Vmware, Inc.

Vikram Nair

Aalap Desai

Anant Agarwal

Ravi Kant Cherukupalli

Rahul Chandrasekaran

Alaa Shaabana

Sourav Kumar

Zhelong Pan

Piyush Parmar

Pawan Saxena

Ala Shaabana

Arvind Mohan

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

A system and method for automatically generating recurrent neural networks for log anomaly detection uses a controller recurrent neural network that generates an output set of hyperparameters when an input set of controller parameters is applied to the controller recurrent neural network. The output set of hyperparameters is applied to a target recurrent neural network to produce a child recurrent neural network with an architecture that is defined by the output set of hyperparameters. The child recurrent neural network is then trained, and a log classification accuracy of the child recurrent neural network is computed. Using the log classification accuracy, at least one of the controller parameters used to generate the child recurrent neural network is adjusted to produce a different input set of controller parameters to be applied to the controller recurrent neural network so that a different child recurrent neural network for log anomaly detection can be generated.

System and method for automatically generating neural networks for anomaly detection in log data from distributed systems

A method of restarting a virtual machine (VM) running in a cluster in a first data center, in a second data center, includes: transmitting images of VMs, including a first VM, running in the cluster of hosts at a first point in time to the second data center for replication in the second data center; generating difference data representing a difference in an image of the first VM at a second point in time and the image of the first VM at the first point in time; transmitting the difference data to the second data center; setting the first VM to be inactive in the first data center; and communicating with a control plane in the second data center to set as active, and power on, a VM in the second data center using the replicated image of the first VM updated with the difference data.

Cloud restart for VM failover and capacity management

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.

Cluster capacity management for hyper converged infrastructure updates

Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.

Machine learning system for workload failover in a converged infrastructure

A method of restarting a virtual machine running in a cluster of hosts in a first data center, in a second data center, wherein each virtual machine is assigned a priority level, includes: transmitting virtual machines images running in the cluster at a first time to the second data center; selecting virtual machines to be restarted in the second data center according to priority levels assigned; and for each selected virtual machine, (a) generating difference data in an image of the selected virtual machine at a second time and at the first time, (b) transmitting the difference data to the second data center, (c) setting the virtual machine inactive in the first data center, and (d) communicating with the second data center to set as active; and power on, a virtual machine in the second data center using the image of the virtual machine transmitted to the second data center.

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Palo Alto, CA, United States of America

Vikram Nair