Machine Learning with Sensitivity Analysis to Determine Key Factors Contributing to Energy Consumption in Cloud Data Centers

Machine learning (ML) approach to modeling and predicting real-world dynamic system behaviours has received widespread research interest. While ML capability in approximating any nonlinear or complex system is promising, it is often a black-box approach, which lacks the physical meanings of the actual system structure and its parameters, as well as their impacts on the system. This paper establishes a model to provide explanation on how system parameters affect its output(s), as such knowledge would lead to potential useful, interesting and novel information. The paper builds on our previous work in ML, and also combines an evolutionary artificial neural networks with sensitivity analysis to extract and validate key factors affecting the cloud data center energy performance. This provides an opportunity for software analysts to design and develop energy-aware applications and for Hadoop administrator to optimize the Hadoop infrastructure by having Big Data partitioned in bigger chunks and shortening the time to complete MapReduce jobs.