Assessment of machine learning algorithms in cloud computing frameworks
Kevin Li, Cynthia Gibson, Daniel E. Ho, Qi Zhou, Jong Kyong Kim, O. Buhisi, Donald E. Brown, Matthew S. Gerber · 2013
In the past decade, digitization of information has led to a data explosion in both volume and complexity. While traditional computing frameworks have failed to provide adequate computing power for the now common data-intensive computing tasks, cloud computing provides an effective alternative to enhance computing power. Machine learning algorithms are powerful analytical methods that allow machines to recognize patterns and facilitate human learning. However, the performance of individual machine learning algorithms within each cloud computing framework remains largely unknown. Furthermore, the lack of a robust selection methodology matching input data with effective machine learning algorithms limits the ability of practitioners to make effective use of cloud computing. This research compares various machine learning algorithms on the widely adopted Apache Mahout framework and the recently introduced GraphLab framework. Whereas previous work has examined the computational architectures of various cloud computing frameworks, this work focuses on a problem-based approach to architecture selection. The experimental results demonstrate that GraphLab generally outperforms Mahout with respect to runtime, scalability, and usability. However, Mahout outperforms GraphLab when the experiment focus shifts to error measurement.