Towards an analysis of traffic shaping and policing in fog networks using stochastic fluid models

This paper gives models and analytic techniques for studying
shaping and policing data traffic in fog networks. The traffic
in these networks is expected to be highly diverse and bursty,
and regulation will be required as an integral part of congestion
control. We generalize the Leaky Bucket model to shape
and police traffic source for rate-based congestion control in
high-speed fog networks. In particular, the Markov modulated
fluid sources reflect the bursty characteristics of data
traffic. To measure the performance of the model in shaping
and policing traffic, we derive four performance metrics. The
experimental results show that with proper design the Leaky
Bucket model effectively controls a 4-way trade-off between
throughput, loss probability, delay and burstiness of data
traffic. Numerical results also reveal that the model performance
is sensitive to certain traffic source characteristics.