Cloud Auto-Scaling With Deadline and Budget Constraints
Source: University of Virginia
Clouds have become an attractive computing platform which offers on-demand computing power and storage capacity. Its dynamic scalability enables users to quickly scale up and scale down underlying infrastructure in response to business volume, performance desire and other dynamic behaviors. However, challenges arise when considering computing instance non-deterministic acquisition time, multiple VM instance types, unique cloud billing models and user budget constraints. Planning enough computing resources for user desired performance with less cost, which can also automatically adapt to workload changes, is not a trivial problem. In this paper, the authors present a cloud auto-scaling mechanism to automatically scale computing instances based on workload information and performance desire. The mechanism schedules VM instance startup and shut-down activities.