CRDCPFDec 11, 2020

TEEMon: A continuous performance monitoring framework for TEEs

arXiv:2012.06554v13 citationsHas Code
AI Analysis

This work addresses the lack of continuous performance monitoring for TEE-based applications, which is a problem for developers and operators managing secure cloud environments.

TEEMon is the first continuous performance monitoring and analysis tool for Trusted Execution Environment (TEE) applications, providing fine-grained runtime metrics and assisting in identifying performance bottlenecks like excessive system calls. The tool integrates with existing open-source solutions and introduces an overhead ranging from 5% to 17%.

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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