Amazon Technologies, Inc.

Akash Kayal

Brandon William Porter

Luan Khai Nguyen

Michael James McInerny

Darryl Havens

Jane Mooney

Donald Arthur Parsons

Joshua Chittle

Robert Karl Beckmeyer

Techniques described herein include systems and methods for throttling requests for content to reduce stress on a check out pipeline associated with an electronic marketplace thereby avoiding overstressing a server to the point of no longer processing requests from users. In embodiments, first information may be maintained that identifies a ranking for a plurality of items based on a score. A predicted velocity of content requests about the plurality of items may be maintained and second information about an actual velocity of content requests about the plurality of items may be received. In response to a request for content, a portion of items may be identified based on the scores associated with said portion and partition the portion into a number of groups or partitions based on the predicted velocity and the second information. A data object that comprises the portion of items associated with a partition may be generated.

Method and system for dynamic traffic shaping of deals to reduce server stress

Generally described, one or more aspects of the present application correspond to an artificial intelligence (AI) system that helps developers build failure-resilient microservices. The AI system can analyze the code of a microservice to identify other, similar microservices, and then use historical data about performance of those similar microservices to build a failure model for the microservice. The AI system can also automatically run failure tests based on the failure model, and can suggest code changes (based on analyzing historical code changes and test performance data) that can help a microservice become resilient to a particular failure.

Microservice failure modeling and testing

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Seattle, WA, United States of America

Akash Kayal