Adaptive workload prediction for cloud-based server infrastructures

Currently,data centers offer cloud computing platforms relying on virtualization technology and multi-tier architecture to handle an ever increasing scale and to provide elastic service. however,in order to achieve elasticity,efficient prediction is needed to control virtual machines. we present a new adaptive linear auto regressive technique for web server workload prediction with feedback loop control. we test the adaptive-feedback ar model with the songkhla rajabhat university's academic web which has a similar daily pattern of workloads and the learning management system (lms) web which has unpredictable workloads. for the 1-minute interval,the suitable result for controlling the ar orders is in the range of 2-8 and previous historical value is in range of 10-25. our new prediction approach predicts both web workloads with a root mean square error (rmse) below 0.6,of which quality is better,in terms of the prediction accuracy resulting in a better performance.