PhDInternet-based applications and services are pervading everyday life. Moreover, the growing
popularity of real-time, time-critical and mission-critical applications set new challenges to
the Internet community. The requirement for reducing response time, and therefore latency
control is increasingly emphasized.
This thesis seeks to reduce queueing delay through active queue management. While
mathematical studies and research simulations reveal that complex trade-off relationships
exist among performance indices such as throughput, packet loss ratio and delay, etc., this
thesis intends to find an improved active queue management algorithm which emphasizes
delay control without trading much on other performance indices such as throughput and
packet loss ratio.
The thesis observes that in TCP/IP network, packet loss ratio is a major reflection of
congestion severity or load. With a properly functioning active queue management algorithm,
traffic load will in general push the feedback system to an equilibrium point in terms of
packet loss ratio and throughput. On the other hand, queue length is a determinant factor on
system delay performance while has only a slight influence on the equilibrium. This
observation suggests the possibility of reducing delay while maintaining throughput and
packet loss ratio relatively unchanged.
The thesis also observes that queue length fluctuation is a reflection of both load changes and
natural fluctuation in arriving bit rate. Monitoring queue length fluctuation alone cannot
distinguish the difference and identify congestion status; and yet identifying this difference is
crucial in finding out situations where average queue size and hence queueing delay can be
properly controlled and reasonably reduced. However, many existing active queue
management algorithms only monitor queue length, and their control policies are solely
based on this measurement. In our studies, our novel finding is that the arriving bit rate
distribution of all sources contains information which can be a better indication of
congestion status and has a correlation with traffic burstiness. And this thesis develops a
simple and scalable way to measure its two most important characteristics, namely the mean
ii
and the variance of the arriving rate distribution. The measuring mechanism is based on a
Zombie List mechanism originally proposed and deployed in Stabilized RED to estimate the
number of flows and identify misbehaving flows. This thesis modifies the original zombie
list measuring mechanism, makes it capable of measuring additional variables. Based on
these additional measurements, this thesis proposes a novel modification to the RED
algorithm. It utilizes a robust adaptive mechanism to ensure that the system reaches proper
equilibrium operating points in terms of packet loss ratio and queueing delay under various
loads. Furthermore, it identifies different congestion status where traffic is less bursty and
adapts RED parameters in order to reduce average queue size and hence queueing delay
accordingly.
Using ns-2 simulation platform, this thesis runs simulations of a single bottleneck link
scenario which represents an important and popular application scenario such as home
access network or SoHo. Simulation results indicate that there are complex trade-off
relationships among throughput, packet loss ratio and delay; and in these relationships delay
can be substantially reduced whereas trade-offs on throughput and packet loss ratio are
negligible. Simulation results show that our proposed active queue management algorithm
can identify circumstances where traffic is less bursty and actively reduce queueing delay
with hardly noticeable sacrifice on throughput and packet loss ratio performances.
In conclusion, our novel approach enables the application of adaptive techniques to more
RED parameters including those affecting queue occupancy and hence queueing delay. The
new modification to RED algorithm is a scalable approach and does not introduce additional
protocol overhead. In general it brings the benefit of substantially reduced delay at the cost
of limited processing overhead and negligible degradation in throughput and packet loss
ratio. However, our new algorithm is only tested on responsive flows and a single bottleneck
scenario. Its effectiveness on a combination of responsive and non-responsive flows as well
as in more complicated network topology scenarios is left for future work
