| 摘要: |
| 边缘计算因其低延迟和高效的处理能力而被广泛应用于各个领域, 而KubeEdge平台系统作为边缘智能场景的核心基础软件, 其运行时可靠性至关重要. 然而, 边缘系统上的软件在长期运行后可能会出现软件老化问题, 导致系统响应延迟甚至服务中断, 进而影响用户体验乃至事故. 抗衰操作可以消除老化现象, 但目前针对边缘系统的老化研究相对较少, 且现有抗衰方法无法直接应用于边缘系统. 为解决上述问题, 针对KubeEdge边缘系统提出了一种称为GIP-MI的老化判定与抗衰综合方法, 该方法首先采用GCN-Informer方法对系统指标的空间关联和时序依赖进行建模, 相较于传统方法, 能够更精准、稳定地预测各指标的未来变化趋势; 进而将预测数据送入深度学习方法ParNet, 通过多时间点切片与多分辨率特征融合, 实现对系统资源动态老化状态的更精准判定; 最后, 提出一种基于分解和信息反馈模型的多目标进化算法(MOEA/D-IFM)的任务卸载方法作为抗衰策略, 有效避免系统停机, 保证服务连续性. 实验结果表明, GIP-MI在老化预测和状态识别精度上均优于基线方法, 并且与传统抗衰方法相比, 在停机时间等关键指标上均表现出显著优势, 能够有效恢复系统状态. |
| 关键词: 软件老化 边缘系统 老化预测 状态判定 抗衰策略 |
| DOI：10.13328/j.cnki.jos.007645 |
| 分类号:TP311 |
| 基金项目:国家自然科学基金(62462047); 内蒙古自然科学基金重点项目(2023ZD18) |
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| Software Aging State Determination and Rejuvenation Strategy Generation for KubeEdge Platform |
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LIU Jing, TAN Xue-Yong, TANG Zhi-Wei, NIU Chao-Yu
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College of Computer Science (College of Software) & College of Artificial Intelligence, Inner Mongolia University, Hohhot 010021, China
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| Abstract: |
| Edge computing has been widely adopted across various domains due to its low latency and high processing efficiency. As core foundational software in edge intelligence scenarios, the runtime reliability of the KubeEdge platform is critically important. However, software on edge systems may experience software aging after prolonged operation, leading to delayed system responses or even service interruptions, which may negatively impact user experience and potentially cause accidents. While rejuvenation operations can mitigate aging effects, current research on aging in edge systems remains relatively limited, and existing rejuvenation methods cannot be directly applied to edge systems. To address these challenges, this study proposes a comprehensive aging state determination and rejuvenation method named GIP-MI for the KubeEdge edge system. The method first employs the GCN-Informer approach to model spatial correlations and temporal dependencies among system metrics, enabling more accurate and stable predictions of future trends in these metrics compared to conventional methods. The predicted data is then fed into the deep learning method ParNet, which leverages multi-timepoint slicing and multi-resolution feature fusion to achieve more precise identification of dynamic aging states of system resources. Finally, a task offloading method based on the multi-objective evolutionary algorithm based on decomposition and information feedback model (MOEA/D-IFM) is introduced as a rejuvenation mechanism, effectively avoiding system downtime and ensuring service continuity. Experimental results demonstrate that GIP-MI outperforms baseline methods in both aging prediction and state identification accuracy. Moreover, compared to traditional rejuvenation approaches, it shows significant advantages in key metrics such as downtime, enabling effective recovery of system states |
| Key words: software aging edge system aging prediction state determination rejuvenation strategy |
