International Business Machines Corporation

Nutanix, Inc.

Lenovo (singapore) Pte. Ltd.

Kognitos, Inc.

Binny Sher Gill

Michael T Benhase

Lokesh M Gupta

Dharmendra S Modha

Miao Cui

Brent Cameron Beardsley

Wendy Ann Belluomini

Sonny Earl Williams

James Lee Hafner

Matthew J Kalos

Parthasarathy Ramachandran

Thomas Charles Jarvis

Brian Byrne

Nitin Parab

Edi Shmueli

Karan Gupta

Joseph S Hyde, Ii

Karthik Chandrasekaran

Ranjan Parthasarathy

Tabrez Parvez Memon

Gail Andrea Spear

Steven Robert Hetzler

Haim Helman

Mohit Aron

Bruce McNutt

Daniel Felix Smith

Michael Anthony Ko

Rajiv Mirani

Mohan Maturi

Jan Olderdissen

Akshay Deodhar

Leo Shyh-Wei Luan

Venu Nayar

Jaya Singhvi

Vidhi Taneja

Naveen Kumar

Ying Chen

Nedlaya Yazzie Francisco

Suguang Li

Manosiz Bhattacharyya

Madhukar R Korupolu

Venkata Vamsi Krishna Kothuri

Owen Theodore Anderson

Assaf Nitzan

Varun Kumar Arora

Anupama Ashok Lolage

Geoffrey Albert Riegel

Aaron Dean Brown

Shlomo Shlomi Vaknin

Carl David Sutton

Manuel Vasconcellos Pereira, Iii

Blinston Savio Fernandes

Pin Zhou

Himanshu Shukla

Abhinay Nagpal

Krishnakumar Rao Surugucchi

Vinod Gupta

Gregory A Smith

Lan Huang

Steven Robert Lowe

Jianjun Wen

Jeremy Michael Pinson

Dmitri Bronnikov

Leo S Luan

Eyal Lotem

Xifeng Yan

Suresh Sivaprakasam

Andre Leibovici

Cong Liu

Jon Carlo Gueco

Shuo Yang

Eyal Lotern

Leo Shyh-Wei Lei

Luis Angel Daniel Bathen

Veera A Deenadhayalan

Gregory Andrew Smith

Methods, systems and computer program products for bringing-up a computing cluster on a public cloud infrastructure with techniques utilizing expressed intents (high level descriptions of desired configuration) and asynchronously receiving configuration status messages from the public cloud infrastructure. The method includes a cloud management computing system transmitting to the public cloud infrastructure a first expressed intent for bringing-up a computing cluster. The cloud management computing system asynchronously receiving periodic status messages comprising cluster status data from the public cloud infrastructure reflecting a current configuration state of the computing cluster. The system determines, based on the cluster status data, whether the first expressed intent for the computing cluster has been achieved.

Computing cluster bring-up on public cloud infrastructure using expressed intents

Systems for high-performance computing. A storage control architecture is implemented by a plurality of nodes, where a node comprises combinations of executable containers that execute in cooperation with virtual machines running above a hypervisor. The containers run in a virtual machine above a hypervisor, and/or can be integrated directly into the operating system of a host node. Sensitive information such as credit card information may be isolated from the containers in a separate virtual machine that is configured to be threat resistant, and which can be accessed through a threat resistant interface module. One of the virtual machines of the node may be a node-specific control virtual machine that is configured to operate as a dedicated storage controller for a node. One of the virtual machines of the node may be a node-specific container service machine that is configured to provide storage-related and other support to a hosted executable container.

Architecture for managing I/O and storage for a virtualization environment using executable containers and virtual machines

Methods, systems, and computer program products for flexible virtualization system deployment into different cloud computing environments. A set of floating licenses to virtualization system software components is established. The set of floating licenses are configured to permit usage of the virtualization system software components on different cloud computing infrastructures. Workload parameters of a workload to be deployed to one of the different cloud computing infrastructures is considered with respect to cloud attributes corresponding to the different cloud computing infrastructures. One or more candidate target cloud computing infrastructures are selected based upon a comparison between workload attributes of a computing workload and cloud attributes of the candidate target cloud computing infrastructures. Virtualization system software components are deployed into the selected target cloud computing infrastructures. Licenses to the virtualization system software components can float between any combination of different cloud computing infrastructures, including floating the licenses between private clouds and public clouds.

Multi-cloud licensed software deployment

Upon receiving a request to hibernate a hypervisor of a virtualization system running on a first computer, acts are carried out to capture a state of the hypervisor, where the state of the hypervisor comprises hypervisor logical resource parameters and an execution state of the hypervisor. After hibernating the hypervisor by quiescing the hypervisor and storing the state of the hypervisor into a data structure, the data structure is moved to a different location. At a later moment in time, the data structure is loaded onto a second computing machine and restored. The restore operation restores the hypervisor and all of its state, including all of the virtual machines of the hypervisor as well as all of the virtual disks and other virtual devices of the virtual machines. Differences between the first computing machine and the second computing machine are reconciled before execution of the hypervisor on the second machine.

Hypervisor hibernation

Methods, systems and computer program products for deploying a virtualization system onto cloud computing infrastructure. Virtual machines of a virtualization system are deployed onto computing nodes that are interconnected via a cloud provider's networking infrastructure. When migrating a virtual machine from a source computing node to a target computing node that is also interconnected to the cloud provider's networking infrastructure, the addressing of the to-be-migrated virtual machine changes. Dynamically-updated media access control translation tables are maintained at the computing nodes. The media access control translation tables are populated with cloud provider media access control addresses received from the cloud provider. A virtual switch at each computing node modifies incoming and outgoing packets of a to-be-migrated virtual machine based on contents of that node's media access control translation table. On an ongoing basis, addressing information for migrated virtual machines is received at the computing nodes by operation of address resolution protocols.

Virtual machine migration in cloud infrastructure networks

Examples of systems are described herein which may dynamically allocate compute resources to recovery clusters. Accordingly, a recovery site may utilize fewer compute resources in maintaining recovery clusters for multiple associate clusters, while ensuring that, during use, compute resources are allocated to a particular cluster. This may reduce and/or avoid vulnerabilities arising from a use of shared resources in a virtualized and/or cloud environment.

Dynamic allocation of compute resources at a recovery site

Architecture for managing i/o and storage for a virtualization environment using executable containers and virtual machines

Disclosed is an approach to implement new behavior using natural language, and to debug and examine what happened in the past via a natural language interface as well. Some approaches use a combination of natural language understanding techniques, knowledge representation techniques, advanced compiler techniques and user interaction techniques, to solve for natural language programming and debugging of computers.

Method, system, and computer program product for natural language programming of a new function or skill

A method for time series analysis of time-oriented usage data pertaining to computing resources of a computing system. A method embodiment commences upon collecting time series datasets, individual ones of the time series datasets comprising time-oriented usage data of a respective individual computing resource. A plurality of prediction models are trained using portions of time-oriented data. The trained models are evaluated to determine quantitative measures pertaining to predictive accuracy. One of the trained models is selected and then applied over another time series dataset of the individual resource to generate a plurality of individual resource usage predictions. The individual resource usage predictions are used to calculate seasonally-adjusted resource usage demand amounts over a future time period. The resource usage demand amounts are compared to availability of the resource to form a runway that refers to a future time period when the resource is predicted to be demanded to its capacity.

Method for forecasting distributed resource utilization in a virtualization environment

Growing community of inventors

San Jose, CA, United States of America

Binny Sher Gill