公开(公告)号：US20240111739A1

公开(公告)日：2024-04-04

申请号：US18534559

申请日：2023-12-08

Applicant: Microsoft Technology Licensing, LLC

Inventor： Yiwen ZHU ,  Subramaniam Venkatraman KRISHNAN ,  Konstantinos KARANASOS ,  Carlo CURINO ,  Isha TARTE ,  Sudhir DARBHA

IPC: G06F16/21 ,  G06F11/30 ,  G06F11/34 ,  G06F16/17 ,  G06F16/182 ,  G06F16/188 ,  G06N20/00

CPC classification number: G06F16/217 ,  G06F11/3006 ,  G06F11/3433 ,  G06F16/1727 ,  G06F16/1734 ,  G06F16/182 ,  G06F16/1834 ,  G06F16/188 ,  G06N20/00

Abstract: 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.

公开(公告)号：US20220164327A1

公开(公告)日：2022-05-26

申请号：US17221755

申请日：2021-04-02

Applicant: Microsoft Technology Licensing, LLC

Inventor： Yiwen ZHU ,  Subramaniam Venkatraman KRISHNAN ,  Konstantinos KARANASOS ,  Carlo CURINO ,  Isha TARTE ,  Sudhir Darbha

IPC: G06F16/21 ,  G06F11/34 ,  G06F11/30 ,  G06N20/00

Abstract: 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.