Microsoft Technology Licensing, LLC

Other

Microsoft Corporation

Thomas Moscibroda

Ranveer Chandra

Paramvir Bahl

Stephen Edward Hodges

Julia Louise Meinershagen

Ajay Mani

Nissanka Arachchige Bodhi Priyantha

Anirudh Badam

Yang Chen

Ying Yan

Karthikeyan Subramanian

Mark Eugene Russinovich

Jiansong Zhang

Onur Mutlu

Shandan Zhou

Anthony John Ferrese

John R Douceur

Yongguang Zhang

James Ernest Johnson

Si Qin

Guo Chen

Peng Cheng

Yunnan Wu

Yongqiang Xiong

Gurpreet Virdi

Yunus Mohammed

Yuan Yuan

Jiaxing Zhang

Bojun Huang

Rohan Narayana Murty

Nisarg Tarakkumar Sheth

Ran Shu

Pan Hu

Evangelia Skiani

Douglas Christopher Burger

Edmund Bernard Nightingale

Manuel Costa

Yong Xu

David Chiyuan Chu

Yunxin Liu

Qingwei Lin

Marcus Felipe Fontoura

Jeremy P Condit

Ningyi Xu

Matthew Kerner

Jurgen Aubrey Willis

Eli Cortez

Jinglei Ren

Engin Ipek

Sorin Iftimie

Haoran Wang

Feng Zhao

Kun Tan

Jacob Rubin Lorch

Jonathan Alan Dutra

Narayanan Ganapathy

Yong He

James Wiliamson Mickens

Yuanchao Shu

Saurabh Agarwal

Hang Dong

Bo Qiao

Haichen Shen

Inigo Goiri Presa

Zhuqi Li

Liang Chen

Alastair Wolman

Jitendra Dattatraya Padhye

Ivan Jelev Tashev

Ramachandran Ramjee

Lintao Zhang

Ratul Mahajan

Benjamin Zorn

Liang Jeff Chen

Ke Wang

Lenin Ravindranath Sivalingam

Yimin Deng

Karthik Pattabiraman

Vishnu Navda

Hariharan Rahul

Aditya Bhandari

John Lawrence Miller

Eric Chang

Gaurav Jagtiani

Sameh Elnikety

Sandeep P Karanth

Chandrasekhar Pasupuleti

Zhengping Qian

Ricardo Gouvêa Bianchini

Joseph Yossi Azar

Brian Jacob Corell

Chieh-Jan Mike Liang

Eli Cortez Custodio Vilarinho

Guobin (Jacky) Shen

Jayanta Mondal

Sanjay Ramanujan

Eyal Lubetzky

Abhay Sudhir Ketkar

Qi Liu

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

According to implementations of the subject matter described herein, there is proposed a solution for supporting communications for an FPGA device. In an implementation, the FPGA device includes an application module and protocol stack modules. The protocol stack modules are operable to access target devices based on different communication protocols via a physical interface. The FPGA device further includes a universal access module operable to receive, from the application module, first data and a first identity of a first target device, the first target device acting as a destination of the first data, and transmit, based on the first identity and predetermined first routing information, the first data to a first protocol stack module accessible to the first target device via the physical interface. By introducing the universal access module, it is possible to provide unified and direct communications for the application module.

Communications for field programmable gate array device

The present disclosure relates to systems, methods, and computer readable media for predicting expansion failures and implementing defragmentation instructions based on the predicted expansion failures and other signals. For example, systems disclosed herein may apply a failure prediction model to determine an expansion failure prediction associated with an estimated likelihood that deployment failures will occur on a node cluster. The systems disclosed herein may further generate defragmentation instructions indicating a severity level that a defragmentation engine may execute on a cluster level to prevent expansion failures while minimizing negative customer impacts. By uniquely generating defragmentation instructions for each node cluster, a cloud computing system can minimize expansion failures, increase resource capacity, reduce costs, and provide access to reliable services to customers.

Predicting expansion failures and defragmenting cluster resources

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 computer implemented method includes receiving telemetry data corresponding to capacity health of nodes in a cloud based computing system. The received telemetry data is processed via a prediction engine to provide predictions of capacity health at multiple dimensions of the cloud based computing system. Node recoverability information is received and node recovery execution is initiated as a function of the representations of capacity health and node recoverability information.

Capacity aware cloud environment node recovery system

The present disclosure describes a type of virtual machine, which the present disclosure may refer to as a harvest virtual machine, that may allow improved utilization of physical computing resources on a cloud-computing system. First, the harvest virtual machine may be evictable. In other words, higher priority virtual machines may preempt the harvest virtual machine's access to physical computing resources. Second, the harvest virtual machine may receive access to a dynamic amount of physical computing resources during the course of its operating life. Third, the harvest virtual machine may have a minimum size (in terms of an amount of physical computing resources) and may terminate whenever the harvest virtual machine has access to an amount of physical computing resources less than the minimum size.

Harvest virtual machine for utilizing cloud-computing resources

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

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

A method for controlling a redundant storage system is proposed. A write request to a redundant storage system is received (). A dataset that is to be written into the redundant storage system by the write request is determined (). A portion of the dataset is logged into the redundant storage system for data recovery in case that a fault occurs in the redundant storage system (). Thus, only a portion of the dataset is logged and the amount of the logged data may be reduced compared with the conventional redundant storage system. Further, the redundant storage system may be recovered in response to a fault occurring in the redundant storage system.

Storage system control

The present disclosure relates to systems, methods, and computer readable media for predicting deployment growth on one or more node clusters and selectively permitting deployment requests on a per cluster basis. For example, systems disclosed herein may apply tenant growth prediction system trained to output a deployment growth classification indicative of a predicted growth of deployments on a node cluster. The system disclosed herein may further utilize the deployment growth classification to determine whether a deployment request may be permitted while maintaining a sufficiently sized capacity buffer to avoid deployment failures for existing deployments previously implemented on the node cluster. By selectively permitting or denying deployments based on a variety of factors, the systems described herein can more efficiently utilize cluster resources on a per-cluster basis without causing a significant increase in deployment failures for existing customers.

Allocating cloud resources in accordance with predicted deployment growth

The present disclosure relates to systems, methods, and computer readable media that utilize a low-impact live-migration system to reduce unfavorable impacts caused as a result of live-migrating computing containers between physical server devices of a cloud computing system. For example, systems disclosed herein evaluates characteristics of computing containers on server devices to determine a predicted unfavorable impact of live-migrating the computing containers between the server devices. Based on the predicted impact, the systems disclosed herein can selectively identify which computing containers to live-migrate as well as carry out live-migration of the select computing containers in such a way the significantly reduces unfavorable impacts to a customer or client device associated with the computing containers.

Minimizing impact of migrating virtual services

Systems, methods, and computer-readable media for determining column ordering of a data storage table for search optimization are described herein. In some examples, a computing system is configured to receive input containing statistics of a plurality of queries. The computing system can then determine a new column order (i.e., layout) based at least in part on the statistics. In some example techniques described herein, the computing system can determine the new column order based at least in part on the hardware components storing the data storage table, storage system parameters, and/or user preference information. Example techniques described herein can apply the new column order to data subsequently added to the data storage table. Example techniques described herein can apply the new column order to existing data in the data storage table.

Column ordering for input/output optimization in tabular data

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Redmond, WA, United States of America

Thomas Moscibroda