公开(公告)号：US20160316003A1

公开(公告)日：2016-10-27

申请号：US14852233

申请日：2015-09-11

Applicant: Microsoft Technology Licensing LLC

Inventor： MATTHEW SNIDER ,  Anurag Gupta ,  Lu Xun ,  Yang Li ,  Gopal Kakivaya ,  Hua-Jun Zeng

IPC: H04L29/08 ,  H04L12/911

CPC classification number: H04L67/1002 ,  G06F9/505 ,  G06F9/5077 ,  H04L47/70

Abstract: In various implementations, methods and systems resource balancing in a distributed computing environment are provided. A client defined resource metric is received that represents a resource of nodes of the cloud computing platform. A placement plan for job instances of service applications is generated. The placement plan includes one or more movements that are executable to achieve a target placement of the job instances on the nodes. It is determined that the placement plan complies with placement rules. Each placement rule dictates whether a given job instance of the job instances is suitable for placement on a given node of the nodes. The placement plan is executed based on determining that the target placement of the job instances improves balance of resources across the nodes of the cloud computing platform based on the resource represented by the client defined resource metric.

Abstract translation: 在各种实现中，提供了分布式计算环境中的方法和系统资源平衡。 接收到表示云计算平台的节点资源的客户端定义的资源度量。 生成服务应用程序作业实例的放置计划。 放置计划包括可执行以实现作业实例在节点上的目标放置的一个或多个移动。 确定展示位置计划符合展示位置规则。 每个放置规则指示作业实例的给定作业实例是否适合放置在节点的给定节点上。 基于由客户端定义的资源度量表示的资源，基于确定作业实例的目标放置改善云计算平台的节点之间的资源平衡来执行放置计划。

公开(公告)号：US10678997B2

公开(公告)日：2020-06-09

申请号：US15825657

申请日：2017-11-29

Applicant: Microsoft Technology Licensing, LLC

Inventor： Karan Ashok Ahuja ,  Befekadu Ayenew Ejigou ,  Ningfeng Liang ,  Lokesh P. Bajaj ,  Wei Wang ,  Paul Fletcher ,  Wei Lu ,  Shaunak Chatterjee ,  Souvik Ghosh ,  Yang Li ,  Wei Deng ,  Qiang Wu

IPC: G06F17/00 ,  G06F40/174 ,  G06Q50/00 ,  G06N20/00 ,  H04L29/08

Abstract: In an example, first and second machine learned models corresponding to a particular context of a social networking service are obtained, the first machine learned model trained via a first machine learning algorithm to output an indication of importance of a social networking profile field to obtaining results in the particular context, and the second machine learned model trained via a second machine learning algorithm to output a propensity of the user to edit a social networking profile field if requested. One or more missing fields in a social networking profile for the user are identified. For each of one or more of the one or more missing fields, the field and an identification of the user are passed through the first and second machine learned models, and outputs of the first and second machine learned models are combined to identify one or more top missing profile fields.

公开(公告)号：US20200287961A1

公开(公告)日：2020-09-10

申请号：US16803836

申请日：2020-02-27

Applicant: Microsoft Technology Licensing, LLC

Inventor： Matthew Snider ,  Anurag Gupta ,  Lu Xun ,  Yang Li ,  Gopal Kakivaya ,  Hua-Jun Zeng

IPC: H04L29/08 ,  H04L12/911 ,  G06F9/50 ,  G06F9/455

Abstract: In various implementations, methods and systems resource balancing in a distributed computing environment are provided. A client defined resource metric is received that represents a resource of nodes of the cloud computing platform. A placement plan for job instances of service applications is generated. The placement plan includes one or more movements that are executable to achieve a target placement of the job instances on the nodes. It is determined that the placement plan complies with placement rules. Each placement rule dictates whether a given job instance of the job instances is suitable for placement on a given node of the nodes. The placement plan is executed based on determining that the target placement of the job instances improves balance of resources across the nodes of the cloud computing platform based on the resource represented by the client defined resource metric.

公开(公告)号：US20190108209A1

公开(公告)日：2019-04-11

申请号：US15825657

申请日：2017-11-29

Applicant: Microsoft Technology Licensing, LLC

Inventor： Karan Ashok Ahuja ,  Befekadu Ayenew Ejigou ,  Ningfeng Liang ,  Lokesh P. Bajaj ,  Wei Wang ,  Paul Fletcher ,  Wei Lu ,  Shaunak Chatterjee ,  Souvik Ghosh ,  Yang Li ,  Wei Deng ,  Qiang Wu

IPC: G06F17/24 ,  G06F15/18 ,  G06Q50/00 ,  H04L29/08

Abstract: In an example, first and second machine learned models corresponding to a particular context of a social networking service are obtained, the first machine learned model trained via a first machine learning algorithm to output an indication of importance of a social networking profile field to obtaining results in the particular context, and the second machine learned model trained via a second machine learning algorithm to output a propensity of the user to edit a social networking profile field if requested. One or more missing fields in a social networking profile for the user are identified. For each of one or more of the one or more missing fields, the field and an identification of the user are passed through the first and second machine learned models, and outputs of the first and second machine learned models are combined to identify one or more top missing profile fields.