Nicira, Inc.

Maplebear Inc.

Xuan Zhang

Hua Wang

Mukesh Hira

Ram Dular Singh

Alexander Tessmer

Rajiv Krishnamurthy

Ronghua Zhang

Jianjun Shen

Ankur Dubey

Sreeram Kumar Ravinoothala

James Joseph Stabile

Haixun Wang

Tejaswi Tenneti

Xiao Xiao

Taesik Na

Vinesh Reddy Gudla

Ruhan Zhang

Xiaochen Wang

An online system generates a template shopping list for a user by accessing a machine learning model trained based on historical order information associated with the user, applying the model to predict likelihoods of conversion for item categories by the user, and populating the template shopping list with one or more item categories based on the predicted likelihoods. The system ranks one or more item types associated with each item category in the template shopping list and determines a set of collection rules associated with one or more item categories/types based on the historical order information. The system generates a suggested shopping list by populating each item category in the template shopping list with one or more item types and a quantity of each item type based on the ranking and rules and sends the suggested shopping list and rules for display to a client device associated with the user.

Generating a suggested shopping list by populating a template shopping list of item categories with item types and quantities based on a set of collection rules

An online system updates the labels on negative examples to account for the possibility that the example is a false negative. The system generates a set of initial training examples that each include a query input by the user and item data for an item presented as a result to the user's query. Each training example also includes an initial label, which represents whether the user interacted with the item presented as a search result. The online system updates the initial label for a negative training example by identifying a set of bridge queries and computing a similarity score between the query for the training example and the bridge queries. The online system computes an updated label for the negative example based on the similarity scores and updates the training example with the updated label.

False negative prediction for training a machine-learning model

A novel method for performing replication of messages in a network that bridges one or more physical networks to an overlay logical network is provided. A physical gateway provides bridging between network nodes of a physical network and virtual machines in the overlay logical network by serving as an endpoint of the overlay logical network. The physical gateway does not replicate messages from the bridged physical network to destination endpoints in the overlay logical network directly, but instead tunnels the message-to-be-replicated to a designated tunnel endpoint in the overlay logical network. The designated tunnel endpoint in turn replicates the message that was tunneled to it to other endpoints in the overlay logical network.

Replicating broadcast, unknown-unicast, and multicast traffic in overlay logical networks bridged with physical networks

Some embodiments provide a novel method for distributing control-channel communication load between multiple controllers in a network control system. In some embodiments, the controllers manage physical forwarding elements that forward data between several computing devices (also called hosts or host computers), some or all of which execute one or more virtual machines (VMs). The method of some embodiments distributes a controller assignment list to the host computers. The host computers use this list to identify the controllers with which they need to interact to perform some of the forwarding operations of their associated logical forwarding elements. In some embodiments, agents executing on the host computers (1) review the controller assignment list to identify the appropriate controllers, and (2) establish control channel communications with these controllers to obtain the needed data for effectuating the forwarding operations of their associated physical forwarding elements. These agents in some embodiments are responsible for out-of-band control channel communication with the controllers.

Host side method of using a controller assignment list

Some embodiments provide method for implementing a logical router of a logical network. The method receives a configuration for a first logical router. The configuration includes a static route for the first logical router. The method defines several routing components with separate routing tables for the logical router. The method adds a first route, having a first static route type, for the static route to the routing tables of at least a first subset of the routing components. Based on the connection of a second logical router to the first logical router, adding a second route, having a second static route type, to the routing tables of at least a second subset of the routing components.

Static route types for logical routers

Controller side method of generating and updating a controller assignment list

Growing community of inventors

Palo Alto, CA, United States of America

Xuan Zhang