The tool calculates the distribution of the cumulative reward in the
following cases: (1) when the random variable
is not bounded, (2) when
the random variable is bounded by
and
.
One measure that can be obtained with this algorithm is the transient queue
length distribution (and from that, the packet loss ratio as a function of
time). Let be the number of packets stored in a limited buffer and
the process that models the traffic source (Markov reward
model). It is not difficult to see that if
is the channel capacity and if we
associate to state
a rate reward
, then the random
variable
is equal to the buffer size at
provided that
is
limited between
and the maximum buffer size
.
The interface for this method is presented in
Figure .
The input parameters: , , , and Precision
have to be specified as in the Uniformization technique. To choose the Reward Name, the user have to click on the little button on the right hand
side of the box with the Reward Name. Then, another window with the name
of all rewards, specified by the user will appear and the user will be able to
select one of them. The probabilities will be computed for the Reward
Levels given. For example, if we give as reward levels and
, the
tool outputs will be
and
plus the
probabilities calculated for the lower and upper bound, provided that bounds are
given for the reward
.
Guilherme Dutra Gonzaga Jaime 2010-10-27