Praxi: Cloud software discovery that learns from practice

Abstract

With today’s rapidly-evolving cloud software landscape, users of cloud systems must constantly monitor software running on their containers and virtual machines (VMs) to ensure compliance, security, and efficiency. Traditional solutions to this problem rely on manually-created rules that identify software installations and modifications, but these require expert authors and are often unmaintainable. More recent automated techniques leverage knowledge of packaging practices to aid in discovery without requiring any pre-training, but these practice-based methods cannot provide precise-enough information to perform discovery by themselves. Other approaches use machine learning models to facilitate discovery of software present in a training corpus, but prior approaches have high runtime and storage requirements. This demonstration features Praxi, a new software discovery method that builds upon the strengths of prior approaches by combining the accuracy of learning-based methods with the efficiency of practice-based methods. We demonstrate Praxi’s training and detection process in real time while allowing laptop-equipped participants to follow along using a provided remote virtual machine.