Distributed platforms are now a de facto standard in modern software and application development. Although the ACM/IEEE Curriculum 2013 introduces Parallel and Distributed Computing as a first class knowledge area for the first time, the right level of abstraction to teach these concepts is still an important question that needs to be explored. This work presents our findings in teaching cloud computing by exposing upper-level students to testbeds in use by the distributed systems research community. The possibility of giving students practical and relevant experience was explored in the context of new course assignment objectives. Furthermore, students were able to significantly contribute to a pilot class project with medium-scale computation based on satellite data. However, the software engineering challenges in these environments proved to be daunting. In particular, these challenges were exacerbated by a lack of debugging support relative to the environments students were more familiar with-requiring development practices that out-stripped typical course experiences. Our proposed set of experiments and project provide a basis for an evaluation of the trade-offs of teaching cloud and distributed systems on the wild side. We hope that these findings provide insight into some of the possibilities to consider when preparing the next generation of computer scientists to engage with software practices and paradigms that are already fundamental in today's highly distributed systems.

No abstract

This paper describes our ongoing effort to develop an efficient and scalable infrastructure to model and simulate near-field tsunamis in order to develop site-specific impact scenarios (e.g. the expected effects of the tsunami on land). Our goal is to be able to leverage this infrastructure in order to provide the necessary information to assist emergency planning, notification and response programs. Our primary focus is to produce the scenario information quickly by efficiently utilizing the available hardware, providing as much lead-time as possible for the response systems. We have been able to show that a distributed computing approach allows for parallel computation of simulations, resulting in shorter times between the occurrence of an event and the simulated output. Included in this paper is our proposed computational model, a summary of our infrastructure and an evaluation of our proposed distributed framework.