Virtual partitioning for robust resource sharing: computational techniques for heterogeneous traffic

We consider virtual partitioning (VP), which is a scheme for sharing a resource among several traffic classes in an efficient, fair, and robust manner. In the preliminary design stage, each traffic class is allocated a nominal capacity, which is based on expected offered traffic and required quality of service. During operations, if the current capacity usage by a class exceeds its nominal allocation, then it is declared to be in "overload," and a state-dependent trunk reservation mechanism gives it lower priority in the admission of new calls. We develop efficient computational algorithms for the case of heterogeneous traffic, and perform extensive numerical experiments to demonstrate the accuracy of the various approximations. The performance of VP is examined and compared to that of complete sharing and complete partitioning. Particular weight is placed on robustness, meaning that traffic classes with arrival rates conforming to the design continue to receive the required quality of service, despite the presence of misbehaving classes with excessive arrival rates. We adopt a reward-penalty paradigm as a combined measure for efficiency and fairness, and show that not only is the revenue generated by VP extremely close to the maximum achievable value, but that the structural form of the optimal policy also closely resembles that of VP. The numerical results confirm that the scheme is efficient, fair, and very robust.