Network traffic characterization using token bucket model

This paper investigated the problem of deriving token bucket parameters from the observed traffic patterns of computer network flows in two cases. In the basic case, we identified the set of token bucket parameters for any given flow so that all the packets in the flow will be delivered immediately without incurring any delay or loss. Then, we extended the basic case by adding a queue to the token bucket model to temporarily store packets that cannot be delivered right away until enough tokens get accumulated in the bucket. The queue has the effect of smoothing the traffic so that the resulting token bucket parameters are less demanding. It also makes the derived token bucket parameters less fluctuated over time. Relationship among the queue size and token bucket parameters for a given flow are analyzed rigorously. Queuing delay for each packet in the flow is calculated and used to describe the adjusted post-queuing traffic pattern. Simple and efficient algorithms for the derivation of measurement-based traffic specification (MBTS) are presented, along with empirical results. This MBTS technology alleviates the need for users to explicitly characterize the traffic a priori in order to reserve network resource in an integrated services network.