Scalable Microservice Forensics and Stability Assessment Using Variational Autoencoders

• URL: http://arxiv.org/abs/2104.13193v1
• Date: Fri, 23 Apr 2021 18:51:41 GMT
• Title: Scalable Microservice Forensics and Stability Assessment Using Variational Autoencoders
• Authors: Prakhar Sharma, Phillip Porras, Steven Cheung, James Carpenter, Vinod Yegneswaran
• Abstract summary: We present a deep learning based approach to containerized application runtime stability analysis. The approach applies variational autoencoders (VAEs) to learn the stable patterns of container images, and then instantiates these container-specific VAEs to implement stability detection and adaptive forensics publishing. We evaluate the VAE-based stability detection technique against two attacks, CPUMiner and HTTP-flood attack, finding that it is effective in isolating both anomalies.
• Score: 6.225019476223629
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a deep learning based approach to containerized application runtime stability analysis, and an intelligent publishing algorithm that can dynamically adjust the depth of process-level forensics published to a backend incident analysis repository. The approach applies variational autoencoders (VAEs) to learn the stable runtime patterns of container images, and then instantiates these container-specific VAEs to implement stability detection and adaptive forensics publishing. In performance comparisons using a 50-instance container workload, a VAE-optimized service versus a conventional eBPF-based forensic publisher demonstrates 2 orders of magnitude (OM) CPU performance improvement, a 3 OM reduction in network transport volume, and a 4 OM reduction in Elasticsearch storage costs. We evaluate the VAE-based stability detection technique against two attacks, CPUMiner and HTTP-flood attack, finding that it is effective in isolating both anomalies. We believe this technique provides a novel approach to integrating fine-grained process monitoring and digital-forensic services into large container ecosystems that today simply cannot be monitored by conventional techniques

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