One or more aspects of the present disclosure relate to service level input/output scheduling to control central processing unit (CPU) utilization. Input/output (I/O) operations are processed with one or more of a first CPU pool and a second CPU pool of two or more CPU pools. The second CPU pool processes I/O operations that are determined to stall any of the CPU cores.

Some embodiments provide a method for a module executing on a Kubernetes node in a cluster. The method retrieves data regarding ongoing connections processed by a forwarding element executing on the node. The method maps the retrieved data to Kubernetes concepts implemented in the cluster. The method exports the retrieved data along with the Kubernetes concepts to an aggregator that receives data regarding ongoing connections from a plurality of nodes in the cluster.

This application describes a virtual machine deployment method and an operation and maintenance management (OMM) virtual machine. The method includes: obtaining, by an OMM virtual machine, a quantity and a specification of service virtual machines created in virtual network function application software to which the OMM virtual machine belongs; and determining, by the OMM virtual machine based on load that needs to be carried by the application software and the quantity and the specification of the service virtual machines, a module to be configured for each service virtual machine. The described implementations avoid or reduce waste of virtual machine resources.

In some examples, a system generates a neural network comprising logical identifiers of compute resources. For executing the neural network, the system maps the logical identifiers to physical addresses of physical resources, and loads instructions of the neural network onto the physical resources, wherein the loading comprises converting the logical identifiers in the neural network to the physical addresses.

Mechanisms for migration in a communication network of VNFs to VIMs. A method is performed by a migration controller. The method includes obtaining information of network complexity per VNF, information of VM usage per VNF, and information of processing capacity of compute nodes per VIM. The method further includes determining a mapping between the VNFs and the VIMs. The mapping defines at which of the VIMs each of the VNFs is instantiable. The method further includes ordering the VNFs in a list by matching the information of network complexity per VNF and the information of VM usage per VNF to the information of processing capacity of compute nodes per VIM and taking into account the mapping between the VNFs and the VIMs. The method further includes initiating migration of the VNFs to the VIMs in the order defined by the list.

An information processing apparatus includes: a memory; and a processor coupled to the memory and configured to: divide a job in units of computing nodes for a plurality of computing nodes; determine execution of scale-out or scale-in on the basis of a load in a case where each of the computing nodes is caused to execute a job obtained by the division; execute, in a case where determining execution of the scale-out, the scale-out according to the division of the job in units of computing nodes; and execute, in a case where determining execution of the scale-in, the scale-in according to the division of the job in units of computing nodes.

In an approach for generating differentiated workload telemetry data, a processor corresponds one or more services with a workload related telemetry generating an event emitter. A processor performs a correlation analysis of corresponding relationship and connection among connected resources and current traffic into and out of the one or more services. A processor labels domain context for each telemetry event. A processor communicates each telemetry event to a global event handler. A processor performs a cross-correlation in real-time of telemetry data with the global event handler. A processor updates a real-time differentiated workload report.

A computer-implemented method includes receiving a request to provision a set of storage volumes for a server cluster, wherein the request includes an identifier for the server cluster and generating a provisioning work ticket for each storage volume in the set of storage volumes, each provisioning work ticket including the identifier for the server cluster. The provisioning work tickets are provided to a message broker. Multiple volume provisioning instances are executed such that at least two of the volume provisioning instances operate in parallel with each other and such that each volume provisioning instance receives a respective provisioning work ticket from the message broker and attempts to provision a respective storage volume of the set of storage volumes for the server cluster in response to receiving the volume provisioning work ticket.

Techniques are described for sharing prepopulated container image caches among container execution environments to improve the performance of container launches. The container images used to prepopulate such a cache at a computing device supporting one or more container execution environments can include various container images that are used as the basis for a wide range of user-created containers such as, for example, container images representing popular operating system distributions, database servers, web-application frameworks, and so forth. Existing systems typically obtain these container images as needed at runtime when launching containers (for example, from a container registry or other external source), often incurring significant overhead in the container launch process. The use of a prepopulated container image cache can significantly improve the performance of container launches by making such commonly used container images available to container execution environments running at a computing device ahead of time.

Deferred reclaiming of secure guest resources within a computing environment is provided, which includes initiating, by a host of the computing environment, removal of a secure guest from the computing environment, while leaving one or more resources of the secure guest to be reclaimed asynchronous to the removal of the secure guest. The deferring also includes reclaiming the one or more secure guest resources asynchronous to the removal of the secure guest, where the one or more secure guest resources are available for reuse as the one or more secure guest resources are reclaimed asynchronous to the removal of the secure guest.