Dynamic correlative VM placement for quality-assured cloud service

How to increase the utilization of data center networks (DCN) is a critical problem to ensure the quality of cloud services. Previous researches showed that the key is to increase the time-average utilization and decrease the overload ratio, and proposed many efficient virtual machine (VM) placement algorithms to achieve higher utilization. However, most of those works did not consider the quality assurance and statistical multiplexing methods, which can greatly improve the effectiveness of VM placement. In this paper, we propose a novel quality-assured VM placement scheme that dynamically places VMs to better multiplex time-varying resource demands. We firstly apply AutoRegressive Integrated Moving Average (ARIMA) and Generalized AutoRegressive Conditional Heteroskedasticity (GARCH model) to forecast the trend and volatility of the future demand, and then develop a Modern Portfolio Theory (MPT)-based method to enlarge DCN utilization and hedge the risk of server overloads. Extensive simulations and detailed analysis are conducted to validate the efficiency of our proposed scheme, which outperforms the previous works greatly.