Vmware, Inc.

Sahan Bamunavita Gamage

Rajesh Venkatasubramanian

Zhelong Pan

Pranshu Jain

Rean Griffith

Maarten Wiggers

Lei Lu

Adrian Drzewiecki

Xiaoyun Zhu

Benjamin J Corrie

Puneet Zaroo

Mark Russell Johnson

Kalyan Saladi

Varun S Lingaraju

Madhuri Yechuri

Charan K Singh

Po-An Tsai

Xiaoyuan Lu

Bing Hu

Hua Wang

Anne Holler

Minwen Ji

Wenyu Zhang

Ivaylo Radoslavov Radev

Bolt Zhang

Tomo Vladimirov Simeonov

Dragos Misca

Wang Qiong

Kalyanaraman Shankari

Selvi Kadirvel

Deepak Babarjung

Various examples are disclosed for performing cross-cluster load balancing. In some aspects, a workload is selected for cross-cluster migration. A destination cluster is identified for a migration of the workload from a source cluster to the destination cluster. A cross-cluster migration recommendation is generated to migrate the workload from the source cluster to the destination cluster.

Cross-cluster load balancer

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

Methods, apparatus, systems, and articles of manufacture are disclosed for high availability (HA) application migration in a virtualized environment. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to at least one of execute or instantiate the instructions to identify an HA slot in a virtual server rack, the HA slot to facilitate a failover of an application executing on a first virtual machine (VM) in the virtual server rack, the first VM identified as a protected VM, deploy a second VM in the HA slot, transfer data from the first VM to the second VM, and, in response to not identifying a failure of at least one of the first or second VMs during the transfer, trigger a shutdown of the first VM, and synchronize migration data associated with the virtual server rack to identify the second VM as the protected VM.

Systems, methods, and apparatus for high availability application migration in a virtualized environment

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

Examples provide a network-utilization based scheduler that considers network utilization when load balancing virtual computing instances (VCIs) in a cluster. The scheduler also periodically evaluates proposed migrations based on a current cluster snapshot including network utilization data of the hosts. Proposed migrations are removed and/or prioritized to prevent network saturation in the cluster.

Distributed resource scheduling based on network utilization

Examples provide two-tiered scheduling within a cluster. A coarse-grained analysis is performed on a candidate set of hosts to select a host for a virtual computing instance based on optimization of at least one resource. A host is selected based on the analysis results. The identified virtual computing instance is placed on the selected host. A fine-grained analysis is performed on a set of communication graphs for a plurality of virtual computing instances to generate a set of penalty scores. A set of communicating virtual computing instances are selected based on the set of penalty scores. A first virtual computing instance from a first host is relocated to a second host to minimize a distance between the first virtual computing instance and a second virtual computing instance. Relocating the first virtual computing instance reduces at least one penalty score for the set of communicating virtual computing instances.

Resource based virtual computing instance scheduling

Systems and methods for reducing network cost in a hyper-converged infrastructure are disclosed. The network cost of workload pairs can be assessed. Migration of the workloads can be considered to reduce the network cost and improve the network efficiency of the hyper-converged infrastructure.

Network costs for hyper-converged infrastructures

A management server and method for performing resource management operations in a distributed computer system utilizes interference scores for clients executing different workloads to create an interference affinity-type rule for at least some of the clients contending for a resource based on the interference scores for that resource. The interference affinity-type rule can then be used to recommend a target host computer to place a client.

Interference-aware client placement

A management server and method for performing resource management operations in a distributed computer system utilizes interference scores for clients executing different workloads, including a client to be placed in the distributed computer system, as utilization values of resources, which are assigned continuously variable weights to produce weighted resource utilization values. The weighted resource utilization values are used to generate overall selection scores for host computers of the distributed compute system, which are then used to recommend a target host computer among the host computers of the distributed computer system to place the client.

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San Jose, CA, United States of America

Sahan Bamunavita Gamage