Handling Multiple Bottlenecks, Service Differentiation, and Demand Uncertainty in Media on Demand Infrastructures

This paper introduces admission control models for accepting or denying requests to differentiated media streaming services to avoid overload and to use multiple scarce server resources efficiently. The decision models are based on deterministic and stochastic mathematical programming techniques and use a number of heuristics to determine model parameters at run-time. In contrast to existing admission control approaches, the models address the problem of allocating multiple scarce resources, allow for fine grained service differentiation, and for adaptive admission control as demand forecasts are updated on every incoming service request and feedback is used regarding current resource utilization levels to dynamically adjust admission control policies. Experiments are described to illustrate the benefits of using the adaptive admission control models over static admission control rules.