Contents Telektronikk - Telenor

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Number of Type 2 Sources
Number of Type 2 Sources
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Peak Cell Rate Alloc.
Convolution
Measurement
CLR objective: 10-4 0
0 1 2 3 4 5 6 7 8
Number of Type 1 Sources
Figure 11 Boundaries for CLR = 10 -4
Peak Cell Rate Alloc.
Convolution
Measurement
CLR objective: 10-4 0
0 1 2 3 4 5 6 7 8
Number of Type 1 Sources
Figure 12 Boundaries for CLR = 10 -6
when a significant portion of the traffic
consists of type 2 sources. It is also evident
that a smaller CLR objective
reduces the achievable multiplexing gain.
The comparison of the convolution
boundaries with the boundary points
found by measurement confirms the ability
of the CAC scheme to maintain CLR
objectives while allowing to exploit a
significant part of the achievable multiplexing
gain. It must be noted, however,
that the source characteristics were not
yet changed to stress the multiplexer,
which is investigated in the next section.
4 Experiments on robustness
of the UPC and
CAC framework
While UPC and CAC functions have
been studied separately in the previous
sections, we now focus on their co-operation.
The aim is to identify whether this
traffic control framework is able to maintain
network performance guarantees
under various network conditions and
load scenarios.
The experiment configuration is almost
the same as for the CAC experiments
(see Figure 8). The major difference is
that all traffic now passes the UPC function
before being multiplexed. For each
source a traffic contract based on its statistical
parameters is established and
enforced during the measurement. The
number of established connections is
determined by the CAC function applying
the convolution algorithm. The convolution
is based on the two UPC parameters,
PCR and SCR, and not on the statistical
parameters of the sources. While
for the PCR there is no difference
between these two methods describing a
source, the SCR is always larger than the
Mean Cell Rate (MCR). The ratio
SCR/MCR depends on the characteristics
of the source and can be rather large. The
used configuration allows to measure the
cell loss ratios at both the UPC (called
Cell Discard Ratio, CDR) and at the multiplexer
(Cell Multiplexing loss Ratio,
CMR). The CDR and the CMR can be
combined to obtain the overall Cell Loss
Ratio (CLR) for each traffic source. We
investigate traffic mixes on the admission
boundary such that the highest possible
values for the CMR may be achieved,
since the CMR increases monotonously
with the number of multiplexed sources.
The robustness of the traffic control
framework is now investigated by introducing
different kinds of changes in the
characteristics of a major part of the
sources. However, the remaining sources
will not be changed to be able to see if
their perceived network performance
parameters are influenced by the other
traffic.
In a first series of measurements we have
investigated the robustness with respect
to changes in the mean cell rate. For
these experiments we have used sources
according to traffic type 3 (see Table 1).
These sources still have an ON/OFF
characteristic but state sojourn times are
distributed according to an Erlang-10 distribution
in order to be able to fix the
SCR closer to the MCR. The UPC
parameters in the traffic contract have
been chosen such that we can expect a
CDR smaller than 10-5 for a compliant
source (i.e. source with parameters
according to Table 1). We call a source
non-compliant if the mean cell rate is
increased either by decreasing the OFFstate
duration or by increasing the ONstate
duration. Approximately 1/3 of the
sources have been chosen as well-behaving,
and the rest equally split as misbehaving
in the two different ways. For the
CAC a CMR target value of 10-4 has
been taken.
The results of this experiment are depicted
in Figures 13 and 14. Each figure
shows the various loss ratios as a function
of the mean cell rate of the non-compliant
sources normalised to the SCR
contained in the traffic contract. Figure
13 clearly shows that compliant sources
are protected since the overall loss ratio
expressed by CLR remains rather unchanged
for these sources, even if the
mean of the non-compliant sources is
nearly doubled. In all cases the value of
CLR is below the target value of 10-4 .
However, sources violating the traffic
contract suffer from cell discards at the
UPC (see Figure 14). Both figures also
reflect the fact that the overall loss ratio
at the multiplexer is hardly influenced at
all by traffic contract violations since the
excess traffic is already discarded at the
UPC. Furthermore, since these discards
at the UPC seem to smooth the output
traffic before this is multiplexed, the
CMR does not increase even if the aggregate
load of the multiplexer increases by
approximately the factor SCR/MCR for
the case with the highest mean rates,
where MCR refers to the rate of the compliant
sources.
In another series of experiments the traffic
characteristics of the sources are modified
by cell level changes to investigate
the impacts on the network performance.
The CDV tolerance τ in the PCR control
is increased, thereby allowing an increasing
number of back-to-back cells to pass
the UPC and enter the multiplexer. With
these experiments we study the sensitivity
of the network performance with
respect to sources sending clumps of
back-to-back cells. This sensitivity may
be crucial because the values for the
CDV tolerance τ are not used by the investigated
CAC algorithms. Bounds on
acceptable values for τ may be needed to
ensure the robustness of the framework.
Some initial results were already obtained,
but further investigations in this area
are necessary before conclusions can be
drawn.
5 Conclusions and future
work
Experimental results obtained in the
EXPLOIT Testbed on Usage Parameter
Control (UPC) and Connection Admission
Control (CAC) have been presented