FAMA: A Middleware for Fast Deploying and Auto Scaling towards Multitier Applications in Clouds

Abstract

As the scale of Internet applications increases, the structure and technology used in backend application servers keep on improving. Facing multitier applications with large workload, how to deploy them rapidly and maintain them effectively become challenges. The traditional solution widely adopted is to make up a large physical cluster of homogeneous servers to combine individual computational capability to improve application performance. However, this way has little flexibility for dynamical load, and finally leads to low recourse utilization and delayed reaction. Relatively, though cloud computing provides a more flexibility and scalability model, traditional cloud is still not convenient and intelligent enough to deploy and maintain applications. We present a fast-deploying and auto-scaling middleware named FAMA towards multitier applications in cloud. FAMA solves the fast-deploying problem on one hand, and provides an open and flexible mechanism to scale applications automatically to meet load peak on the other hand. We implement the prototype and combine it within OpenNebula for evaluation. Our work shows that FAMA can deploy and scale applications in a very short time. Comparing to original IaaS platforms, it shortens the VM-creation to only few seconds. Through compatible with users' own monitoring mechanism, FAMA has improved flexibility and scalability.