The demand for graduates with exposure in Cloud Computing is on the rise. For many educational institutions, the challenge is to decide on how to incorporate appropriate cloud-based technologies into their curricula. In this paper, we describe our design and experiences of integrating Cloud Computing components into seven third/fourth-year undergraduate-level information system, computer science, and general science courses that are related to large-scale data processing and analysis at the University of Queensland, Australia. For each course, we aimed at finding the best-available and cost-effective cloud technologies that fit well in the existing curriculum. The cloud related technologies discussed in this paper include opensource distributed computing tools such as Hadoop, Mahout, and Hive, as well as cloud services such as Windows Azure and Amazon Elastic Computing Cloud (EC2). We anticipate that our experiences will prove useful and of interest to fellow academics wanting to introduce Cloud Computing modules to existing courses.

/pdf/introducing-cloud-computing-topics-in-curricula-4og8t5cm69.pdf

Introducing Cloud Computing Topics in Curricula

In today's multicore world, every CS student should learn about and gain experience with (at least) shared-memory parallelism. CS Curriculum 2013 acknowledges this by shifting parallel computing from elective status into the core. This paper argues that students should be introduced to parallelism early, that the CS2 (Data Structures) course is a natural place to do so, and reports our experience in doing this. The paper also argues that students should be taught to solve problems using parallel patterns, which are industry-standard best-practice strategies for parallel problem solving. To support such teaching, the paper presents patternlets -- minimalist, scalable, executable programs, each illustrating the behavior of a parallel pattern -- as a useful pedagogical tool for teaching parallel concepts. Several patternlets and their executions are given. The paper presents evidence that this injection of parallelism into CS2 has been successful.

Injecting parallel computing into CS2

In this work, three of many ICT-specific challenges of LCA are discussed. First, the inconsistency versus uncertainty is reviewed with regard to the meta-technological nature of ICT. As an example, the semiconductor technologies are used to highlight the complexities especially with respect to energy and water consumption. The need for specific representations and metric to separately assess products and technologies is discussed. It is highlighted that applying product-oriented approaches would result in abandoning or disfavoring of new technologies that could otherwise help toward a better world. Second, several believed-untouchable hot spots are highlighted to emphasize on their importance and footprint. The list includes, but not limited to, i) User Computer-Interfaces (UCIs), especially screens and displays, ii) Network-Computer Interlaces (NCIs), such as electronic and optical ports, and iii) electricity power interfaces. In addition, considering cross-regional social and economic impacts, and also taking into account the marketing nature of the need for many ICT's product and services in both forms of hardware and software, the complexity of End of Life (EoL) stage of ICT products, technologies, and services is explored. Finally, the impact of smart management and intelligence, and in general software, in ICT solutions and products is highlighted. In particular, it is observed that, even using the same technology, the significance of software could be highly variable depending on the level of intelligence and awareness deployed. With examples from an interconnected network of data centers managed using Dynamic Voltage and Frequency Scaling (DVFS) technology and smart cooling systems, it is shown that the unadjusted assessments could be highly uncertain, and even inconsistent, in calculating the management component's significance on the ICT impacts.

Challenges and complexities in application of LCA approaches in the case of ICT for a sustainable future

In this work, three of many ICT-specific challenges of LCA are discussed. First, the inconsistency versus uncertainty is reviewed with regard to the meta-technological nature of ICT. As an example, the semiconductor technologies are used to high- light the complexities especially with respect to energy and water consumption. The need for specific representations and metric to separately assess products and technologies is discussed. It is highlighted that applying product-oriented approaches would result in abandoning or disfavoring of new technologies that could otherwise help toward a better world. Second, several believed- untouchable hot spots are highlighted to emphasize on their importance and footprint. The list includes, but not limited to, i) User Computer-Interfaces (UCIs), especially screens and displays, ii) Network-Computer Interlaces (NCIs), such as electronic and optical ports, and iii) electricity power interfaces. In addition, considering cross-regional social and economic impacts, and also taking into account the marketing nature of the need for many ICT's product and services in both forms of hardware and soft- ware, the complexity of End of Life (EoL) stage of ICT products, technologies, and services is explored. Finally, the impact of smart management and intelligence, and in general software, in ICT solutions and products is highlighted. In particular, it is observed that, even using the same technology, the significance of software could be highly variable depending on the level of intelligence and awareness deployed. With examples from an interconnected network of data centers managed using Dynamic Voltage and Frequency Scaling (DVFS) technology and smart cooling systems, it is shown that the unadjusted assessments could be highly uncertain, and even inconsistent, in calculating the management component's significance on the ICT impacts.

pdf/challenges-and-complexities-in-application-of-lca-approaches-163db14vzc.pdf

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 cloud-based 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.

Teaching Big Data and Cloud Computing with a Physical Cluster

The reality of multicore machines as a standard and the prevalence of distributed cloud computing has signaled a need for parallel and distributed computing to become integrated into the computer science curriculum. At the same time, operating system virtualization has become a common technique with open standard tools available to any practitioners. Virtual machines (VMs) installed on available computer lab resources can be used to simulate high-performance cluster computing environments. This paper describes two such virtual clusters in use at small colleges, demonstrates their effectiveness for parallel computing education, and provides information about how to obtain the VMs for use in an educational lab setting. We have used these clusters to introduce parallelism into several courses in our undergraduate curriculum.

Virtual clusters for parallel and distributed education

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Virtual clusters for parallel and distributed education