions data including the navigation data for the navigation system and sensor data from the plurality of sensors.