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Cloud Resource Allocation Algorithms for Elastic Media Collaboration Flows

Real-time Audio/Video (A/V) collaboration is an application domain for which Cloud adoption is being investigated. However, the professional market generally prefers reliability over elasticity / scalability, in terms of managed delay and quality, and hence usually relies on dedicated specialized hardware and network setups, which are costly and hard to scale. This paper proposes cloud-enabled A/V resource allocation algorithms that combine elasticity and scalability with a focus on solving professional SLA requirements. The proposed algorithms attempt to minimize cloud cost and network usage by intelligently provisioning service endpoints along distributed data centers and splitting A/V streaming content in selected endpoints, while striving for the measurable and controlled reliability offered by dedicated solutions. An extended version of the CloudSim simulator, developed to generate and simulate A/V collaboration patterns while collecting statistics about resource usage and cost, network congestion and delay, among others, was used for evaluation purposes. When comparing results between the most efficient proposed approach and the second one, bandwidth usage and virtual machine costs are reduced by 60% and 65%, respectively, while maintaining industry-accepted SLA compliance levels.