Lock strategy adjustment method based on lock scheduling condition and terminal

By predicting the contention degree of locks in real time and adjusting the lock strategy dynamically, the lock competition problem of lock scheduling and concurrent control in the Linux kernel is solved, and the balance between high performance and fairness is achieved, and the system stability and responsiveness are improved.

CN119645674APending Publication Date: 2025-03-18CONTEMPORARY NEBULA TECH ENERGY CO LTD
0 Cites 1 Cited by

Patent Information

Application Number
CN202411509586.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-07-24
Publication Date
2025-03-18

AI Technical Summary

Technical Problem

Lock scheduling and concurrent control in the existing Linux kernel have problems such as CPU idle caused by lock competition, long resource waiting time, and the difficulty of traditional static strategies to adapt to dynamically changing workloads.

Method used

By acquiring historical system running data, the machine learning model is trained to predict the contention degree of locks in real time, and dynamically adjust the lock's scheduling strategy based on the prediction results, including adjusting the spin number of spins of the spin lock, the priority strategy of the read and write lock, and the spin number of the mutex lock or converting it into a blocking lock.

Benefits of technology

The lock scheduling strategy is optimized, unnecessary spin and context switching is reduced, CPU usage efficiency is improved, and the overall stability and response ability of the system are improved.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention relates to the technical field of lock scheduling prediction, in particular to a lock scheduling condition-based lock strategy adjustment method and a terminal, and the method comprises the steps of obtaining historically collected system operation data which comprises lock scheduling data, system load data and system scheduling overhead data; training a machine learning model according to the historically collected system operation data; predicting the contention degree of the lock in real time according to the trained machine learning model to obtain a prediction result; adjusting a scheduling strategy of the lock according to the prediction result; according to the technical scheme, the contention degree of the lock can be intelligently predicted, then the lock strategy is dynamically adjusted, the balance between high performance and fairness is achieved, the optimization of the lock scheduling strategy is achieved, unnecessary spinning and context switching can be reduced, the CPU use efficiency is improved, and therefore the overall stability and response capacity of the system are improved.
Need to check novelty before this filing date? Find Prior Art