switching probability). Thus if P = 1 all
recommended path switches occur;
when P = 0.001 only one of every 1,000
recommended path switches actually
takes place. Thus the top curve shows
how the switching probability affects
path oscillations, starting with a given
path and returning to it again; as the
switching probability increases so does
the rate at which paths oscillate. The effect of P on the packet drop rate at the
output resequencing buffer is shown
in the bottom chart in the figure.
Figure 12 indicates that improvement in QoS (delay in this case) can be
achieved with a small switching probability (P = 0.01 or a little higher). Path
switching improves average delay (and
is why CPN attempts to switch paths),
though it comes at the cost of packet
loss. However, one can mitigate this
loss by probabilistically limiting the
switching while retaining the benefit of
improved QoS by lowering packet delay.
The SAN programmer can also limit
oscillations by setting a threshold that
allows a path switch only when the projected QoS improvement exceeds the
threshold. A small threshold allows
more frequent switches and hence potentially more oscillations, but a large
threshold may hurt QoS. Figure 13
shows that if the threshold is small, the
observed packet delay is large, and as
the threshold increases delay improves,
but packet delay increases again for
larger thresholds. For small threshold
values, longer packet delays indicate
that switching occurs based on “noise”
rather than on real gain. Increasing
the threshold in Figure 14 would reduce the oscillations, though the effect
would level out quickly. The threshold
thus limits the negative effect of switching but preserves the advantages of self-awareness and adaptation.
The approach to developing self-aware
networks presented here gives end users the means to explore the state of
the network so as to find the best ways
to meet their communication needs.
Focusing on the primary function of
packet routing, I have tried to answer
a number of questions concerning
the feasibility of such networks and
whether reliable communications
is possible in largely unknown networks. I have also addressed whether
there is a risk of unstable behavior in
such systems due to constant “
changes of mind” and oscillations as new information becomes available to users
and whether a user’s ability to adapt
to changing circumstances in the network reduces the consequences of
network failure. The experiments reported relate to small (up to 46-node)
networks; more results are available at
The Internet consists of hierarchically organized autonomous systems of
relatively small size, and one can imagine that routing inside and among them
would benefit from the techniques discussed here. Future research is likely to
investigate how these ideas can be integrated into existing networks, how they
scale to large networks, how they might
be able to withstand the malicious behavior of users and network nodes, and
how they can support mobile users.
Research sponsored by the U.K. Engineering and Physical Sciences Research Council Grant GR/S52360/01
on self-aware networks and quality of
service; and by E.U. FP6 Projects on
self-aware networks, performance,
and adaptivity and componentware
for autonomic situation-aware communications and dynamically adaptable services.
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Erol Gelenbe ( firstname.lastname@example.org) is head of
the Intelligent Systems and Networks Research Group
and Professor in the Dennis Gabor Chair, Electrical and
Electronic Engineering Department, Imperial College
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