An Artificial Neural Network Approach to Power Consumption Model Construction for Servers in Cloud Data Centers

Abstract

The power consumption estimation or prediction of cloud servers is the basis of energy-aware scheduling to realize energy saving in cloud datacenters. The existing works are mainly based on the static mathematical formulas which establish the relationship between the server power consumption and the system performance. However, these models are weak in adaptability and generalization ability, not adaptable to the changes and fluctuation of different workload, and demanding on the clear and profound understanding of the inner relationship among related power consumption parameters. Therefore, we propose the ANN (Artificial Neural Network) method to model the power consumption of the servers in datacenters, a kind of end-to-end black box model. We performed a fine-grained and in-depth analysis about the system performance and power consumption characteristics of the CPU, memory, and disk of the server running different types of task loads, and selected a set of performance counters that can fully reflect the status of system power consumption as the input of the model. Then, we establish power consumption models based on BP neural network, Elman neural network, and LSTM neural network, respectively. In order to get a better result, we use data collected from four different types of task loads (i.e., CPU-intensive, memory-intensive, I/O-intensive, and mixed load) to train, validate, and test our target models. The experimental results show that, compared with multiple linear regression and support vector regression, the proposed three power models have better performance in predicting the server's real-time power consumption.