Dynamics of TCP Congestion Avoidance with Random Drop and Random Marking Queues

Abstract

Development and deployment of newer congestion feedback measures such as RED and ECN provide us a significant opportunity for modifying TCP response to congestion. Effective utilization of such opportunities requires detailed analysis of the behavior of congestion avoidance schemes with such randomized feedback mechanisms. In this dissertation, we consider the behavior of generalized TCP congestion avoidance when subject to randomized congestion feedback, such as RED and ECN. The window distribution of individual flows under a variable packet loss/marking probability is established and studied to demonstrate the desirability of specifying a less drastic reduction in the window size in response to ECN-based congestion feedback. A fixed-point based analysis is also presented to derive the mean TCP window sizes (and throughputs) and the mean queue occupancy when multiple such generalized TCP flows interact with a single bottleneck queue performing randomized congestion feedback. Recommendations on the use of memory (use of weighted averages of the past queue occupancy) and on the use of "drop biasing" (minimum separation between consecutive drops) are provided to reduce the variability of the queue occupancy. Finally, the interaction of TCP congestion avoidance with randomized feedback is related to a framework for global optimization of network costs. Such a relation is used to provide the theory behind the shape of the marking (dropping) functions used in a randomized feedback buffer.