In today’s Internet, demand is increasing for guarantees of speed and efficiency. Current routers are very limited in the type and quantity of observed data they can provide, making it difficult for providers to maximize utilization without the risk of degraded throughput. This research uses statistical data currents provided by router vendors to estimate the impact of changes in network configuration on the probability of link overflow. This allows service providers to calculate in advance, the effect of grooming on a network, eliminating the conservative trial-and-error approach normally used. These predictions are made using Large Deviation Theory, which focuses on the tails of the distribution, giving a better estimate than average and peak values.
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