Quality-of-service provisioning via stochastic path selection under Weibullian link delays

We study the problem of finding the most likely path satisfying a requested additive Quality-of-Service (QoS) value, such as delay. The link metrics are defined as random variables following Weibull probability distributions as empirically reported in [13] and analytically derived in [12]. The problem of finding the most likely path is NP-Hard [24]. Our approach involves reducing the complicated probability convolutions necessary to calculate the most probable path that satisfies a requested delay value. With the reduction of the objective function, an extended Bellman-Ford algorithm is devised to solve the problem. The resulting approach have the same complexity as the standard Bellman-Ford algorithm. Our reduced objective function only needs the location parameter of the Weibull distributions, hence avoiding the complexity of inferring the shape and scale parameters. We evaluate the performance of our approach by simulations and conclude with possible extensions of our work.

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