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Developments in virtual containers, especially in the cloud infrastructure, have led to diversification of jobs that containers are being used to support, particularly in the big data and machine learning spaces. The diversification has been powered by the adoption of orchestration systems that marshal fleets of containers to accomplish complex programming tasks. The additional components in the vertical technology stack, plus the continued horizontal scaling have led to questions regarding how to forensically analyze complicated technology stacks. This paper proposed a solution through the use of introspection. An exploratory case study has been conducted on a bare-metal cloud that utilizes Kubernetes, the introspection tool Prometheus, and Apache Spark. The contribution of this research is two-fold. First, it provides empirical support that introspection tools can acquire forensically viable data from different levels of a technology stack. Second, it provides the ground work for comparisons between different virtual container platforms. 

Large-scale adoption of virtual containers has stimulated concerns by practitioners and academics about the viability of data acquisition and reliability due to the decreasing window to gather relevant data points. These concerns prompted the idea that introspection tools, which are able to acquire data from a system as it is running, can be utilized as both an early warning system to protect that system and as a data capture system that collects data that would be valuable from a digital forensic perspective. An exploratory case study was conducted utilizing a Docker engine and Prometheus as the introspection tool. The research contribution of this research is two-fold. First, it provides empirical support for the idea that introspection tools can be utilized to ascertain differences between pristine and infected containers. Second, it provides the ground work for future research conducting an analysis of large-scale containerized applications in a virtual cloud.