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// Copyright 2017 The Chromium Authors
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.
#include "net/nqe/observation_buffer.h"
#include <float.h>
#include <algorithm>
#include <utility>
#include "base/time/default_tick_clock.h"
#include "base/time/time.h"
#include "net/nqe/network_quality_estimator_params.h"
#include "net/nqe/weighted_observation.h"
namespace net::nqe::internal {
ObservationBuffer::ObservationBuffer(
const NetworkQualityEstimatorParams* params,
const base::TickClock* tick_clock,
double weight_multiplier_per_second,
double weight_multiplier_per_signal_level)
: params_(params),
weight_multiplier_per_second_(weight_multiplier_per_second),
weight_multiplier_per_signal_level_(weight_multiplier_per_signal_level),
tick_clock_(tick_clock) {
DCHECK_LT(0u, params_->observation_buffer_size());
DCHECK_LE(0.0, weight_multiplier_per_second_);
DCHECK_GE(1.0, weight_multiplier_per_second_);
DCHECK_LE(0.0, weight_multiplier_per_signal_level_);
DCHECK_GE(1.0, weight_multiplier_per_signal_level_);
DCHECK(params_);
DCHECK(tick_clock_);
}
ObservationBuffer::ObservationBuffer(const ObservationBuffer& other)
: params_(other.params_),
weight_multiplier_per_second_(other.weight_multiplier_per_second_),
weight_multiplier_per_signal_level_(
other.weight_multiplier_per_signal_level_),
tick_clock_(other.tick_clock_) {
DCHECK(other.observations_.empty());
}
ObservationBuffer::~ObservationBuffer() = default;
std::optional<Observation> ObservationBuffer::AddObservation(
const Observation& observation) {
DCHECK_LE(observations_.size(), params_->observation_buffer_size());
// Observations must be in the non-decreasing order of the timestamps.
DCHECK(observations_.empty() ||
observation.timestamp() >= observations_.back().timestamp());
DCHECK(observation.signal_strength() == INT32_MIN ||
(observation.signal_strength() >= 0 &&
observation.signal_strength() <= 4));
std::optional<Observation> evicted_observation;
// Evict the oldest element if the buffer is already full.
if (observations_.size() == params_->observation_buffer_size()) {
evicted_observation = observations_.front();
observations_.pop_front();
}
observations_.push_back(observation);
DCHECK_LE(observations_.size(), params_->observation_buffer_size());
return evicted_observation;
}
std::optional<int32_t> ObservationBuffer::GetPercentile(
base::TimeTicks begin_timestamp,
int32_t current_signal_strength,
int percentile,
size_t* observations_count) const {
DCHECK(current_signal_strength == INT32_MIN ||
(current_signal_strength >= 0 && current_signal_strength <= 4));
// Stores weighted observations in increasing order by value.
std::vector<WeightedObservation> weighted_observations;
// Total weight of all observations in |weighted_observations|.
double total_weight = 0.0;
ComputeWeightedObservations(begin_timestamp, current_signal_strength,
&weighted_observations, &total_weight);
if (observations_count) {
// |observations_count| may be null.
*observations_count = weighted_observations.size();
}
if (weighted_observations.empty())
return std::nullopt;
double desired_weight = percentile / 100.0 * total_weight;
double cumulative_weight_seen_so_far = 0.0;
for (const auto& weighted_observation : weighted_observations) {
cumulative_weight_seen_so_far += weighted_observation.weight;
if (cumulative_weight_seen_so_far >= desired_weight)
return weighted_observation.value;
}
// Computation may reach here due to floating point errors. This may happen
// if |percentile| was 100 (or close to 100), and |desired_weight| was
// slightly larger than |total_weight| (due to floating point errors).
// In this case, we return the highest |value| among all observations.
// This is same as value of the last observation in the sorted vector.
return weighted_observations.at(weighted_observations.size() - 1).value;
}
void ObservationBuffer::RemoveObservationsWithSource(
const DeletedObservationSources& deleted_observation_sources) {
base::EraseIf(observations_,
[deleted_observation_sources](const Observation& observation) {
return deleted_observation_sources[static_cast<size_t>(
observation.source())];
});
}
void ObservationBuffer::ComputeWeightedObservations(
const base::TimeTicks& begin_timestamp,
int32_t current_signal_strength,
std::vector<WeightedObservation>* weighted_observations,
double* total_weight) const {
DCHECK_GE(Capacity(), Size());
weighted_observations->clear();
double total_weight_observations = 0.0;
base::TimeTicks now = tick_clock_->NowTicks();
for (const auto& observation : observations_) {
if (observation.timestamp() < begin_timestamp)
continue;
base::TimeDelta time_since_sample_taken = now - observation.timestamp();
double time_weight =
pow(weight_multiplier_per_second_, time_since_sample_taken.InSeconds());
double signal_strength_weight = 1.0;
if (current_signal_strength >= 0 && observation.signal_strength() >= 0) {
int32_t signal_strength_weight_diff =
std::abs(current_signal_strength - observation.signal_strength());
signal_strength_weight =
pow(weight_multiplier_per_signal_level_, signal_strength_weight_diff);
}
double weight = time_weight * signal_strength_weight;
weight = std::clamp(weight, DBL_MIN, 1.0);
weighted_observations->push_back(
WeightedObservation(observation.value(), weight));
total_weight_observations += weight;
}
// Sort the samples by value in ascending order.
std::sort(weighted_observations->begin(), weighted_observations->end());
*total_weight = total_weight_observations;
DCHECK_LE(0.0, *total_weight);
DCHECK(weighted_observations->empty() || 0.0 < *total_weight);
// |weighted_observations| may have a smaller size than |observations_|
// since the former contains only the observations later than
// |begin_timestamp|.
DCHECK_GE(observations_.size(), weighted_observations->size());
}
size_t ObservationBuffer::Capacity() const {
return params_->observation_buffer_size();
}
} // namespace net::nqe::internal
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