Selecting optimum cloud availability zones by learning user satisfaction levels

Cloud service providers enable enterprises with the ability to place their business applications into availability zones across multiple locations worldwide. While this capability helps achieve higher availability with smaller failure rates, business applications deployed across these independent zones may experience different quality of service (QoS) due to heterogeneous physical infrastructures. Since the perceived QoS against specific requirements are not usually advertised by cloud providers, selecting an availability zone that would best satisfy the user requirements is a challenge. In this paper, we introduce a predictive approach to identify the cloud availability zone that maximizes satisfaction of an incoming request against a set of requirements. The prediction models are built from historical usage data for each availability zone and are updated as the nature of the zones and requests change. Simulation results show that our method successfully predicts the unpublished zone behavior from historical data and identifies the availability zone that maximizes user satisfaction against specific requirements.