Teaching Big Data and Cloud Computing with a Physical Cluster

Cloud Computing and Big Data continue to be disruptive forces in computing and have made inroads in the Computer Science curriculum, with courses in Cloud Computing and Big Data being routinely offered at the graduate and undergraduate level. One major challenge in offering courses in Big Data and Cloud Computing is resources. The question is how to provide students with authentic experiences making use of current Cloud and Big Data resources and tools and do so in a cost effective manner. Historically, three options, namely physical clusters, virtual clusters and cloud-based clusters, have been used to support Big Data and Cloud Computing courses. Virtual clusters and cloudbased options are those that institutions have typically adopted and many arguments in favor of these options exist in the literature, citing cost and performance. Here we argue that teaching Big Data and Cloud Computing courses can be done making use of a physical cluster and that many of the existing arguments fail to take into account many important factors in their calculations. These factors include the flexibility and control of a physical cluster in responding to changes in industry, the ability to work with much larger datasets, and the synergy and broad applicability of an appropriately equipped physical cluster for courses such as Cloud Computing, Big Data and Data Mining. We present three possible configurations of a physical cluster which span the spectrum in terms of cost and provide cost comparisons of these configurations against virtual and cloud-based options, taking into account the unique requirements of an academic setting. While limitations do exist with a physical cluster and it is not an option for all situations, our analysis and experience indicates that there is great value in using a physical cluster to support teaching Cloud Computing and Big Data courses and it should not be dismissed.