Here are some simulations on the churn-clustering topic which we've
been discussing..

The simulations show that churn by shortlived nodes entering and quickly
leaving the network can add upp to the clustering in the address space.
When freenet nodes join the network they typically randomize their
positions. The simulations conver what may happen when they leave after
a relatively short while, after having participated with the swapping
algorithm - so we can think of this as even non-malicious and possibly
normal behaviour.

The experiments were made as follows

1. Create a network with Kleinberg edges, average degree=10 and HTL=100
   when routing. The positions are then shuffled and the swapping algorithm
   is run until we have 90% success rate and relatively good routing
   (short paths). This should represent a network when the swapping has
   been running for a while, so we have quite good performance there.

2. For a number of steps (e.g. 10000) we introduce 5 nodes to the network
   trying to fit their edges into the circle as in the original model.
   These nodes randomize their initial locations.
   In each step we let each node in the network initate on average 10 swap
   attempts in the network (a 6-step random walk before asking the target
   to swap). So if N=1000, then we try 10050 swaps in the network.
   When this is done, the shortlived nodes are removed (and so are their
   positions).

3. We evaluate routing and distribution in the address range...
   Some example plots are below: for N=1000 and N=5000

The values are chosen so that I believe the interval is short and
reasonable. Here we are able to count the number of evaluated swaps
and see what happens after then; instead of trying to simulate the
whole underlying swap/swaplock/delay mechanisms involved we study
the evolution of the system with respect to evaluated swaps.

Results (comments on the plots):

For the 1000-node networks we can most clearly see the impact; the 5000-node
networks also seem to be on their way to prefer certain positions (the number
of introduced nodes/positions are the same here, thus we can expect it to take
more time for positions to propagate). A larger inflow (or longer time) is
needed for larger networks to degrade, that is quite natural.

The preference for positions in certain parts of the keyspace not only seem
to depend on initial skewness when nodes randomize positions (randomizing
initial positions of nodes always leads to slight variations in densities
of positions taken on the circle, but there is no clear 1-1 relation, see
below for experiments to test this idea).

Regarding if the /circle/ (not network) positions that are taken initially
(in 1.) by nodes affecting the outcome: experiments were also made over
initially *uniformly* distributed positions, which were then shuffled (to
have a totally uniform distribution of positions on the keyspace is done
by simply splitting the keyspace into N parts, and shuffling the positions
randomly instead of randomizing positions independently for nodes).

More on efficiency of randomness to counter this soon...

regards,
Vilhelm
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