Update proposal to match implementation.

doc/spec/proposals/151-path-selection-improvements.txt

@@ -20,7 +20,7 @@ Motivation
 
 Implementation
 
-  Storing Build Times
+  Gathering Build Times
 
     Circuit build times are stored in the circular array
     'circuit_build_times' consisting of uint32_t elements as milliseconds.
@@ -30,8 +30,16 @@ Implementation
     too large, because it will make it difficult for clients to adapt to
     moving between different links.
 
-    From our observations, this value appears to be on the order of 1000,
+    From our observations, the minimum value for a reasonable fit appears
-    but is configurable in a #define NCIRCUITS_TO_OBSERVE.
+    to be on the order of 500 (MIN_CIRCUITS_TO_OBSERVE). However, to keep
+    a good fit over the long term, we store 5000 most recent circuits in
+    the array (NCIRCUITS_TO_OBSERVE).
+
+    The Tor client will build test circuits at a rate of one per
+    minute (BUILD_TIMES_TEST_FREQUENCY) up to the point of
+    MIN_CIRCUITS_TO_OBSERVE. This allows a fresh Tor to have
+    a CircuitBuildTimeout estimated within 8 hours after install,
+    upgrade, or network change (see below).
 
   Long Term Storage
 
@@ -43,9 +51,9 @@ Implementation
     Example:
 
     TotalBuildTimes 100
-    CircuitBuildTimeBin 0 50
+    CircuitBuildTimeBin 25 50
-    CircuitBuildTimeBin 50 25
+    CircuitBuildTimeBin 75 25
-    CircuitBuildTimeBin 100 13
+    CircuitBuildTimeBin 125 13
     ...
 
     Reading the histogram in will entail inserting <count> values
@@ -57,7 +65,12 @@ Implementation
   Learning the CircuitBuildTimeout
 
     Based on studies of build times, we found that the distribution of
-    circuit buildtimes appears to be a Pareto distribution.
+    circuit buildtimes appears to be a Frechet distribution. However,
+    estimators and quantile functions of the Frechet distribution are
+    difficult to work with and slow to converge. So instead, since we
+    are only interested in the accuracy of the tail, we approximate
+    the tail of the distribution with a Pareto curve starting at
+    the mode of the circuit build time sample set.
 
     We will calculate the parameters for a Pareto distribution
     fitting the data using the estimators at
@@ -73,11 +86,8 @@ Implementation
 
   Detecting Changing Network Conditions
 
-    We attempt to detect both network connectivty loss and drastic
+    We attempt to detect both network connectivity loss and drastic
-    changes in the timeout characteristics. Network connectivity loss
+    changes in the timeout characteristics.
-    is detected by recording a timestamp every time Tor either completes
-    a TLS connection or receives a cell. If this timestamp is more than
-    90 seconds in the past, circuit timeouts are no longer counted.
 
     If more than MAX_RECENT_TIMEOUT_RATE (80%) of the past
     RECENT_CIRCUITS (20) time out, we assume the network connection
@@ -86,6 +96,11 @@ Implementation
     position on the Pareto Quartile function for the ratio of
     timeouts.
 
+    Network connectivity loss is detected by recording a timestamp every
+    time Tor either completes a TLS connection or receives a cell. If
+    this timestamp is more than CircuitBuildTimeout*RECENT_CIRCUITS/3
+    seconds in the past, circuit timeouts are no longer counted.
+
   Testing
 
     After circuit build times, storage, and learning are implemented,
@@ -96,44 +111,28 @@ Implementation
     the python produces matches that which is output to the state file in Tor,
     and verify that the Pareto parameters and cutoff points also match.
 
-  Soft timeout vs Hard Timeout
+    We will also verify that there are no unexpected large deviations from
-
+    node selection, such as nodes from distant geographical locations being
-    At some point, it may be desirable to change the cutoff from a
+    completely excluded.
-    single hard cutoff that destroys the circuit to a soft cutoff and
-    a hard cutoff, where the soft cutoff merely triggers the building
-    of a new circuit, and the hard cutoff triggers destruction of the
-    circuit.
-
-    Good values for hard and soft cutoffs seem to be 80% and 60%
-    respectively, but we should eventually justify this with observation.
-
-  When to Begin Calculation
-
-    The number of circuits to observe (NCIRCUITS_TO_CUTOFF) before
-    changing the CircuitBuildTimeout will be tunable via a #define. From
-    our measurements, a good value for NCIRCUITS_TO_CUTOFF appears to be
-    on the order of 100.
 
   Dealing with Timeouts
 
     Timeouts should be counted as the expectation of the region of
-    of the Pareto distribution beyond the cutoff. The proposal will
+    of the Pareto distribution beyond the cutoff. This is done by
-    be updated with this value soon.
+    generating a random sample for each timeout at points on the
+    curve beyond the current timeout cutoff.
 
-    Also, in the event of network failure, the observation mechanism
+  Future Work
-    should stop collecting timeout data.
 
-  Client Hints
+    At some point, it may be desirable to change the cutoff from a
-
+    single hard cutoff that destroys the circuit to a soft cutoff and
-    Some research still needs to be done to provide initial values
+    a hard cutoff, where the soft cutoff merely triggers the building
-    for CircuitBuildTimeout based on values learned from modem
+    of a new circuit, and the hard cutoff triggers destruction of the
-    users, DSL users, Cable Modem users, and dedicated links. A
+    circuit.
-    radiobutton in Vidalia should eventually be provided that
-    sets CircuitBuildTimeout to one of these values and also
-    provide the option of purging all learned data, should any exist.
 
-    These values can either be published in the directory, or
+    It may also be beneficial to learn separate timeouts for each
-    shipped hardcoded for a particular Tor version.
+    guard node, as they will have slightly different distributions.
+    This will take longer to generate initial values though.
 
 Issues