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Insight from a Containerized Kubernetes Workload Introspection

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Title:Insight from a Containerized Kubernetes Workload Introspection
Authors:Watts, Thomas
Benton, Ryan
Shropshire, Jordan
Bourrie, David
Keywords:Cyber Operations, Defence, and Forensics
big data
containers
forensics
kubernetes
show 1 morespark
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Date Issued:05 Jan 2021
Abstract: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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/71457
ISBN:978-0-9981331-4-0
DOI:10.24251/HICSS.2021.836
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Cyber Operations, Defence, and Forensics


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