Google Inc.

Yahoo! Inc.

Cask Data, Inc.

Oath Inc.

Nitin Motgi

Derek Lewis Wood

Albert Yen Shau

Jonathan Michael Gray

Poorna Chandra Gowda Bannikkuppe Ramachandra

Christopher Gianelloni

Rohit Sarma Nistala

Deepak Agarwal

Bee-Chung Chen

Pradheep K Elango

Wei Chu

H Scott Roy

Raghunath Ramakrishnan

John Thrall

Amitabh Seth

Raghu Ramakrishnan

Howard Scott Roy

Vik Singh

Bruno Fernandez-Ruiz

Vijay Krishna Narayanan

Joe Zachariah

Sharat Israni

Chandar Venkataraman

Amit Phadke

Michael Salisbury

A processing device receives a first cluster management operation request, wherein the first cluster management operation request is for a deployment operation of a first cluster and comprises a cluster template, a specified number of nodes to deploy and a configuration. The processing device determines a plurality of constraints from at least one of the cluster template or the configuration. The processing device deterministically solves for and outputs a first cluster layout comprising the specified number of nodes, each of the specified number of nodes comprising a hardware type, an image type and one or more services, wherein the first cluster layout satisfies the plurality of constraints.

Solver for cluster management system

System, method, and device for providing services on a network. The device comprises a security assessor and a service provider unit. The security assessor is connected to the network and is configured to identify rights of an entity on the network. The service provider unit is connected to the network and the security assessor. The service provider unit comprises a discovery unit, an interaction unit, and an interest unit. The discovery unit identifies content available on the network. The interaction unit identifies interactions of the entity on the network. The interest unit identifies interests of the entity based on the identified interactions and the identified content. The service provider unit provides services to the entity on the network, based on the rights of the entity, and at least one of the identified content and the identified interests of the entity.

Network appliance

A processing device executing a provisioner requests a node level task from a server. The processing device receives an assignment of a first node level task, wherein the first node level task is associated with a cluster management operation for a first cluster. The processing device determines a first plugin capable of performing the first node level task from a plurality of plugins, wherein each of the plurality of plugins comprises a self-contained program for performing a specific set of node level tasks. The processing device performs the first node level task by the first plugin, wherein the first node level task is an operation on a remote target host that does not include software associated with the provisioner. The processing device reports a result of the node level task to the server.

Provisioner for cluster management system

A processing device receives a starting cluster layout for a first cluster, a target cluster layout for the first cluster, and a cluster management operation for creating or modifying the first cluster. The processing device determines a plurality of node level tasks to transition the first cluster from the starting cluster layout to the target cluster layout, determines dependencies between the plurality of node level tasks, and determines an order for the plurality of node level tasks based on the cluster management operation and the dependencies. The processing device then creates an execution plan that divides the cluster management operation into the plurality of node level tasks in the determined order.

Planner for cluster management system

Content items are selected to be displayed on a portal page in such a way as to maximize a performance metric such as click-through rate. Problems relating to content selection are addressed, such as changing content pool, variable performance metric, and delay in receiving feedback on an item once the item has been displayed to a user. An adaptation of priority-based schemes for the multi-armed bandit problem, are used to project future trends of data. The adaptation introduces experiments concerning a future time period into the calculation, which increases the set of data on which to solve the multi-armed bandit problem. Also, a Bayesian explore/exploit method is formulated as an optimization problem that addresses all of the issues of content item selection for a portal page. This optimization problem is modified by Lagrange relaxation and normal approximation, which allow computation of the optimization problem in real time.

Enhanced matching through explore/exploit schemes

Content items are selected to be displayed on a portal page in such a way as to maximize a performance metric such as click-through rate. Problems relating to content selection are addressed, such as changing content pool, variable performance metric, and delay in receiving feedback on an item once the item has been displayed to a user. An adaptation of priority-based schemes for the multi-armed bandit problem are used to project future trends of data. The adaptation introduces experiments concerning a future time period into the calculation, which increases the set of data on which to solve the multi-armed bandit problem. Also, a Bayesian explore/exploit method is formulated as an optimization problem that addresses all of the issues of content item selection for a portal page. This optimization problem is modified by Lagrange relaxation and normal approximation, which allow computation of the optimization problem in real time.

A method and apparatus for customizing content presented to individual users or user segments is provided. There may be three components, a web portal and toolbar component, a modeling component, and a scoring component. The web portal and toolbar component presents content items and collects data. The web portal and toolbar component generates user event data based on the user actions. The user event data is forwarded to the modeling component. The modeling component generates content scoring functions based on user event data and attributes of content items. Content scoring functions may be unique to individual user segments. The content scoring functions based on content features generate probability a content item will be viewed. The scoring component decides which content items are placed in a portal. The scoring component uses the scoring functions generated by the modeling component to rank content items in real time.

Customized today module

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Santa Clara, CA, United States of America

Nitin Motgi