Vmware, Inc.

Numecent Holdings, Inc.

Wmware, Inc.

Endeavors Technologies, Inc.

Anne Holler

Ganesha Shanmuganathan

Irfan Ahmad

Mustafa Uysal

Pradeep Padala

Lacky V Shah

Minwen Ji

Chirag Bhatt

Sameer Panwar

Manuel Enrique Benitez

Daniel Arai

Keith Farkas

Xiaoyun Zhu

Kinshuk Govil

Rean Griffith

Shankari Kalyanaraman

Limin Wang

Anil Kv Kumar

Kumar Gaurav

Aashish Parikh

Vijayaraghavan Soundararajan

Manoj Krishnan

Elisha Ziskind

Jayanth Gummaraju

Maarten Wiggers

Timothy P Mann

Chengwei Wang

Adarsh Jagadeeshwaran

Umit Rencuzogullari

Vaibhav Kohli

Shruti Sharma

Rajdeep Dua

Alok Kumar Gupta

Anil Kapur

Kalyanaraman Shankari

Tahir Mobashir

Canturk Isci

Pinar Yanardag Delul

Erik C Cota-Robles

Michael Nelson

Gabriel Tarasuk-Levin

Ajay Gulati

Vishal I Gupta

Servesh Pratap Singh

Benjamin J Corrie

Duncan Epping

Sahan Bamunavita Gamage

Aalap Desai

Frank Denneman

Vineet Kumar Sinha

Richard McDougall

Prasenjit Sarkar

Kiran Singh

George Gregory Hicken

Arun Narayanaswamy

Lei Lu

Shyam Sundar Rao Mankala

Rohit Bhoj

Ifran Ahmad

Yann Hodique

Jai Jaswani

Samit Sawant

Andrew Mitchell

Yong Fu

Selvi Kadirvel

Amit Patel

A system and method for performing an operational metric analysis for a virtual appliance uses application operational data from multiple instances of the virtual appliance. The application operational data is then used to generate an operational metric prediction for the virtual appliance.

Crowd-sourced operational metric analysis of virtual appliances

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

A system and method for efficiently replacing clients running a large scale application uses a pool of clients, which includes a group of active clients running the large scale application and at least one standby client. When a particular active client is identified to be replaced, one of the at least one standby client is selected as a replacement client for the particular active client. The particular active client is replaced with the selected replacement client with respect to the running of the large scale application such that the particular client is removed from the group of active clients and the selected replacement client is added to the group of active clients.

Efficient replacement of clients running large scale applications

Embodiments associate software applications with computing resource containers based on a placement rule and a selected failure correlation. A placement rule indicates that a first software application is to be co-located with a second software application during execution of the first and second software applications. The placement rule also indicates that the first software application is to be separated from the second software application during execution of the first and second software applications. Failure correlations are determined for a plurality of computing resources associated with the first software application. A computing resource with a lowest failure correlation is selected from the plurality of computing resources, and the second software application is associated with the selected computing resource despite the association violating the placement rule.

Software application placement using computing resource containers

In an example, a method of placing a virtual computing instance among a plurality of datacenters includes comparing constraints specified for the virtual computing instance against resource metrics obtained from the plurality of datacenters, and a budget specified for the virtual computing instance against admission prices for a plurality of tiers of the plurality of datacenters, to generate a candidate set of datacenters. The method further includes scoring the candidate set of datacenters. The method further includes deploying the virtual computing instance to a target datacenter selected from the candidate set of datacenters that satisfies a threshold score.

Hybrid cloud resource scheduling

An automatic scaling system and method for reducing state space in reinforced learning for automatic scaling of a multi-tier application uses a state decision tree that is updated with new states of the multi-tier application. When a new state of the multi-tier application is received, the new state is placed in an existing node of the state decision tree only if a first attribute of the new state is same as a first attribute of any state contained in the existing node and a second attribute of the new state is sufficiently similar to a second attribute of each existing state contained in the existing node based on a similarity measurement of the second attribute of each state contained in the existing node with the second attribute of the new state.

System and method for reducing state space in reinforced learning by using decision tree classification

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.

A method for scheduling computing resources without container migration includes determining a resource availability for one or more hosts, a resource allocation for one or more virtual machines (VMs), and a resource usage for one or more containers. The method further includes calculating a target resource configuration for one or more VMs, wherein calculating a target resource configuration comprises determining an upper limit of resource demand on a VM from one or more containers allocated on the VM, based at least in part on the resource usage. The method also includes removing or adding resources to each of the one or more VMs for which a target resource configuration was calculated to achieve the target resource configuration for each VM. The method further includes allocating the one or more VMs on the one or more hosts based on the resource availability of the one or more hosts.

Virtual resource scheduling for containers without migration

System and method for performing resource allocation for a host computer cluster use resource allocation weight scores for resource nodes in a cluster resource allocation hierarchy of the host computer cluster based on the number of powered-on clients in the resource nodes.

System and method for performing resource allocation for a host computer cluster

A technique for managing distributed computing resources in a virtual computing environment is disclosed. In an embodiment, a method includes receiving a recommended change to a virtual architecture of a virtual computing environment; determining an impact on current workload in the virtual computing environment if the recommended change is performed; determining an impact on future workload in the virtual computing environment if the recommended change is performed; calculating a combined impact on current and future workload; determining if the combined impact is above or below a threshold; if the combined impact on current and future workload is below the threshold, do not perform the recommended change; and if the combined impact on current and future workload is above the threshold, perform the recommended change.

System and method for managing a virtualized computing environment

A method of reducing power consumption of a server cluster of host systems with virtual machines executing on the host systems is provided. The method includes recommending host system power-on when there is a host system whose utilization is above a target utilization, and recommending host system power-off when there is a host system whose utilization is below the target utilization. Recommending host system power-on includes calculating impact of powering on a standby host system with respect to reducing the number of highly-utilized host systems in the server cluster. Recommending host system power-off includes calculating impact of powering off a host system with respect to decreasing the number of less-utilized host systems in the server cluster.

Reducing power consumption in a server cluster

A resource management system and method for automatically creating affinity-type rules for resource management in a distributed computer system uses association inference information for at least one resource to determine resource association between resources, which is used to automatically create an affinity-type rule for the resources. The affinity-type rule is considered when executing a resource management operation.

Automatic creation of affinity-type rules for resources in distributed computer systems

A resource management system and method for performing resource capacity management in a cluster of host computers uses a snapshot of the cluster with one or more ghost host computers added to the cluster to execute a power management analysis. A ghost host computer is a fictitious construct based on a physical host computer. The results of the power management analysis may then be used as a cluster capacity recommendation to increase resource capacity of the cluster of host computers.

Resource capacity management in a cluster of host computers using power management analysis

A method for scheduling computing resources with container migration includes determining a resource availability for one or more hosts, a resource allocation for one or more virtual machines (VMs), and a resource usage for one or more containers. The method includes identifying the hosts on which VMs and containers can be consolidated based on resource availability. The method also includes calculating a target resource configuration for one or more VMs. The method further includes removing or adding resources to the VMs for which a target resource configuration was calculated to achieve the target resource configuration. The method further includes allocating the one or more VMs on the one or more hosts based on the resource availability of the one or more hosts, and allocating the one or more containers on the one or more VMs based on the resource configuration of each VM and the resource usage of each container.

Virtual resource scheduling for containers with migration

Growing community of inventors

Los Altos, CA, United States of America

Anne Holler