A software switch and a method performed by the software switch are disclosed. The software switch receives, from a node deploying a virtual machine, a request for a virtual port to be polled by the virtual machine. The request includes a Central Processing Unit “CPU” identity identifying a CPU on which the virtual machine executes. The request includes an indication of a clock frequency at which the CPU is set to operate. The software switch determines a number of packets in a queue associated with the virtual port. The software switch adjusts the clock frequency of the CPU based on the number of packets in the queue. A corresponding computer program and a computer program carrier are also disclosed.

Examples herein relate to developing an orchestration plan. Examples disclose the development of a representation of a set of services wherein each service relates to other services via different types of relationships. The examples apply a set of dependency rules for each type of relationship at each service within the set of services such that the application of the set of dependency rules creates inter-service dependencies between state transitions of the set of services. Based on the creation of the inter-service dependencies, the orchestration plan is developed which includes a sequenced order of the state transitions for the set of services.

Systems described herein may allow for the intelligent configuration of containers onto virtualized resources. As described, systems described herein may generate configurations based on received parameters for utilization to configure (e.g., install, instantiate, etc.) virtualized resources. Once generated, a configuration may be selected according to determined selection parameters and/or intelligent selection techniques.

Disclosed are various examples for platform independent graphics processing unit (GPU) profiles for more efficient utilization of GPU resources. A virtual machine configuration can be identified to include a platform independent graphics computing requirement. Hosts can be identified as available in a computing environment based on the platform independent graphics computing requirement. The virtual machine can be placed on a host based on a consideration of host priority.

The present disclosure relates to a method and system for optimizing a parameter configuration of a distributed computing job. The method includes: obtaining job programs of different distributed computing jobs, and determining a key parameter configuration set; obtaining a cluster status during execution of the distributed computing job, randomly generating a sample data set based on the key parameter configuration set and the cluster status, and establishing a performance prediction model; correcting the performance prediction model by using a multi-objective genetic algorithm and an optimization module configured with an optimal configuration selection strategy; obtaining a job program of a to-be-optimized distributed computing job and a cluster status during execution of the to-be-optimized distributed computing job, and determining a to-be-optimized key parameter configuration item combination; and inputting, to the performance prediction model, the to-be-optimized key parameter configuration item combination and the cluster status during execution of the to-be-optimized distributed computing job, and outputting a key parameter configuration item combination with a shortest execution time. The present disclosure can rapidly and effectively optimize the key parameter configuration.

A query operation is performed to obtain information for a select entity of a computing environment. The information includes boost information of one or more boost features currently available for the select entity. The one or more boost features are to be used to temporarily adjust one or more processing attributes of the select entity. The boost information obtained from performing the query operation is provided in an accessible location to be used to perform one or more actions to facilitate processing in the computing environment.

A system of monitoring and optimizing computing resources usage for computing application may include predicting a first performance metric for job load capacity of a computing application for optimal job concurrency and optimal resource utilization. The system may include generating an alerting threshold based on the first performance metric. The system may further include, in response to a difference between the alerting threshold and a job load of the computing application within an interval exceeding a threshold, predicting a second performance metric for job load capacity of the computing application for optimal job concurrency and optimal resource utilization. The system may further include, in response to a difference between the first performance metric and the second performance metric exceeding a difference threshold, updating the alerting threshold with a job load capacity with the optimal resource utilization rate corresponding to the second performance metric.

In one or more embodiments, one or more systems, one or more methods, and/or one or more processes may determine first thermal attribute values associated with multiple information handling systems (IHSs) with respect to a period of time as the IHSs execute a first workload; determine multiple variance ranges respectively associated with the first thermal attributes; periodically determine second thermal attribute values associated with the IHSs as the IHSs execute a second workload; determine that a thermal attribute value of the second thermal attribute values exceeds a respective variance range of the variance ranges as a first information handling system (IHS) of the IHSs executes the second workload; generate an alert based at least on the thermal attribute value exceeding the respective variance range; and in response to the alert, transfer at least a portion of the second workload from the first IHS to a second IHS of the IHSs.

An example method of placing a virtual machine (VM) in a cluster of hosts is described. Each of the hosts having a hypervisor managed by a virtualization management server for the cluster, the hosts separated into a plurality of nonuniform memory access (NUMA) domains. The method including: comparing a virtual central processing unit (vCPU) and memory configuration of the VM with physical NUMA topologies of the hosts; selecting a set of the hosts spanning at least one of the NUMA domains, each host in the set of hosts having a physical NUMA topology that maximizes locality for vCPU and memory resources of the VM as specified in the vCPU and memory configuration; and providing the set of hosts to a distributed resource scheduler (DRS) executing in the virtualization management server, the DRS configured to place the VM in a host selected from the set of hosts.

An adaptive idle detection method determines whether software defined data centers (SDDCs) in a hyperconverged infrastructure (HCI) environment are idle. Idleness may be quantified via a coefficient of variation (CV) against resource usage, so as to adapt the idle detection method to SDDCs with different hardware specifications and workloads. Management overhead may also be filtered out by the idle detection method, and the idle detection method may use idleness scores to further reduce overhead.