Adaptive virtual machine allocation algorithms for cloud-hosted elastic media services

Cloud computing is growing in adoption for different services previously supported by traditional infrastructure, including dedicated hardware setups. One of these cloud-enabled services is real-time Audio/Video collaboration. Existing cloud-based collaboration systems generally function on a best-effort basis, and offer little to no delivery and quality guarantees. High-quality business-focused solutions are therefore offered using dedicated and often manually configured hardware. In this paper, we present resource provisioning algorithms to provide the mentioned elasticity under strict quality requirements. These algorithms are evaluated, using an extended version of the CloudSim simulator, making use of realistic collaborative meeting patterns prepared to deal with seasonality and usage prediction. Our results show that the algorithms improve costs by up to 98.38% when compared with previously designed more naive approaches and with an effectiveness of 99.9% in meeting A/V collaboration setup deadlines.