Vmware, Inc.

Aalap Desai

Anant Agarwal

Vikram Nair

Ravi Kant Cherukupalli

Rahul Chandrasekaran

Piyush Parmar

Alaa Shaabana

Sourav Kumar

Keith Farkas

Zhelong Pan

Manoj Krishnan

Amita Vasudev Kamat

Anne Holler

Vishal I Gupta

Pradeep Padala

Pawan Saxena

Kevin Scott Christopher

Chirag Bhatt

Rajit S Kambo

Prafulla Mahindrakar

Xuwen Yu

Xiaojun Liang

Rohan Patil

Aiguo Dong

Ala Shaabana

Arvind Mohan

Jesse Andrew Mendonca

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

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.

Cloud restart for non-critical performance virtual machines

The subject matter described herein is generally directed towards detection and remediation of virtual computing instance (VCI) failure on host devices. Monitoring is performed to detect suspected failures of different guest operating systems, identify failure information, and perform remediation to provide high availability for the VCI.

Detecting a guest operating system crash on a virtual computing instance

The subject matter described herein provides virtual computing instance (VCI) component protection against networking failures in a datacenter cluster. Networking routes at the host level, VCI level, and application level are monitored for connectivity. Failures are communicated to a primary host or to a datacenter virtualization infrastructure that initiates policy-based remediation, such as moving affected VCIs to another host in the cluster that has all the necessary networking routes functional.

Protecting virtual computing instances from network failures

A management system and method for remediating poor-performing clients running in a distributed computer system uses a machine learning technique to automatically detect one or more poor-performing clients among a plurality of clients running in the distributed computer based on at least performance data and resource usage data of the clients. An action is then initiated to mitigate the effects of the poor-performing clients.

Automatic remediation of poor-performing virtual machines for scalable applications

Performance of server that is managing a group of servers, such as in a cloud, is analyzed by identifying patterns of threads executed in the server. A command is issued, repeatedly over a time period, to an operating system of the server to return a resource consumption value representative of the amount of computing resources consumed by one or more threads. The returned resource consumption values are stored and used to generate points on a graph which depicts the amount of computing resources consumed by the one or more threads over the time period and provide task breakdowns and latency information for each of the tasks.

Diagnosis of application server performance problems via thread level pattern analysis

Embodiments perform adaptive throttling of tasks into a virtual datacenter having dynamically changing resources. Tasks are processed concurrently in batches. The rate of change in throughput at different batch sizes is calculated. With each iteration, the batch size is increased or decreased based on the rate of change to achieve a maximum throughput for given resources and load on the virtual datacenter.

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Palo Alto, CA, United States of America

Aalap Desai