Decomposition Based Cloud Resource Demand Prediction Using Extreme Learning Machines

Cloud computing has drastically transformed the means of computing in past few years. Apart from numerous advantages, it suffers with a number of issues including resource under-utilization, load balancing and power consumption. The workload prediction is being widely explored to solve these issues using time series analysis regression and neural networks based models. The time series analysis based models are unable to capture the dynamics in the workload behavior whereas neural network based models offer better accuracy on the cost of high training time. This paper presents a workload prediction model based on extreme learning machines (ELM) whose learning time is very low and forecasts the workload more accurately. The performance of the model is evaluated over two real world cloud server workloads i.e. CPU and Memory demand traces of Google cluster and compared with predictive models based on state-of-art techniques including Auto Regressive Integrated Moving Average (ARIMA), Support Vector Regression (SVR), Linear Regression (LR), Differential Evolution (DE), Blackhole Algorithm (BhA), and Propagation (BP). It is observed that the proposed model outperforms the state-of-art techniques by reducing the mean prediction error up to 100% and 99% on CPU and memory request traces respectively.