Traditional infrastructure models are being replaced by Cloud-enabled models with support for several different applications/services. One of these services is professional real-time Audio/Video collaboration. Although high-end business collaboration solutions are nowadays offered with high reliability, they rely on dedicated specialized (and costly) hardware and network infrastructure setups, and usually prove difficult to scale up. On the other hand, existing cloud-based A/V collaboration solutions do allow for easier elasticity and scalability, but do so on a best-effort basis with no guarantees for reliability or quality. In this context, this paper proposes cloud-enabled A/V collaboration resource allocation algorithms focused on solving the business requirements and SLAs dealing with elasticity and scalability. The proposed algorithms attempt to minimise Cloud resource usage, in order to keep the cost within budget, while striving for the reliability and quality of dedicated hardware solutions. The evaluation is conducted using a version of the CloudSim simulator that has been extended to support collaborative meeting patterns, usage seasonality, resource expenditure prediction, network congestion and streaming multicast. Compared to the second best solution applied to the same evaluated scenario, our results show a reduction of bandwidth usage and virtual machine costs by 49% and 63%, respectively, and an effectiveness of 99% in meeting A/V collaboration setup deadlines.
['Rafael Albuquerque Xavier', 'Hendrik Moens', 'Bruno Volckaert', 'Filip De Turck']
Resource Allocation Algorithms for Multicast Streaming in Elastic Cloud-Based Media Collaboration Services