Microsoft Technology Licensing, LLC

Other

Microsoft Corporation

James Ernest Johnson

Ajay Mani

Mark Eugene Russinovich

Thomas Moscibroda

Yang Chen

Michael Ray Clark

John David Doty

Max A Feingold

Luis Irun-Briz

Kapil Gupta

Jonathan M Cargille

Ying Yan

William J Carley

Dana D Groff

Jurgen Aubrey Willis

Jiaxing Zhang

Haoran Wang

Nicholas Alexander Allen

Christopher G Kaler

John Anthony Taylor

Surendra Verma

Tirunelveli R Vishwanath

David E Langworthy

Kartik Paramasivam

Guhan Suriyanarayanan

Bryan William Tuttle

Alejandro Matute Gonzalez

Huy Vu-Bao Hoang

Norbert Paul Kusters

Erik B Christensen

Kenneth David Wolf

Stephen James Millet

Giovanni Moises Della-Libera

David Wortendyke

Mark Joseph Zbikowski

Ran Kalach

Yosseff Levanoni

David Allen Dion

Saad Syed

Lance Olson

Richard Douglas Hill

Sushant Pramod Rewaskar

Affan Arshad Dar

Stefan R Batres

Umesh Madan

Michael James Coulson

Marcus Felipe Fontoura

Stefan Harrington Pharies

Craig Andrew Critchley

Alexander Dadiomov

Uday Sooryakant Hegde

Vijay Gajjala

Michael Marucheck

Dexter Paul Bradshaw

Krishnan Srinivasan

Elliot Lee Waingold

Geary L Eppley

Matthew Thomas Tavis

Tomasz Janczuk

Stephen T Swartz

David Edwin Levin

Michael Steven Vernal

Shy Cohen

Richard Dievendorff

Jagan M Peri

Dax H Hawkins

Douglas M Purdy

Natasha Harish Jethanandani

David Owen Driver

Aaron Stern

Siddhartha Puri

Saurab Nog

Jesus Ruiz-Scougall

Anastasios Kasiolas

Joe Dennis Long

Gopal Kakivaya

Douglas A Walter

Egidio Sburlino

Vipul A Modi

Ryan Thomas Sturgell

Milan Vukosavljevic

Muhunthan Sivapragasam

Sowmyanarayanan K Srinivasan

Jon Cargille

Pavithra Tyamagondlu Nagesharao

Scott Christopher Seely

Jonathan T Wheeler

Alexander Martin DeJarnatt

Mohammad Makarechian

Margaret J Lye

Evgeny Osovetsky

Yaniv Pessach

Erin Honeycutt

Mauro Ottaviani

Michael Thomas Dice

Chaitanya Dutt Upadhyay

Yasser Shohoud

Eugene Shvets

Alfred M Lee, Iv

Systems and methods are taught for providing customers of a cloud computing service to control when updates affect the services provided to the customers. Because multiple customers share the cloud's infrastructure, each customer may have conflicting preferences for when an update and associated downtime occurs. Preventing and resolving conflicts between the preferences of multiple customers while providing them with input for scheduling a planned update may reduce the inconvenience posed by updates. Additionally, the schedule for the update may be transmitted to customers so that they can prepare for the downtime of services associated with the update.

Tenant-controlled cloud updates

Embodiments are directed to a distributed computing system comprising a plurality of compute nodes for providing resources to users and a hierarchy of two or more layers of controllers coupling the compute nodes to a user interface via a control plane, wherein at least one compute node receives a local application program interface (API) call from an application running on the at least one compute node, the local API call causing the at least one compute node to configure a local resource without requiring commands from the control plane.

Data plane API in a distributed computing network

In various embodiments, methods and systems for optimizing allocation of multi-priority service instances are provided. In embodiments, a quality metric associated with each candidate node to which a service instance could be allocated is determined. An eviction cost or a survival metric associated with at least a portion of the candidate nodes to which the service instance could be allocated are determined. The eviction costs generally indicate a cost to evict a service instance from a corresponding node such that another service instance can be allocated to that node. At least a portion of the quality metrics and either the eviction costs or the survival metrics are used to select a node from the candidate nodes to which to allocate the service instance.

Multi-priority service instance allocation within cloud computing platforms

Techniques for facilitating allocation of cloud services in cloud computing systems are disclosed herein. In one embodiment, a method can include transmitting a request to service controllers configured to manage corresponding cloud services deployable in the cloud computing system, the request including a set of cloud services to be deployed in the cloud computing system. The method can also include receiving, from each of the service controllers, a list of at least some of the computing facilities arranged in order of preference for deploying one or more corresponding cloud services. The method can further includes selecting one of the computing facilities from the multiple computing facilities for deploying the set of cloud services based on the preferences of the service controllers reflected in the received lists and instructing the service controllers to deploy the one or more corresponding cloud services to the selected one of the computing facilities.

Service location management in computing systems

Technologies for managing fault recovery in a cloud computing environment may be used after faults of various sizes, including faults which put total functioning capacity below subscribed capacity. Computing services have repair priorities. A fault recovery manager selects a higher priority service whose capacity is below a minimum availability, and chooses a lower priority service still above its minimal availability, and reassigns capacity from the lower priority service to the higher priority service without depriving the lower priority service of operability. Capacity reassignment continues at least until the higher priority service is at or above minimal availability, or the lower priority service is at minimal availability. Lower priority services may also be terminated entirely to free up resources for higher priority services. New deployments may be prevented until all services are at or above minimal availability. Spare capacity may be reserved against demand fluctuations or further faults.

Fault recovery management in a cloud computing environment

In various embodiments, methods and systems for optimizing allocation of dynamic resources are provided. A user service request resource instance is received at an allocator. The user service request resource instance is based on a dynamic resource protocol that supports generating and communicating resource instances between components in a resource allocation platform. The dynamic resource protocol also defines a set of rules for translating and representing resources as logical resource types and logical units. At a node, a node resource instance is generated and communicated, based on the dynamic resource protocol, from the node to the allocator. The node resource instance specifically comprises a resource interaction matrix that indicates dependencies between resource types. A resource allocation instance is generated at the allocator for the user service request resource instance. The resource allocation instance is communicated, such that, resource types on the node are allocated based on the resource allocation instance.

Optimal allocation of dynamic cloud computing platform resources

In various embodiments, methods and systems for optimizing allocation of multi-priority service instances are provided. In embodiments, a packing quality metric associated with each candidate node to which a service instance could be allocated are determined. An eviction cost associated with at least a portion of the candidate nodes to which the service instance could be allocated are determined. The eviction costs generally indicate a cost to evict a service instance from a corresponding node such that another service instance can be allocated to that node. At least a portion of the packing quality metrics and the eviction costs are used to select a node from the candidate nodes to which to allocate the service instance.

Tenant-controlled cloud apparatus

Embodiments are directed to a distributed computing system comprising a plurality of compute nodes for providing resources to users and a hierarchy of two or more layers of controllers coupling the compute nodes to a user interface via a control plane, wherein at least one compute node receives a local application program interface (API) call from an application running on the at least compute node, the local API call causing the at least one compute node to configure a local resource without requiring commands from the control plane.

Certain embodiments of computing systems, devices, components, modules, routines, and processes for implementing distributed operational control in a computing fabric are described herein. In one embodiment, a method includes receiving, at a control cluster, a tenant request for a cloud-based computing service at the computing system. The method also includes creating an application configured to provide the requested cloud-based computing service based on the tenant request and pushing configuration data of the created application to the execution cluster to be executed at the execution cluster without further intervention from the control cluster.

Distributed operational control in computing systems

Tenant and server platform coordination of the performance of atomic tasks that are to be performed by the platform, but which have at least a potential impact on the tenant. The server platform allows a particular tenant a timeframe within which the tenant itself may approve of initiation of each of one or more tasks. The server platform may protect itself from delayed action by imposing time constraints on the timeframe such that after the timeframe is elapsed, the tenant no longer has control over triggering the initiation of the particular task. Accordingly, the tenant is provided with flexibility on controlling initiation of a task performed by the server platform, while the server platform maintains a level of control to protect its resources and balances the needs of the various tenants.

Tenant control in initiating atomic platform tasks

Growing community of inventors

Bellevue, WA, United States of America

James Ernest Johnson