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In this paper, we explore the idea of using traffic forecasting to improve the delay performance of a schedulebased medium access control protocol. Schedule-based channel access has been shown to utilize network and energy resources efficiently but is often hindered by the extra delay that scheduling introduces. We explore the use of traffic forecasting to anticipate transmission schedules instead of establishing them reactively, thereby reducing scheduling delays. We show the potential performance benefits traffic forecasting can bring to schedulebased medium access in the context of an existing MAC protocol called DYNAMMA [14]. Preliminary results using a machinelearning based traffic forecasting technique are also presented.

The Internet has been evolving into a more heterogeneous internetwork with diverse new applications imposing more stringent bandwidth and QoS requirements. Already new applications such as YouTube, Hulu, and Netflix are consuming a large fraction of the total bandwidth. We argue that, in order to engineer future internets such that they can adequately cater to their increasingly diverse and complex set of applications while using resources efficiently, it is critical to be able to characterize the load that emerging and future applications place on the underlying network. In this article, we investigate entropy as a metric for characterizing per-flow network traffic complexity. While previous work has analyzed aggregated network traffic, we focus on studying isolated traffic flows. Per-application flow characterization caters to the need of network control functions such as traffic scheduling and admission control at the edges of the network. Such control functions necessitate differentiating network traffic on a per-application basis. The "entropy fingerprints" that we get from our entropy estimator summarize many characteristics of each application's network traffic. Not only can we compare applications on the basis of peak entropy, but we can also categorize them based on a number of other properties of the fingerprints.

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