Adaptive Optimal Scheduling Method for Mobile Terminal Software Based on Deep Reinforcement Learning

Abstract

An adaptive optimization scheduling method for mobile terminal software based on deep reinforcement learning, involving computing technology. Automatically optimize the number of unloaded software processes on mobile devices, and prioritize local task processes, allocate CPU computing resources and memory resources, aiming to reduce energy consumption of mobile devices and task processing delays. By measuring the scale of real-time thread tasks of each software on the mobile device, estimating the bandwidth of the dynamic wireless link from the mobile device to the edge device, and using deep reinforcement learning algorithms to evaluate the feedback information such as delay and energy consumption of each process, and obtain software optimization Scheduling scheme. It does not need to predict the wireless channel model from the mobile device to the edge device and the CPU computing resource and memory resource occupancy model of the mobile device system, which can reduce the processing delay and energy consumption of each software task on the mobile device and improve the user experience.

Description

technical field [0001] The invention relates to computing technology, in particular to a mobile terminal software adaptive optimization scheduling method based on deep reinforcement learning. Background technique [0002] With the development and innovation of various application software, mobile devices such as smart phones need to handle larger and larger computing tasks, and the computing power requirements for mobile device CPUs are getting higher and higher. Poor CPU performance or unreasonable calculation order of mobile devices will lead to unsmooth operation of application software, such as game freezes, etc., affecting user experience. Coordinating task processes through system-level optimization schemes under fixed and mobile device hardware conditions is of great significance for reducing task processing delays and improving user experience. [0003] In this regard, A.S.Wu et al. (A.S.Wu, H.Yu, S.Jin, et al, "An incremental geneticgorithm approach to multiprocess...

AI Technical Summary

Problems solved by technology

However, mobile devices usually must be connected to the edge computing network through a wireless network. The instability of the wireless channel has a great impact on the effect of the edge computing network. When the communication channel quality is poor, the mobile device takes longer to unload, even exceeding Edge computing reduces computing delay, and at the same time, unloading data adds additional antenna transmission power loss to mobile devices