Boosted regression for predicting CPU utilization in the cloud with periodicity

Abstract

Predicting CPU usage is crucial to cloud resource management. Precise CPU prediction, however, is a tough challenge due to the variable and dynamic nature of CPUs. In this paper, we introduce TrAdaBoost.WLP, a novel regression transfer boosting method that employs Long Short-Term Memory (LSTM) networks for CPU consumption prediction. Concretely, a dedicated Periodicity-aware LSTM (PA-LSTM) model is specifically developed to take into account the use of periodically repeated patterns in time series data while making predictions. To adjust for variations in CPU demands, multiple PA-LSTMs are trained and concatenated in TrAdaBoost.WLP using a boosting mechanism. TrAdaBoost.WLP and benchmarks have been thoroughly evaluated on two datasets: 160 Microsoft Azure VMs and 8 Google cluster traces. The experimental results show that TrAdaBoost.WLP can produce promising performance, improving by 32.4% and 59.3% in terms of mean squared error compared to the standard Probabilistic LSTM and ARIMA.