Microsoft Technology Licensing, LLC

Bausch & Lomb Incorporated

Webtv Networks, Inc.

Microsoft Corporation

Robert M Fries

Srivatsan Parthasarathy

Galen Clyde Hunt

John M Parchem

Dharshan Rangegowda

Nikhil Vijay Chandhok

Michael L Michael

Vinay Badami

Eric Joseph Winner

Robert Victor Welland

Bassam Tabbara

Ashvinkumar J Sanghvi

Rene Antonio Vega

Aamer Hydrie

Geoffrey Hugh Outhred

Anders Bertil Vinberg

Rob M Mensching

Michael E Pietraszak

William L Scheidel

Brian Michael Wahlert

Jeffrey A Woolsey

Brian Thomas Berkowitz

Karandeep Singh Anand

Catharine Van Ingen

Kevin Grealish

Seetharaman Harikrishnan

James P Finnigan

Alan C Shi

Robert S T Gibson

Yezdi Z Lashkari

Edwin R Lassettre

Mike Jazayeri

Jonathan A Wray

Vijay Sen

Shiraz M Somji

Eric J Wolz

Vijay Mital

Moe Khosravy

Suraj T Poozhiyil

Michael K Fleming

Michael Grier

Abid Ali

Aravind Ramarathinam

Max Uritsky

Bruce Louis Lieberman

Sergey Royt

Natalya V Varava

Venkatasubrahmanyan Raman

Nigel Stuart Keam

Ran Kalach

Raymond W McCollum

Vij Rajarajan

Giedrius Zizys

David C James

Efstathios Papaefstathiou

Stuart Hillary Schaefer

Robert Pike

Alexander Evgenievich Nosov

James Taylor Perkins

Igor Sedukhin

Vitaly Voloshin

Michael Joseph Healy

Bruce Wayne Copeland

Vivek Sahasranaman

Shobana Balakrishnan

John H Wilson

Gilad Golan

Mazhar Naveed Mohammed

Jonathan C Hardwick

Daniel Eshner

Laurence J Cull

Chia-Chi Teng

Rajagopalan Badri Narayanan

Sunil Pai

Manoj K Valiyaparambil

Anand Lakshminarayanan

Amit Singla

Kushagra Vaid

David E Gonzalez

Nelson Sampaio Araujo, Jr

Kevin D J Grealish

Zhengkai Kenneth Pan

Purushottam M Kulkarni

Paul Brandon Luber

Asaf Kariv

Venkatasubrahmanyam Raman

Alan E Shield

Michael Oliver Neary

Heidi H Breslauer

Przemek Pardyak

Deployment and servicing tasks associated with multi-tier, distributed applications, application environments and data centers are automated so that a person does not have to manually perform these tasks. All of the information describing and defining the distributed service is modeled and stored in a re-useable service template that can be used to drive an automated system to programmatically deploy and manage the service over time. Deployment and servicing of a distributed application can be automated using re-useable models that capture hardware and workload definitions. The re-useable models in the form of service templates enable delta-based servicing of the application. The service can be deployed to one or more physical machines, one or more virtual machines or to a combination thereof. A default deployment plan can be customized with instance-specific customizations of service parameters.

Automated deployment and servicing of distributed applications

A data marketplace infrastructure provides a crowd sourcing solution to development, discovery and publication of decision applications. Applications can be submitted from a user to a data warehouse in association with a data feed. One or more discovery properties are determined with regard to each application. The applications are made available to other client systems in association with the data feed. A relevant data feed and a relevant application can be identified based on satisfaction of a discovery request by the one or more determined discovery properties of the application. The application can be selected and downloaded to the user for evaluation and customization. The customized application can then be submitted to the data warehouse for publication with the other applications associated with the data feed.

Business application publication

Model-based virtual system provisioning includes accessing a model of a workload to be installed on a virtual machine of a system as well as a model of the system. A workload refers to some computing that is to be performed, and includes an application to be executed to perform the computing, and optionally includes the operating system on which the application is to be installed. The workload model identifies a source of the application and operating system of the workload, as well as constraints of the workload, such as resources and/or other capabilities that the virtual machine(s) on which the workload is to be installed must have. An installation specification for the application is also generated, the installation specification being derived at least in part from the model of the workload and the model of the virtual system.

Model-based virtual system provisioning

Disclosed are techniques for using known update technologies to automatically identify the updates installed on a physical machine prior to facsimile creation, to locate the update packages from an update server, and to download them to the machine performing the facsimile creation. The use of update technologies enables precise identification of the updates that affect the defined set of files requiring replacement, and thus minimizes the number of updates that need to be downloaded. In addition, the desired set of replacement files are extracted and cached to allow for their reuse during subsequent facsimile creations using either the same or different physical or virtual machine as the source. Downloading the minimal set of updates and caching them for reuse eliminates the redundant downloads of updates and results in a shorter time for facsimile creation.

Management of software and operating system updates required for the process of creating a virtual machine facsimile of an existing physical or virtual machine

Network utilization of an arbitrary application is controlled by tracking network usage statistics of virtual machines (VMs), including at least VMs hosting parts of the application. For network utilization control, VMs serve as network proxies for elements of the application. A specification for a network requirement of the application is evaluated against the network usage statistics. When a network capacity requirement to/from/through an element of the application is not satisfied, one or more VMs are adapted to satisfy the requirement. For example, a VM may be migrated from a host or network location that has excess network bandwidth. Or, for example, network bandwidth availability for an under-requirement VM may be increased and bandwidth availability for a VM at an appropriate host or network location may be decreased. Thus, application-level communication path requirements may be satisfied using VM adaptations.

Controlling network utilization

Tools and techniques for migrating applications to compute clouds are described herein. A tool may be used to migrate any arbitrary application to a specific implementation of a compute cloud. The tool may use a library of migration rules, apply the rules to a selected application, and in the process generate migration output. The migration output may be advisory information, revised code, patches, or the like. There may be different sets of rules for different cloud compute platforms, allowing the application to be migrated to different clouds. The rules may describe a wide range of application features and corresponding corrective actions for migrating the application. Rules may specify semantic behavior of the application, code or calls, storage, database instances, interactions with databases, operating systems hosting the application, and others.

Virtual machine migration tool

A logical communication path is provided between a target virtual machine (VM) and a host or application communicating with the VM. The target VM runs on a hypervisor host that has a hypervisor and a proxy agent. The hypervisor manages execution of the VM. A mapping is maintained indicating which VMs execute on which hosts. When the host or application is to send a message or packet to the target VM, the mapping is consulted and the hypervisor host hosting the target VM is identified. The message or packet, which may identify the target VM, is transmitted to the hypervisor host. A proxy agent at the hypervisor host selects a communication channel between the hypervisor and the target VM. The hypervisor then passes the message or packet through the selected channel to the target VM.

Host enabled management channel

An optimized placement of virtual machines may be determined by optimizing an energy cost for a group of virtual machines in various configurations. For various hardware platforms, an energy cost per performance value may be determined. Based on the performance usage of a group of virtual machines, a total power cost may be determined and used for optimization. In some implementations, an optimized placement may include operating a group of virtual machines in a manner that does not exceed a total energy cost for a period of time.

Virtual machine placement based on power calculations

Techniques are described to allow substation of blocks into a virtual machine image of an executing virtual machine. A patch may be applied to a first virtual machine image. One or more blocks modified by the patch are identified and copied to a host having a virtual machine executing from a virtual machine image that is based on the first virtual machine image (prior to the patch). While the virtual machine is executing, the substitution blocks are applied (supersede) corresponding original blocks in the virtual machine image. A guest operating system in the virtual machine may either begin using an executable file in a substitution block. In addition, the guest operating system may be forced to flush from memory and cache executable code replaced by the substitution block.

Virtual machine block substitution

Described are techniques for capturing and analyzing snapshots of virtual machines. One or more computers may automatically obtain snapshots of virtual machines as they are executing to form a pool of virtual machine snapshots. The virtual machine snapshots are then read to obtain a set of features properties of the virtual machine snapshots, including information about a running guest operating system, software installed on the virtual machine, metadata about the virtual machine itself, and others. The features or properties are analyzed, in one embodiment using a machine learning algorithm, to automatically compute and store information about the virtual machines.

Virtual machine snapshotting and analysis

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Kirkland, WA, United States of America

Robert M Fries