Microsoft Technology Licensing, LLC

Huaming Huang

Avnish Kumar Chhabra

Abhishek Kumar Singh

Muhammad Usman Sharif

Yue Zhao

Michael Charles Stephenson

Siddharth Verma

Pallavi Baral

Bhuvan Malladihalli Shashidhara

Sisi Xiong

Yilan Zhang

Ash Beitz

Prateek Punj

Juan-Arturo Herrera Ortiz

Arbab Amjad

Abhishek Kumar

Hanyi Xu

Mayank Meghwanshi

Abhishek Singh

The present concepts may automate and optimize deployment to a cloud computing fleet. Artificial intelligence and/or optimization algorithms may be used to find optimal deployment parameters (e.g., deployment order of computers in the fleet) that minimize deployment time and minimize deployment risk. For example, machine-learning prediction models may be used to generate a shortest path graph problem models a deployment planning problem. Then, optimization algorithms may be used to efficiently find approximations of Pareto-optimal solutions to the shortest path graph problem. Depending on the preferred level of time and risk, one of the solutions may be used to run the deployment.

Smart deployment using graph optimization

Methods, systems, apparatuses, and computer program products are provided that enable the automated deployment of microservices to a network-accessible server set. The automated deployment may be based on constraint(s) that are specified by a declarative deployment model that is associated with the microservice to be deployed. For example, a centralized deployment orchestrator may receive microservice(s) and their associated declarative deployment model(s). The deployment orchestrator analyzes the declarative deployment model(s) and determines which microservice(s) are to be deployed based on the constraint(s) specified by the declarative deployment model(s). The foregoing techniques advantageously determine when to deploy microservice(s), while also minimizing human intervention typically required to deploy microservice(s). Moreover, by controlling when certain microservice(s) are deployed and/or which microservice(s) are deployed, deployed microservice(s) that function incorrectly and/or cause at least a portion of the network-accessible computing environment to malfunction can be easily identified.

Automated constraint-based deployment of microservices to cloud-based server sets

Methods, systems, and apparatuses manage rolling out of updates in a network-accessible server infrastructure which operates a plurality of instances of a supporting service. The supporting service is comprised by a plurality of service portions. The instances of the supporting service each include of the service portions. The instances of the supporting service are partitioned into a plurality of slices. Each instance is partitioned to include one or more of the slices, and each slice of an instance includes one or more of the service portions. A software update is deployed to the instances by applying the software update to the slices in a sequence such that the software update is applied to a same slice in parallel across the instances containing that same slice before being applied to a next slice, and waiting a wait time before applying the software domain to a next slice in the sequencing.

Safe and agile rollouts in a network-accessible server infrastructure using slices

Growing community of inventors

Sammamish, WA, United States of America

Huaming Huang