Teaching Big Data and Cloud Computing with a Physical Cluster
Teaching Big Data and Cloud Computing with a Physical Cluster
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使用物理集群教授大数据和云计算
DOI：
10.1145/3017680.3017705
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Sharad Shrestha
中科院分区：
文献类型：
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作者：
Jesse Eickholt;Sharad Shrestha
云计算和大数据仍然是计算领域的颠覆性力量，并在计算机科学课程中取得了进展，研究生和本科生定期提供云计算和大数据课程。提供大数据和云计算课程的一个主要挑战是资源。问题是如何利用当前的云和大数据资源和工具为学生提供真实的体验，并以具有成本效益的方式这样做。从历史上看，三个选项，即物理集群，虚拟集群和基于云的集群，已被用于支持大数据和云计算课程。虚拟集群和基于云的选项是机构通常采用的选项，文献中存在许多支持这些选项的论点，引用成本和性能。在这里，我们认为大数据和云计算课程的教学可以利用物理集群来完成，并且许多现有的论点在计算中没有考虑到许多重要因素。这些因素包括物理集群在响应行业变化方面的灵活性和控制能力，处理更大数据集的能力，以及适当配备的物理集群的协同作用和广泛适用性，如云计算，大数据和数据挖掘。我们提出了三种可能的配置的物理集群跨越频谱的成本和提供这些配置的成本比较对虚拟和基于云的选项，考虑到学术环境的独特要求。虽然物理集群确实存在局限性，并且它不是所有情况下的选择，但我们的分析和经验表明，使用物理集群来支持云计算和大数据课程的教学具有很大的价值，不应被忽视。
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.