Teaching Scalability, Fault Tolerance, and Performance in the Cloud: A Practical Laboratory Exercise

Designing practical exercises to aid in teaching nonfunctional properties of distributed and cloud systems, like scalability, fault tolerance, and performance is hard, as these properties emerge as a result of complex system designs and interactions. In this paper, we describe a hands-on laboratory exercise that can be used as part of a Distributed Systems or Cloud Computing class to help students understand the effect of replication in scalability, fault tolerance and performance of a Big Data platform running on a public cloud provider. Preliminary results indicate that the students like the activity and that working on it improves their understanding of the concepts mentioned above. We have released our laboratory guide on GitHub so that other instructors can use it in their classes: https://github.com/marcelleonlafebre/lab_manualcloud/blob/main/non_functional_demo_lab.md.