Vmware, Inc.

Pranshu Jain

Zhelong Pan

Sahan Bamunavita Gamage

Rajesh Venkatasubramanian

Yaniv Ben-Itzhak

Aditi Ghag

Thaleia Dimitra Doudali

Dragos Misca

Adrian Drzewiecki

Benjamin J Corrie

Mark Russell Johnson

Maarten Wiggers

Sabareesh Subramaniam

Arpitha Dondemadahalli Ramegowda

Jianjun Shen

Sujata Banerjee

Yongzhe Fan

A method for containerized workload scheduling can include monitoring network traffic between a first containerized workload deployed on a node in a virtual computing environment to determine affinities between the first containerized workload and other containerized workloads in the virtual computing environment. The method can further include scheduling, based, at least in part, on the determined affinities between the first containerized workload and the other containerized workloads, execution of a second containerized workload on the node on which the first containerized workload is deployed.

Containerized workload scheduling

An example method of scheduling a workload in a virtualized computing system including a host cluster having a virtualization layer directly executing on hardware platforms of hosts is described. The virtualization layer supports execution of virtual machines (VMs) and is integrated with an orchestration control plane. The method includes: receiving, at the orchestration control plane, a workload specification for the workload; selecting, at the orchestration control plane, a plurality of nodes for the workload based on the workload specification, each of the plurality of nodes implemented by a host of the hosts; selecting, by the orchestration control plane in cooperation with a virtualization management server managing the host cluster, a node of the plurality of nodes; and deploying, by the orchestration control plane in cooperation with the virtualization management server, the workload on a host in the host cluster implementing the selected node.

Scheduling workloads in a container orchestrator of a virtualized computer system

The present disclosure relates generally to virtualization, and more particularly to techniques for deploying containers in a virtual environment. The container scheduling can be based on information determined by a virtual machine scheduler. For example, a container scheduler can receive a request to deploy a container. The container scheduler can send container information to the virtual machine scheduler. The virtual machine scheduler can use the container information along with resource utilization of one or more virtual machines to determine an optimal virtual machine for the container. The virtual machine scheduler can send an identification of the optimal virtual machine back to the container scheduler so that the container scheduler can deploy the container on the optimal virtual machine.

Techniques for container scheduling in a virtual environment

A method for containerized workload scheduling can include determining a network state for a first hypervisor in a virtual computing cluster (VCC). The method can further include determining a network state for a second hypervisor. Containerized workload scheduling can further include deploying a container to run a containerized workload on a virtual computing instance (VCI) deployed on the first hypervisor or the second hypervisor based, at least in part, on the determined network state for the first hypervisor and the second hypervisor.

Microservice scheduling

Workloads are scheduled on a common set of resources distributed across a cluster of hosts using at least two schedulers that operate independently. The resources include CPU, memory, network, and storage, and the workloads may be virtual objects, including VMs, and also operations including live migration of virtual objects, network file copy, reserving spare capacity for high availability restarts, and selecting hosts that are to go into maintenance mode. In addition, the at least two independent schedulers are assigned priorities such that the higher priority scheduler is executed to schedule workloads in its inventory on the common set of resources before the lower priority scheduler is executed to schedule workloads in its inventory on the common set of resources.

Scheduling workloads on a common set of resources by multiple schedulers operating independently

Various examples are disclosed for placing virtual machine (VM) workloads in a computing environment. Ephemeral workloads can be placed onto reserved instances or reserved hosts in a cloud-based VM environment. If a request to place a guaranteed workload is received, ephemeral workloads can be evacuated to make way for the guaranteed workload.

Cost-savings using ephemeral hosts in infrastructure as a service environments based on health score

Various aspects are disclosed for unified resource management of containers and virtual machines. A podVM resource configuration for a pod virtual machine (podVM) is determined using container configurations. The podVM comprising a virtual machine (VM) that provides resource isolation for a pod based on the podVM resource configuration. A host selection for the podVM is received from a VM scheduler. The host selection identifies hardware resources for the podVM. A container scheduler is limited to bind the podVM to a node corresponding to the hardware resources of the host selection from the VM scheduler. The podVM is created in a host corresponding to the host selection. Containers are started within the podVM. The containers correspond to the container configurations.

Unified resource management for containers and virtual machines

Disclosed are various implementations of approaches for reassigning hosts between computing clusters. A computing cluster assigned to a first queue is identified. The first queue can include a first list of identifiers of computing clusters with insufficient resources for a respective workload. A host machine assigned to a second queue can then be identified. The second queue can include a second list of identifiers of host machines in an idle state. A command can then be sent to the host machine to migrate to the computing cluster. Finally, the host machine can be removed from the second queue.

Growing community of inventors

Sunnyvale, CA, United States of America

Pranshu Jain