A Distributed Environment Decision Maker Based on Machine Learning Techniques

There are many computational options nowadays, capable to serve properly a wide range of users. Web Services, Cloud Computing, Internet of Things among others are some of the ways technology can be offered. To scientific research this variety is fundamental. One of the major concerns in distributed environments such as cloud computing is the size of data and how to handle it's transport. In some cases, it is necessary to save information in hard drives and send it to other locations. The experiments in Protein Structure Predictions (PSP) usually results in very large amounts of data that are difficult to move from one place to another. Besides that, those results have different computational costs, processing time and price range. The current state-of-art shows the limitations the researchers have to face when working with local computers and most of their work is focused on improve the PSP algorithms. We understand that the historic runs in such systems are a reliable source to be studied and we propose a application of machine learning techniques that can be used as a base for a decision maker capable of defining machine configurations and a proper set-up, while keeping Quality of Service (QoS). The results of the kNN algorithm show a strategy capable of predicting with good accuracy the outcomes such as processing time and resulting file sizes. This defines the base for the decision maker mechanism.