Vmware, Inc.

Hewlett-packard Development Company, L.p.

Compaq Computer Technologies Group, L.p.

Keith Farkas

Joanne Ren

Elisha Ziskind

Parthasarathy Ranganathan

Manoj Krishnan

Guoqiang Shu

Christos Karamanolis

Sven Graupner

Igor Tarashansky

Cullen Edwin Bash

Chandrakant D Patel

Norman Paul Jouppi

Martin Arlitt

Subramoniam N Iyer

Marc Sevigny

Ganesha Shanmuganathan

Gopalakrishnan Janakiraman

Jerome Alexander Rolia

Akhil Sahai

Vijay Machiraju

James Christopher Pruyne

Mike Zucca

Sridhar Rajagopal

Michael Nelson

Anne Holler

Minwen Ji

Maarten Wiggers

Martin Marinov

Martin Valkanov

Rostislav Vavrick

Marie-Jo Fremont

Baruch Oxman

Smriti Desai

Jeffrey Clifford Mogul

Aashish Parikh

Duncan Epping

Aalap Desai

Ron Passerini

Jeevitha Kirubanandam

David W Barry

Biliana Petrova

Konstantin Ivanov

Anil Kapur

Carl Alan Waldspurger

Umeshwar Dayal

Rajesh Venkatasubramanian

Ming C Hao

Abdlmonem H Beitelmal

Erik C Cota-Robles

Larry D Seiler

Marcos Kawazoe Aguilera

Dirk Manfred Beyer

Joel James McCormack

Mayank Rawat

Alex Xin Zhang

Janet Lynn Wiener

Derek William Beard

Parth Shah

Praveen Vegulla

Christopher C Wanner

Fereydoon Safai

Robert Stephen McNamara

Robert Nelson Mayo

Kevin Scott Christopher

Ivaylo Petkov Strandzhev

Brian J Watson

Piyush Parmar

Justin Moore

Michael Zucca

Eddie Ma

Konstantin Ivanov Spirov

Ianislav Trendafilov

Lawrence Sivert Brakmo

Amita Vasudev Kamat

Aniket Avinash Sakhardande

Sandro Rafaeli

Amit Singh Rathore

Eduardo S Pinheiro

Stoimen Gerenski

Ajay Verghese

Arthur Klauser

Jesse Andrew Mendonca

Avinash Goud

Rohan Patil

Robert Stets

Mark B Misroch

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

The disclosure provides an approach for the dynamic configuration of virtualized objects. A virtual object may be associated with a desired state defining a first plurality of resources for allocating to the virtual object. The first plurality of resources correspond to one or more resource types. Techniques include determining that each of a plurality of hosts does not have sufficient available resources to allocate the first plurality of resources to the virtual object according to the desired state. Techniques include selecting, a first host of the plurality of hosts to run the virtual object. Techniques include allocating a second plurality of resources to the virtual object from the first host, wherein the second plurality of resources is less than the first plurality of resources, and running the virtual object in the first host.

Dynamic configuration of virtual objects

Exemplary methods, apparatuses, and systems include a target site management server transmitting, to a source site management server, a plurality of protection service plans available for replication of data from the source site to the target site. The transmission of the protection service plans includes a description of one or more service level characteristics provided by each protection service plan and excludes a listing of physical and virtual resources within the target site that are to provide the service level characteristics. The target site management server receives selection of one of the protection service plans and determines the physical resources within the target site to provide the advertised service level characteristics for the data replication. The target site management server further transmits configuration details to one or more of the determined physical resources to implement the replication infrastructure within the target site according to the selected protection service plan.

Policy-based selection and configuration of target site resources for data replication

Exemplary methods, apparatuses, and systems include a hypervisor receiving an error message from an agent within a first virtual machine run by the hypervisor. In response to the error message, the hypervisor determines and initiates a corrective action for the hypervisor to take in response to the error message. An exemplary corrective action includes initiating a reset of the first virtual machine or a reset of a second virtual machine.

Hypervisor remedial action for a virtual machine in response to an error message from the virtual machine

Disclosed are aspects of proactive high availability that proactively identify and predict hardware failure scenarios and migrate virtual resources to healthy hardware resources. In some aspects, a mapping that maps virtual resources to hardware resources. A plurality of hardware events are identified in association with a hardware resource. A hardware failure scenario is predicted based on a health score of a first hardware resource. A health score is determined based on the hardware events, and a fault model that indicates a level of severity of the hardware events. A particular virtual resource is migrated from the hardware resource to another hardware that has a greater health score.

Proactive high availability in a virtualized computer system

Techniques are disclosed for maintaining high availability (HA) for virtual machines (VMs) running on host systems of a host cluster, where each host system executes a HA module in a plurality of HA modules and a storage module in a plurality of storage modules, where the host cluster aggregates, via the plurality of storage modules, locally-attached storage resources of the host systems to provide an object store, where persistent data for the VMs is stored as per-VM storage objects across the locally-attached storage resources comprising the object store, and where a failure causes the plurality of storage modules to observe a network partition in the host cluster that the plurality of HA modules do not. In one embodiment, a host system in the host cluster executing a first HA module invokes an API exposed by the plurality of storage modules for persisting metadata for a VM to the object store. If the API is not processed successfully, the host system: (1) identifies a subset of second HA modules in the plurality of HA modules; (2) issues an accessibility query for the VM to the subset of second HA modules in parallel, the accessibility query being configured to determine whether the VM is accessible to the respective host systems of the subset of second HA modules; and (3) if at least one second HA module in the subset indicates that the VM is accessible to its respective host system, transmits a command to the at least one second HA module to invoke the API on its respective host system.

Maintaining high availability during network partitions for virtual machines stored on distributed object-based storage

A system for monitoring a virtual machine executed on a host. The system includes a processor that receives an indication that a failure caused a storage device to be inaccessible to the virtual machine, the inaccessible storage device impacting an ability of the virtual machine to provide service, and applies a remedy to restore access to the storage device based on a type of the failure.

Protecting virtual machines against storage connectivity failures

A system and method for semi-automatic workload domain deployment in a computing environment uses a user host selection of at least one host computer for a workload domain to automatically recommend candidate host computers for the workload domain from available host computers using relative and absolute selection criteria. The relative selection criteria include criteria that are based on properties of any manually selected host computers, while the absolute selection criteria include criteria that are not based on properties of any manually selected host computers. Another user selection of at least one of the candidate host computers can then be made for the workload domain. The workload domain is deployed using the user host selections of the at least one hot computer and the at least one of the candidate host computers.

Systems and methods for semi-automatic workload domain deployments

The present disclosure is related to systems and methods for protecting virtual computing instances. An example system can include a first virtual computing instance (VCI) deployed on a hypervisor and provisioned with a pool of physical computing resources. The hypervisor and the first VCI can operate according to a first configuration profile. The system can include a fault domain manager (FDM) running on a second VCI that is deployed on the hypervisor and provisioned by the pool of physical computing resources. The FDM can be configured to provide high availability support for the first VCI, and the FDM can operate according to a second configuration profile. The system can further include a hypervisor manager running on the second VCI. The hypervisor manager can be configured to facilitate interaction between the FDM and the hypervisor by translating between the first configuration profile and the second configuration profile.

Protecting virtual computing instances

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

A method of managing virtual resources executing on a hardware platform that employs sensors to monitor the health of hardware resources of the hardware platform, includes filtering sensor data from the hardware platform and combining the sensor data with a fault model for the hardware platform to generate a health score, receiving an inventory that maps the virtual resources to the hardware resources of the hardware platform, receiving resource usage data describing use of the hardware resources of the hardware platform by the virtual resources, and generating resource utilization metrics from the resource usage data. The method includes receiving policy data specifying rules applicable to the inventory, determining a set of recommendations for changes to the inventory based on the health score, the resource usage data, and the policy data, and executing at least one recommendation to implement the changes to the inventory.

Techniques are disclosed for maintaining high availability (HA) for virtual machines (VMs) running on host systems of a host cluster, where each host system executes a HA module in a plurality of HA modules and a storage module in a plurality of storage modules, where the host cluster aggregates, via the plurality of storage modules, locally-attached storage resources of the host systems to provide logical data store, and where persistent data for the VMs is stored across the locally-attached storage resources comprising the logical data store.

The present disclosure is related to systems and methods for scheduling and managing series of snapshots. An example method can include estimating a transfer time to transfer a first snapshot of a virtual computing instance (VCI) to a first snapshot series, and estimating a transfer time to transfer a second snapshot of the VCI to a second snapshot series. The method can further include determining a first schedule time to start a transfer of a first series of snapshots and determining a second schedule time to start a transfer of a second series of snapshots, wherein the first schedule time and the second schedule time are based at least in part on a respective recovery point objective (RPO). In some embodiments, the method can further include scheduling a point in time to record a next snapshot based at least in part on the shorter schedule time of the first schedule time and the second schedule time.

Scheduling and managing series of snapshots

Exemplary methods, apparatuses, and systems include a source site management server receiving, from first and second potential target site management servers, characteristics of the first and second potential target sites that affect replication of data from the source site to each potential target site. The source site management server receives selection of one or more criteria to be met in the selection of a target site for replication of data. The source site management server determines which potential target site is a better match for the received criteria based upon a comparison of the received criteria with the received characteristics of the target sites. The source site management server configures, automatically in response to the determination, the selected site as the target site for replication of data within the source site.

Exemplary methods, apparatuses, and systems determine a list of virtual machines to be subject to a corrective action. When one or more of the listed virtual machines have dependencies upon other virtual machines, network connections, or storage devices, the determination of the list includes determining that the dependencies of the one or more virtual machines have been met. An attempt to restart or take another corrective action for the first virtual machine within the list is made. A second virtual machine that is currently deployed and running or powered off or paused in response to the corrective action for the first virtual machine is determined to be dependent upon the first virtual machine. In response to the second virtual machine's dependencies having been met by the attempt to restart or take corrective action for the first virtual machine, the second virtual machine is added to the list of virtual machines.

Interdependent virtual machine management

Growing community of inventors

San Carlos, CA, United States of America

Keith Farkas