Microsoft technology licensing, llc (20240111739). TUNING LARGE DATA INFRASTRUCTURES simplified abstract

TUNING LARGE DATA INFRASTRUCTURES

Organization Name

microsoft technology licensing, llc

Inventor(s)

Yiwen Zhu of Sunnyvale CA (US)

Subramaniam Venkatraman Krishnan of Santa Clara CA (US)

Konstantinos Karanasos of San Francisco CA (US)

Carlo Curino of Woodinville WA (US)

Isha Tarte of Woodinville WA (US)

Sudhir Darbha of Redmond WA (US)

TUNING LARGE DATA INFRASTRUCTURES - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240111739 titled 'TUNING LARGE DATA INFRASTRUCTURES

Simplified Explanation

An automated tuning service is used to automatically adjust the operational parameters of a large-scale cloud infrastructure based on real-time performance data.

• The tuning service utilizes machine learning models to optimize parameter tuning and inform strategic decisions.
• It combines observational models with testing in production to configure operational parameters automatically.
• The service is designed to cater to a wide range of applications in a large cloud infrastructure.

Potential Applications

The technology can be applied in various industries such as e-commerce, healthcare, finance, and entertainment to optimize cloud infrastructure performance.

Problems Solved

1. Manual tuning of operational parameters can be time-consuming and error-prone. 2. Inefficient cloud infrastructure performance can lead to increased costs and decreased user satisfaction.

Benefits

1. Improved performance and efficiency of cloud infrastructure. 2. Cost savings through automated parameter tuning. 3. Enhanced user experience with optimized cloud services.

Potential Commercial Applications

Optimizing cloud infrastructure for large enterprises, cloud service providers, and data centers to improve performance and reduce operational costs.

Possible Prior Art

One possible prior art could be the use of manual tuning processes in cloud infrastructure management before the introduction of automated tuning services.

How does the tuning service handle security considerations in parameter optimization?

The article does not address how the tuning service ensures that security is not compromised during the automated parameter optimization process.

What are the potential limitations or challenges faced by the tuning service in optimizing complex cloud infrastructures?

The article does not discuss any potential limitations or challenges that the tuning service may encounter when optimizing operational parameters in complex cloud infrastructures.

Original Abstract Submitted

an automated tuning service is used to automatically tune, or modify, the operational parameters of a large-scale cloud infrastructure. the tuning service performs automated and fully data/model-driven configuration based from learning various real-time performance of the cloud infrastructure. such performance is identified through monitoring various telemetric data of the cloud infrastructure. the tuning service leverages a mix of domain knowledge and principled data-science to capture the essence of our cluster dynamic behavior in a collection of descriptive machine learning (ml) models. the ml models power automated optimization procedures for parameter tuning, and inform administrators in most tactical and strategical engineering/capacity decisions (such as hardware and datacenter design, software investments, etc.). rich “observational” models (models collected without modifying the system) are combined with judicious use of “fighting” (testing in production), allowing the tuning service to automatically configure operational parameters of a large cloud infrastructure for a broad range of applications.