Self-optimizing autonomic control of geographically distributed collaboration applications

Abstract

In the past few years, cloud computing has become an integral technology both for the day to day running of corporations, as well as in everyday life as more services are offered which use a backend cloud. At the same time online collaboration tools are becoming more important as both businesses and individuals need to share information and collaborate with other entities. Previous work has presented an architecture for a collaboration online application which allows users in different locations to share videos, images and documents while at the same time video chatting. The application's servers are deployed in a cloud environment which can scale up and down based on demand. Furthermore, the design allows the application to be deployed on multiple clouds which are deployed in different geographic locations. Previous work however did not introduce how the application's up and down scaling is to be achieved. In this paper the autonomic system which manages the self-optimizing function of the cloud is presented. The autonomic system itself is a self-organizing system with a control model based on the leaky-bucket theory often used in network congestion control. A testbed for the collaboration application is used in order to gather performance metrics for the model.