阿奎拉:用于云原生环境的高效内核系统呼叫远程测量
Juyong Shin1, Jisu Kim1, Jaehyun Nam2
1Department of AI-Based Convergence, Dankook University, 152, Jukjeon-ro, Suji-gu, Yongin-si 16890, Republic of Korea.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
阿奎拉是一个新的系统呼叫遥测框架,通过降低开销和提高可靠性来增强云原生监控. 它为分析复杂的工作负载行为提供了更丰富的背景,克服了现有的基于eBPF的系统的局限性.
科学领域:
- 计算机科学 计算机科学
- 云计算 云计算 云计算 云计算
- 系统工程 系统工程
背景情况:
- 系统调用遥测对于理解云原生运行时行为至关重要.
- 现有的基于eBPF的监控器面临着高开支,不可靠的交付和有限的上下文信息的挑战.
- 这些局限性阻碍了可扩展性,并使复杂的工作负载活动的分析变得复杂.
研究的目的:
- 推出Aquila,这是一种新的轻量级遥测框架,旨在提高系统呼叫监控的效率,可靠性和语义忠实性.
- 解决当前基于eBPF的遥测解决方案的可扩展性和可靠性问题.
- 增强可用于云原生环境中的系统调用分析的上下文信息.
主要方法:
- 阿奎拉使用双路径内核管道将元数据与属性分开,优化事件处理.
- 实现优先级感知缓冲和明确掉落检测,以改善事件保留和过载可见性.
- 在用户空间中以Kubernetes元数据丰富内核痕迹,用于对云资源的系统调用进行语义映射.
主要成果:
- 与现有方法相比,Aquila在代表性工作负载下显示了更好的可扩展性.
- 该框架显著减少事件丢失,提高遥测流的可靠性.
- 阿奎拉为系统呼叫遥测提供了增强的语义完整性,有助于分析复杂的行为.
结论:
- 阿奎拉为云原生基础设施中的系统呼叫远程测量提供了更高效,可靠和语义丰富的解决方案.
- 该框架有效地克服了基于eBPF的现有监控工具的局限性.
- 阿奎拉在复杂的云环境中更好地理解和分析运行时行为.
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