Struct tor_circmgr::timeouts::pareto::History
source · struct History {
time_history: BoundedVecDeque<MsecDuration>,
time_histogram: BTreeMap<MsecDuration, u16>,
success_history: BoundedVecDeque<bool>,
}
Expand description
A history of circuit timeout observations, used to estimate our likely circuit timeouts.
Fields§
§time_history: BoundedVecDeque<MsecDuration>
Our most recent observed circuit construction times.
For the purpose of this estimator, a circuit counts as “constructed” when a certain “significant” hop (typically the third) is completed.
time_histogram: BTreeMap<MsecDuration, u16>
A histogram representation of the values in History::time_history
.
This histogram is implemented as a sparse map from the center value of each histogram bucket to the number of entries in that bucket. It is completely derivable from time_history; we keep it separate here for efficiency.
success_history: BoundedVecDeque<bool>
Our most recent circuit timeout statuses.
Each true
value represents a successfully completed circuit
(all hops). Each false
value represents a circuit that
timed out after having completed at least one hop.
Implementations§
source§impl History
impl History
sourcefn set_success_history_len(&mut self, n: usize)
fn set_success_history_len(&mut self, n: usize)
Change the number of successes to record in our success
history to n
.
sourcefn from_sparse_histogram<I>(iter: I) -> Self
fn from_sparse_histogram<I>(iter: I) -> Self
Construct a new History
from an iterator representing a sparse
histogram of values.
The input must be a sequence of (D,N)
tuples, where each D
represents a circuit build duration, and N
represents the
number of observations with that duration.
These observations are shuffled into a random order, then added to a new History.
sourcefn sparse_histogram(&self) -> impl Iterator<Item = (MsecDuration, u16)> + '_
fn sparse_histogram(&self) -> impl Iterator<Item = (MsecDuration, u16)> + '_
Return an iterator yielding a sparse histogram of the circuit build
time values in this History
.
Each histogram entry is a (D,N)
tuple, where D
is the
center of a histogram bucket, and N
is the number of
observations in that bucket.
Buckets with N=0
are omitted. Buckets are yielded in order.
sourcefn bucket_center(time: MsecDuration) -> MsecDuration
fn bucket_center(time: MsecDuration) -> MsecDuration
Return the center value for the bucket containing time
.
sourcefn inc_bucket(&mut self, time: MsecDuration)
fn inc_bucket(&mut self, time: MsecDuration)
Increment the histogram bucket containing time
by one.
sourcefn dec_bucket(&mut self, time: MsecDuration)
fn dec_bucket(&mut self, time: MsecDuration)
Decrement the histogram bucket containing time
by one, removing
it if it becomes 0.
sourcefn add_time(&mut self, time: MsecDuration)
fn add_time(&mut self, time: MsecDuration)
Add time
to our list of circuit build time observations, and
adjust the histogram accordingly.
sourcefn n_times(&self) -> usize
fn n_times(&self) -> usize
Return the number of observations in our time history.
This will always be <= TIME_HISTORY_LEN
.
sourcefn add_success(&mut self, succeeded: bool)
fn add_success(&mut self, succeeded: bool)
Record a success (true) or timeout (false) in our record of whether circuits timed out or not.
sourcefn n_recent_timeouts(&self) -> usize
fn n_recent_timeouts(&self) -> usize
Return the number of timeouts recorded in our success history.
sourcefn n_most_frequent_bins(&self, n: usize) -> Vec<(MsecDuration, u16)>
fn n_most_frequent_bins(&self, n: usize) -> Vec<(MsecDuration, u16)>
Helper: return the n
most frequent histogram bins.
sourcefn estimate_xm(&self, n_modes: usize) -> Option<u32>
fn estimate_xm(&self, n_modes: usize) -> Option<u32>
Return an estimator for the X_m
of our Pareto distribution,
by looking at the n_modes
most frequently filled histogram
bins.
It is not a true X_m
value, since there are definitely
values less than this, but it seems to work as a decent
heuristic.
Return None
if we have no observations.
sourcefn pareto_estimate(&self, n_modes: usize) -> Option<ParetoDist>
fn pareto_estimate(&self, n_modes: usize) -> Option<ParetoDist>
Compute a maximum-likelihood pareto distribution based on this
history, computing X_m
based on the n_modes
most frequent
histograms.
Return None if we have no observations.
Trait Implementations§
Auto Trait Implementations§
impl Freeze for History
impl RefUnwindSafe for History
impl Send for History
impl Sync for History
impl Unpin for History
impl UnwindSafe for History
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