2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) (2015)
May 4, 2015 to May 7, 2015
We propose Cloud-based machine learning tools for enhanced Big Data applications, where the main idea is that of predicting the "next" workload occurring against the target Cloud infrastructure via an innovative ensemble-based approach that combine the effectiveness of different well-known classifiers in order to enhance the whole accuracy of the final classification, which is very relevant at now in the specific context of Big Data. So-called workload categorization problem plays a critical role towards improving the efficiency and the reliability of Cloud-based big data applications. Implementation-wise, our method proposes deploying Cloud entities that participate to the distributed classification approach on top of virtual machines, which represent classical "commodity" settings for Cloud-based big data applications. Preliminary experimental assessment and analysis clearly confirm the benefits deriving from our classification framework.
Hidden Markov models, Benchmark testing, Discrete cosine transforms, Big data, Virtual machining, Machine learning algorithms, Training
A. Cuzzocrea, E. Mumolo and P. Corona, "Cloud-Based Machine Learning Tools for Enhanced Big Data Applications," 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)(CCGRID), Shenzhen, China, 2015, pp. 908-914.