Vmware, Inc.

Piyush Parmar

Aalap Desai

Rahul Chandrasekaran

Anant Agarwal

Vikram Nair

Amita Vasudev Kamat

Keith Farkas

Gabriel Tarasuk-Levin

Pawan Saxena

Ravi Kant Cherukupalli

Umesha Margi

Dhaval Shah

System and computer-implemented method for generating multi-cloud recommendations for workloads uses costs and performance metrics of appropriate instance types in specific public clouds for target workloads to produce recommendation results. The appropriate instance types in the specific public clouds are determined based on instance capabilities and the workload type of the target workloads. In addition, a recommended cloud resource offering is determined for the target workloads, which is sent as a notification with the recommendation results of the appropriate instance types in the specific public clouds.

Multi-cloud recommendation engine for customer workloads

System and computer-implemented method for autoscaling clusters of host computers in a cloud-based computing environment uses an aggressive scale-in resource utilization threshold that is greater than a corresponding standard scale-in resource utilization threshold to search for any target clusters of host computers in response to a scale-out recommendation for a cluster of host computers to select a candidate cluster of host computers when the number of available reserved resource instance for the cloud-based computing environment is below a predefined value. A scale-in operation is executed on the candidate cluster of host computers to remove an existing resource instance from the candidate cluster of host computers. A scale-out operation is executed on the cluster of host computers using an available resource instance for the cloud-based computing environment.

Efficient use of reserved resource instances in clusters of host computers in a cloud-based computing environment

System and computer-implemented method for managing multi-availability zone (AZ) clusters of host computers in a cloud computing environment automatically detects a degraded state of a first AZ in the cloud computing environment based on host failure events for host computers in a first cluster section of a multi-AZ cluster of host computers located in the first AZ and a recovered state of the first AZ based a successful scale-in operation of another multi-AZ cluster located partially in the first AZ. In response to the detection of the degraded state of the first AZ, a second cluster section of the multi-AZ cluster of host computers located in a second AZ is scaled out. In response to the detection of the recovered state of the first AZ, the second cluster section of the multi-AZ cluster of host computers located in the second AZ is scaled in.

Degraded availability zone remediation for multi-availability zone clusters of host computers

A system and method for scaling resources of a secondary network for disaster recovery uses a disaster recovery notification from a primary resource manager of a primary network to a secondary resource manager of the secondary network to generate a scale-up recommendation for additional resources to the secondary network. The additional resources are based on latest resource demands of workloads on the primary network included in the disaster recovery notification. A scale-up operation for the additional resources is then executed based on the scale-up recommendation from the secondary resource manager to operate the secondary network with the additional resources to run the workloads on the secondary network.

System and method for scaling resources of a secondary network for disaster recovery

Disclosed are various implementations of cluster capacity management for infrastructure updates. In some examples, cluster hosts for a cluster can be scheduled for an update. A component of a datacenter level resource scheduler can analyze cluster specific resource usage data to identify a cluster scaling decision for the cluster. The datacenter level resource scheduler transmits an indication that the resource scheduler is successfully invoked. Cluster hosts can then be updated.

Growing community of inventors

Bengaluru, India

Piyush Parmar