Dear Prof. Anderson,
We have recently completed a work "On achievable service differentiation
for TCP flows with token bucket marking". We thought this might be
of interest to you. Currently the technical report is available
at: ftp://gaia.cs.umass.edu/pub/Sahu99-tcp-tb.ps.gz
In this, we provide a simple model to analyze the TCP throughput
when token bucket metering is used at edge-node and active queue
management is used at network core. Using this model, we provide
insights regarding what can be achieved and what cannot under such
metering scheme.
We would greatly appreciate your feedback. Below is the abstract of the
paper:
Thanks,
Sambit
Abstract
--------
The Differentiated services (diffserv) architecture has been
proposed as a scalable solution for providing service
differentiation among flows without any per-flow buffer management
inside the core of the network. It has been advocated that it is
feasible to provide service differentiation among a set of flows by
choosing an appropriate ``marking profile'' for each flow. In this
paper, we examine (i) whether it is possible to provide service
differentiation among a set of TCP flows by choosing appropriate
marking profiles for each flow, (ii) under what circumstances, the
marking profiles are able to influence the service that a TCP flow
receives, and, (iii) how to choose a correct profile to achieve a
given service level. We derive a simple, and yet accurate, analytical
model for determining the achieved rate of a TCP flow when
edge-routers use ``token bucket'' packet marking and core-routers
use active queue management for preferential packet dropping. From
our study, we observe three important results: (i) the achieved rate
is not proportional to the assured rate, (ii) it is not always
possible to achieve the assured rate and, (iii) there exist ranges
of values of the achieved rate for which token bucket parameters have no
influence. We find that it is not easy to regulate the service
level achieved by a TCP flow by solely setting the profile parameters.
In addition, we derive conditions that determine when the bucket
size influences the achieved rate, and rates that can be achieved and
those that cannot. Our study provides insight for choosing appropriate
token bucket parameters for the achievable rates.