Maximizing Utilization in Private IaaS Clouds with Heterogenous Load through Time Series Forecasting

Abstract—This document presents ongoing work on creating a computing system that can run two types of workloads on a private cloud computing cluster, namely web servers and batch computing jobs, in a way that would maximize utilization of the computing infrastructure. To this end, a queue engine called Cloud Gunther has been developed. This application improves upon current practices of running batch computations in the cloud by integrating control of virtual machine provisioning within the job scheduler. For managing web server workloads, we present ScaleGuru, which has been modeled after Amazon Auto Scaler for easier transition from public to private cloud. Both these tools are tested to run over the Eucalyptus cloud system. Further research has been done in the area of Time Series Forecasting, which enables to predict the load of a system based on past observations. Due to the periodic nature of the interactive load, predictions can be made in the horizon of days with reasonable accuracy. Two forecasting models (Holt-Winters exponential smoothing and Box-Jenkins autoregressive) have been studied and evaluated on six server load time series. The autoscaler and queue engine are not yet integrated. Meanwhile, the prediction can be used to decide how many servers to turn off at night or as an internal component for the autoscaling system