Microsoft Technology Licensing, LLC

Yunus Mohammed

Thomas Moscibroda

Gurpreet Virdi

Mark Eugene Russinovich

Karthikeyan Subramanian

Pritesh Patwa

Apurva F Dalia

Sandeep P Karanth

Christopher J Saam

Venugopala Rao Moram

Christopher Gariepy

Joydeep Bhattacharyya

Si Qin

Shandan Zhou

Hang Dong

Bo Qiao

Ravikiran Janardhan Reddy

Marko Lalic

Rodrigo Junqueira Lode

Lizhong Huang

Phani Soma Shekar Burela

Xiaoxiong Tian

Sean David Zimmerman

Gregory Marvin Door

Yuwen Yang

Parveen Kumar Patel

Marcus Felipe Fontoura

Jun Wang

Avnish Kumar Chhabra

Srinath Setty

Michael Ari Cohen

Inigo Goiri Presa

Sorin Iftimie

Sanjay Ramanujan

Eli Cortez Custodio Vilarinho

Ziv Rafalovich

Felipe Vieira Frujeri

Yandan Wang

Joseph A Kennebec

Xing Wen Wang

Rostislav Sudakov

Vi Lam Nguyen

Fernando Gonzalez Todisco

Sandeep Kumta Vishnu

Rakesh Akkera

Kamel Sbaia

The present disclosure relates to systems, methods, and computer readable media for predicting surplus capacity on a set of server nodes and determining a quantity of deferrable virtual machines (VMs) that may be scheduled over an upcoming period of time. This determination of VM quantity may be determined while minimizing risks associated with allocation failures on the set of server nodes. This disclosure described systems that facilitate features and functionality related to improving utilization of surplus resource capacity on a plurality of server nodes by implementing VMs having some flexibility in timing of deployment while also avoiding significant risk caused as a result of over-allocated storage and computing resources. In one or more embodiments, the quantity of deferrable VMs is determined and scheduled in accordance with rules of a scheduling policy.

Allocating computing resources for deferrable virtual machines

Systems and methods are provided for scheduling a virtual machine (VM) to host a workload in a cloud system. In particular, the disclosed technology schedules an evicted VM for redeploying an interruptible workload. The scheduling is based on capacity prediction and inference data associated with a type of the evicted VM. Capacity signal predictor generates training data for training a machine learning model using capacity signal history data of the cloud system. The machine-learning model, once trained, predicts capacity including a rate of evictions for the types of the evicted VM. The predicted data is based on at least the current status of available computing resources. Upon receiving a notice associated with a workload interruption, the intelligent scheduler prioritizes the evicted VM for scheduling and determines whether to defer redeploying the evicted VM based on the rate of eviction for the type of the evicted VM.

Machine-learning-based replenishment of interruptible workloads in cloud environment

A cloud computing capacity management system can include a fine-grained admission control layer, a policy engine, and an enforcement layer. The fine-grained admission control layer can be configured to ingest capacity signals and create a capacity mitigation policy, based at least in part on the capacity signals, to protect available capacity of a cloud computing system for prioritized users. The capacity mitigation policy can be directed to users of the cloud computing system. The policy engine can be configured to control how the capacity mitigation policy is applied to the cloud computing system. The enforcement layer can be configured to handle incoming resource requests and to enforce resource limits based on the capacity mitigation policy as applied by the policy engine.

Cloud computing capacity management system using automated fine-grained admission control

A storage account is replicated across multiple data centers so as to withstand an outage of the storage account. A front end and applications use data stored in the storage accounts to manage resources of a cloud computing system. A client includes an interface used by the front end to access the storage accounts, in addition to an interface that is used by the applications to access the storage accounts. Other features, which ensure that a read operation survives even if a head replica or a tail replica is down, include reading from a head replica instead of from a tail replica, a two phase prepare-commit operation to propagate changes from head to tail replicas, and provisioning a back end repair service if a failure occurs in the middle of the prepare-commit operation.

Replicating storage tables used to manage cloud-based resources to withstand storage account outage

Various methods and systems for autonomously upgrading deployed resources in a distributed computing environment are provided. An autonomous upgrade system identifies updates such as operating system image updates and virtual machine extension updates for deployment in the distributed computing environment. The autonomous upgrade system identifies eligible tenants, identifies deployed resources that may be impacted by the identified update, batches the resources and upgrades the batched resources. The autonomous upgrade system performs a diagnostic test on upgraded resources to determine whether an upgrade was successful. In some embodiments, the diagnostic test is performed by executing a diagnostic script that can be provided by a tenant. The autonomous upgrade system can stop or pause the upgrade if various success metrics are not satisfied. In some embodiments, the autonomous upgrade system tests and certifies newly published updates for deployment to the distributed computing environment.

Autonomous upgrade of deployed resources in a distributed computing environment

Software updates within one or more regions of a multi-tenant cloud are coordinated. Tenant vs. tenant conflicts, tenant vs. infrastructure provider conflicts, and conflicts between security and another priority are identified and resolved using a shared update coordinator, update priority specifications, and availability specifications. An infrastructure update request may be presented to tenants for approval. Postponed infrastructure updates may be prioritized higher. Preventing exploits of zero-day vulnerabilities may be prioritized over meeting availability targets. Updates may be merged to reduce downtime, even when the updates originate from independently controlled entities. Maximum downtime, minimum fault domains, minimum virtual machines, permitted update start times, and other availability criteria may be specified. Updates may be preempted, or allowed to complete, based on their relative priorities. Tenants and infrastructure providers may still use their own deployment engines after updates are coordinated. Rogue behaviors involving adverse update approvals or disapprovals may be detected.

Update coordination in a multi-tenant cloud computing environment

Methods and computer-readable media are provided herein for implementing custom application programming interfaces (API) for creating, managing, and provisioning packages of online applications. An offer management service API is implemented that provides methods for creating and modifying offers for packages of online applications. An offer provisioning API is implemented that provides methods enabling the provisioning of online applications for subscribers to offers. An application provider API is published and utilized to provision and maintain individual instances of online applications provided by application providers.

Creating managing, and provisioning packages of online applications

Creating, managing, and provisioning packages of online applications

Online services offer modeling is provided. A system for managing online offers for products and services includes a components module, an offer module, a subscription module, a billing module and a change management module. The component module defines the properties of components that may be included in an offer. The offer module defines the features of the offer, including one or more components. The components may be a base component or an add-on component. The subscription module processes the provisioning of an offer. The market module specifies countries or regions in which offers are made available. The billing module processes billing and payment actions for an offer. The change management module processes alterations to a provisioned offer, inserting the properties of an add-on component into the properties of a base component.

Online services offer management

A compressed lexicon is built by receiving a word list, which includes word-dependent data associated with each word in the word list. A word is selected from the word list. A hash value is generated based on the selected word, and the hash value identifies an address in a hash table which, in turn, is written with a location in lexicon memory that is to hold the compressed form of the selected word, and the compressed word-dependent data associated with the selected word. The word is then encoded, or compressed, as is its associated word-dependent data. This information is written at the identified location in the lexicon memory.

Compressed speech lexicon and method and apparatus for creating and accessing the speech lexicon

A strategy is described for alerting a user when an Internet domain becomes active. The strategy includes receiving a domain change request and logging this request in a domain table. The domain change request can instruct a registrar to create a new domain, or modify an existing domain (such as by transferring or re-delegating an existing domain). Monitoring logic is configured to use the domain table to perform periodic checks to determine whether the domain has become active. If the domain has become active, notification logic is configured to send a recipient a message which alerts the recipient to the activation of the Internet domain. The notification logic can send this message using electronic mail or some other communication mechanism.

Providing notifications for domain registration changes

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Bellevue, WA, United States of America

Yunus Mohammed