Cupertino, CA, United States of America

Xiaotang Shao

USPTO Granted Patents = 1 

Average Co-Inventor Count = 1.0


Company Filing History:


Years Active: 2025

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1 patent (USPTO):Explore Patents

Title: Innovations by Xiaotang Shao in Predictive Autoscaling

Introduction

Xiaotang Shao is an accomplished inventor based in Cupertino, CA (US). He has made significant contributions to the field of resource utilization forecasting, particularly in the context of predictive autoscaling. His innovative approach aims to enhance the efficiency of container-based clusters, which are increasingly vital in modern computing environments.

Latest Patents

Xiaotang Shao holds a patent titled "Resource utilization forecasting for predictive autoscaling." This patent outlines a method that includes determining resource utilization metrics for multiple instances of a service running in a container-based cluster over various timestamps. The process involves applying a smoothing filter to these metrics, adjusting them by a nominal value, and calculating ratio metrics. A machine learning model is then utilized to predict future resource utilization, allowing for automatic adjustments to the configuration parameters of the cluster based on these predictions. This innovative method enhances the performance and scalability of services in dynamic environments.

Career Highlights

Xiaotang Shao is currently employed at Intuit, Inc., where he continues to develop cutting-edge technologies that improve resource management and operational efficiency. His work is instrumental in advancing the capabilities of predictive analytics in cloud computing.

Collaborations

Some of Xiaotang's notable coworkers include Hui Luo and Estela Ramirez. Their collaborative efforts contribute to the innovative environment at Intuit, fostering advancements in technology and resource management.

Conclusion

Xiaotang Shao's contributions to predictive autoscaling through his patent demonstrate his commitment to innovation in technology. His work not only enhances the efficiency of container-based clusters but also sets a foundation for future advancements in resource utilization forecasting.

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