Microsoft Technology Licensing, LLC

Abhishek Roy

Alekh Jindal

Hiren Shantilal Patel

Shi Qiao

Marc Todd Friedman

Markus Weimer

Irene Rogan Shaffer

Gilbert Antonius

Lucas Rosenblatt

Soundararajan Srinivasan

Vijay Kumar Ramani

Remmelt Herbert Lieve Ammerlaan

H M Sajjad Hossain

Peter Orenberg

Carlo Aldo Curino

Rathijit Sen

Subramaniam Venkatraman Krishnan

Zhicheng Yin

Methods of machine learning for system deployments without performance regressions are performed by systems and devices. A performance safeguard system is used to design pre-production experiments for determining the production readiness of learned models based on a pre-production budget by leveraging big data processing infrastructure and deploying a large set of learned or optimized models for its query optimizer. A pipeline for learning and training differentiates the impact of query plans with and without the learned or optimized models, selects plan differences that are likely to lead to most dramatic performance difference, runs a constrained set of pre-production experiments to empirically observe the runtime performance, and finally picks the models that are expected to lead to consistently improved performance for deployment. The performance safeguard system enables safe deployment not just for learned or optimized models but also for additional of other ML-for-Systems features.

System and method for machine learning for system deployments without performance regressions

The cloud-based query workload optimization system disclosed herein the cloud-based query workloads optimization system receives query logs from various query engines to a cloud data service, extracts various query entities from the query logs, parses query entities to generate a set of common workload features, generates intermediate representations of the query workloads, wherein the intermediate representations are agnostic to the language of the plurality of the queries, identifies a plurality of workload patterns based on the intermediate representations of the query workloads, categorizes the workloads in one or more workload type categories based on the workload patterns and the workload features, and selects an optimization scheme based on the category of workload pattern.

Cloud based query workload optimization

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Bellevue, WA, United States of America

Abhishek Roy