LAUSR

Over the last few years, videoconferencing systems have experienced several changes that enable videoconferencing applications in personal devices to a high number of users. To efficiently attend this high and variable demand, deploying distributed videoconferencing servers in cloud-based infrastructures is highly recommended. However, videoconferencing systems have particular characteristics that impede the application of regular resource scheduling solutions used in other kinds of distributed environments. In this paper, we propose a methodology to design and evaluate scheduling strategies adapted to the necessities of each specific scenario. It involves using a new metric to estimate the resource consumption of each connection and provides a set of coefficients to evaluate the efficiency of the strategy. We test the methodology in a real setup and compare the behavior and performance of three scheduling algorithms. The conclusion is that the proposed methodology allows us to configure decision policies adapted to the requirements and necessities of a range of use cases. Thereby, we achieve a more efficient way of using cloud resources, improving the service performance and saving costs.