This is in response to community feedback that our existing reputation
calculation is too likely to disqualify storage nodes unfairly with
extreme swings up and down.
For details and analysis, please see the data_loss_vs_dq_chance_sim.py
tool, the "tuning reputation further.ipynb" Jupyter notebook in the
storj/datascience repository, and the discussion at
https://forum.storj.io/t/tuning-audit-scoring/14084
In brief: changing the lambda and initial-alpha parameters in this way
causes the swings in reputation to be smaller and less likely to put a
node past the disqualification threshold unfairly.
Note: this change will cause a one-time reset of all (non-disqualified)
node reputations, because the new initial alpha value of 1000 is
dramatically different, and the disqualification threshold is going to
be much higher.
Change-Id: Id6dc4ba8fde1be3db4255b72282207bab5491ca3
In an effort to distribute load on the reputation database, the
reputation write cache scheduled nodes to be written at a time offset by
the local nodeID. The idea was that no two repair workers would have the
same nodeID, so they would not tend to write to the same row at the same
time.
Instead, since all satellite processes share the same satellite ID
(duh), this caused _all_ workers to try and write to the same row at the
same time _always_. This was not ideal.
This change uses a random number instead of the satellite ID. The random
number is sourced from the number of nanoseconds since the Unix epoch.
As long as workers are not started at the exact same nanosecond, they
ought to get well-distributed offsets.
Change-Id: I149bdaa6ca1ee6043cfedcf1489dd9d3e3c7a163
