Abstract:
Cloud users must monitor software running on containers and virtual machines (VMs) to ensure compliance, security, and efficiency in today’s rapidly evolving cloud landscape that embraces continuous integration and delivery.
Traditional solutions use manually-created rules to identify software installations and modifications, but these require expert authors and are often unmaintainable. Software discovery is now automated. Some methods use software examples to train machine learning models to predict which software is installed.
Others use packaging practices to aid discovery without pre-training, but they cannot provide precise enough information to do so. Praxi, a new software discovery method, combines learning-based accuracy with practice-based efficiency.
Praxi correctly classifies installations at least 97.6% of the time in real-world cloud system samples, running 14.8 times faster and using 87% less disk space than a similar learning-based method. This article quantitatively compares Praxi to systematic rule-, learning-, and practice-based methods and discusses their best uses using a diverse software dataset.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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