Cloud auto-scaling with deadline and budget constraints (Grid 2010)


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, we present a cloud auto-scaling mechanism to automatically scale computing instances based on workload information and performance desire. Our mechanism schedules VM instance startup and shut-down activities. It enables cloud applications to finish submitted jobs within the deadline by controlling underlying instance numbers and reduces user cost by choosing appropriate instance types. We have implemented our mechanism in Windows Azure platform, and evaluated it using both simulations and a real scientific cloud application. Results show that our cloud auto-scaling mechanism can meet user specified performance goal with less cost.

Proceedings of the 11th IEEE/ACM International Conference on Grid Computing (Grid 2010). Oct 25-28, 2010. Brussels, Belgium.