论文标题

Teemon:T恤的连续性能监视框架

TEEMon: A continuous performance monitoring framework for TEEs

论文作者

Krahn, Robert, Dragoti, Donald, Gregor, Franz, Quoc, Do Le, Schiavoni, Valerio, Felber, Pascal, Souza, Clenimar, Brito, Andrey, Fetzer, Christof

论文摘要

值得信赖的执行环境(TEE),例如英特尔软件守卫扩展(SGX),被认为是解决云中安全挑战的有前途的方法。 TEE通过提供隔离的安全内存区域(即飞地)来保护应用程序代码和数据的机密性和完整性,甚至免受具有根和物理访问的特权攻击者。安全保证由CPU提供,因此,即使系统软件受到损害,攻击者也无法访问飞地的内容。尽管此方法可确保应用程序的强大安全保证,但它还引入了相当大的运行时开销,部分原因是受保护内存的可用性有限(Enclave Page Cache)。当前,仅存在针对TEE应用程序的有限数量的性能测量工具,并且在运行时没有提供性能监控和分析。 本文介绍了Teemon,这是第一个用于基于TEE的应用程序的连续性能监视和分析工具。 Teemon不仅在运行时提供细粒度的性能指标,还可以帮助分析识别性能瓶颈的原因,例如,系统呼叫过多。我们的方法与现有的开源工具(例如Prometheus或Grafana)顺利集成到整体监控解决方案,特别是针对通过Docker容器或Kubernetes部署的系统进行了优化,并提供了几种专用的指标和可视化。我们的评估表明,Teemon的高架从5%到17%不等。

Trusted Execution Environments (TEEs), such as Intel Software Guard eXtensions (SGX), are considered as a promising approach to resolve security challenges in clouds. TEEs protect the confidentiality and integrity of application code and data even against privileged attackers with root and physical access by providing an isolated secure memory area, i.e., enclaves. The security guarantees are provided by the CPU, thus even if system software is compromised, the attacker can never access the enclave's content. While this approach ensures strong security guarantees for applications, it also introduces a considerable runtime overhead in part by the limited availability of protected memory (enclave page cache). Currently, only a limited number of performance measurement tools for TEE-based applications exist and none offer performance monitoring and analysis during runtime. This paper presents TEEMon, the first continuous performance monitoring and analysis tool for TEE-based applications. TEEMon provides not only fine-grained performance metrics during runtime, but also assists the analysis of identifying causes of performance bottlenecks, e.g., excessive system calls. Our approach smoothly integrates with existing open-source tools (e.g., Prometheus or Grafana) towards a holistic monitoring solution, particularly optimized for systems deployed through Docker containers or Kubernetes and offers several dedicated metrics and visualizations. Our evaluation shows that TEEMon's overhead ranges from 5% to 17%.

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