A Reinforcement Learning-based Framework for Memory Layout Optimization in Ubiquitous Operating Systems

SHA Letian; CHEN Xiao; ZHENG Hongmei; PAN Jiaye; DONG Jiankuo; XIAO Fu

doi:10.12263/DZXB.20250835

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A Reinforcement Learning-based Framework for Memory Layout Optimization in Ubiquitous Operating Systems

更新时间：2026-05-08

- A Reinforcement Learning-based Framework for Memory Layout Optimization in Ubiquitous Operating Systems
- ACTA ELECTRONICA SINICA Pages: 1-17(2026)
- 作者机构：
  
  1.南京邮电大学计算机学院、软件学院、网络空间安全学院，江苏南京 210023
  2.江苏省物联网智能感知与计算重点实验室，江苏南京 210023
  3.南京邮电大学教育科学与技术学院，江苏南京 210023
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62572255;62302238);The 2024 Frontier Technology Research and Development Program of Jiangsu(BF2024071)
- DOI：10.12263/DZXB.20250835
  CLC： TP316.4;
- Received：24 September 2025，
  
  Accepted：05 March 2026，
  
  Online First：07 May 2026，
- 稿件说明：
移动端阅览
SHA Letian, CHEN Xiao, ZHENG Hongmei, et al. A Reinforcement Learning-based Framework for Memory Layout Optimization in Ubiquitous Operating Systems[J/OL]. ACTA ELECTRONICA SINICA, 2026, 1-17.
DOI：

SHA Letian, CHEN Xiao, ZHENG Hongmei, et al. A Reinforcement Learning-based Framework for Memory Layout Optimization in Ubiquitous Operating Systems[J/OL]. ACTA ELECTRONICA SINICA, 2026, 1-17. DOI： 10.12263/DZXB.20250835.

摘要

随着人机物三元融合时代的到来，泛在操作系统（Ubiquitous Operating System，UOS）对资源受限且负载多变的边缘节点内存管理提出了弹性与自适应的严苛要求。然而，现有的内存管理策略大多依赖预设的静态规则，难以感知运行时的动态负载特征，导致系统在面对突发性、多模态任务时，常出现严重的内存碎片化与实时响应性能下降，成为制约泛在计算能力落地的关键瓶颈。针对上述挑战，本文提出一种基于深度强化学习的内存布局优化框架AIMO。该框架旨在通过“感知-决策-执行”的闭环机制，实现对内存资源的智能动态管理。首先，多维状态感知模块通过处理器性能监控单元（Performance Monitoring Unit，PMU）和内核插桩，实时捕捉硬件层指标（如缓存未命中率、介质损耗）、对象层行为（如访问频率、冷热度）以及系统层上下文（如内存碎片率、应用场景类型），并将其编码为高维状态向量。其次，强化学习智能决策模块将内存布局问题建模为马尔可夫决策过程（Markov Decision Process，MDP），利用近端策略优化（Proximal Policy Optimization，PPO）算法在线生成最优决策。为了兼顾嵌入式环境的资源约束，该模块采用了“离线预训练与轻量化在线微调”的策略，在保证决策确定性的同时大幅降低了计算开销。最后，策略执行模块通过在内核态设置轻量级钩子函数，实现了分配拦截、对象重定位、策略驱动的对象放置及主动碎片整理等机制，完成了对内存布局的闭环自适应控制。本文在OpenHarmony内核中实现了AIMO框架，并基于Raspberry Pi Zero 2W平台进行了实证评估。实验结果表明，与实时性能标杆TLSF相比，AIMO在维持同等水平最坏情况分配时间的同时，将大块内存分配失败率从3.4%显著降低至0.9%，最大可用连续空闲块从318 KB提升至476 KB，并使上层业务的预加载延迟减少了15.8%。此外，该框架表现出极高的轻量化特性，其决策模块的平均CPU占用率仅为1.9%，元数据存储开销相对于512 MB系统总内存占比不足0.001%。本研究证实了强化学习在操作系统内核内存管理中的有效性，为构建高效、自适应的泛在操作系统内核提供了新思路。

Abstract

With the advent of the era of human-cyber-physical ternary integration

ubiquitous operating systems (UOS) impose stringent requirements for elasticity and adaptability on memory management within resource-constrained edge nodes handling variable workloads. However

most existing memory management strategies rely on predefined static rules and struggle to perceive dynamic workload characteristics at runtime. Consequently

when facing bursty and multi-modal tasks

systems often suffer from severe memory fragmentation and degraded real-time response performance

which has become a critical bottleneck restricting the practical deployment of ubiquitous computing capabilities. To address these challenges

this paper proposes AIMO

a memory layout optimization framework based on deep reinforcement learning. This framework aims to achieve intelligent and dynamic management of memory resources through a closed-loop mechanism of “perception-decision-execution”. First

a multi-dimensional state perception module captures hardware-level metrics (e.g.

cache miss rate

media wear)

object-level behaviors (e.g.

access frequency

coldness/hotness)

and system-level context (e.g.

memory fragmentation rate

application scenario types) in real-time via the processor’s performance monitoring unit (PMU) and kernel instrumentation

encoding them into high-dimensional state vectors. Second

a reinforcement learning intelligent decision module models the memory layout problem as a markov decision process (MDP) and utilizes the proximal policy optimization (PPO) algorithm to generate optimal decisions online. To accommodate the resource constraints of embedded environments

this module adopts a strategy of “offline pre-training and lightweight online fine-tuning”

which significantly reduces computational overhead while ensuring decision determinism. Finally

a policy execution module implements mechanisms such as allocation interception

object relocation

policy-driven object placement

and proactive defragmentation by establishing lightweight hook functions in the kernel space

thereby completing the closed-loop adaptive control of the memory layout. We implemented the AIMO framework in the OpenHarmony kernel and conducted an empirical evaluation based on the Raspberry Pi Zero 2W platform. Experimental results demonstrate that

compared to the real-time performance benchmark TLSF

AIMO significantly reduces the large-block memory allocation failure rate from 3.4% to 0.9% and increases the maximum available contiguous free block from 318 KB to 476 KB

while maintaining an equivalent worst-case allocation time. Furthermore

it reduces the preloading latency of upper-layer applications by 15.8%. In addition

the framework exhibits a highly lightweight nature; the average CPU utilization of its decision module is only 1.9%

and the metadata storage overhead accounts for less than 0.001% of the 512 MB total system memory. This study verifies the effectiveness of reinforcement learning in operating system kernel memory management

providing a novel approach for constructing efficient and adaptive UOS kernels.

关键词

Keywords

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