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/*
* Copyright (c) 2016 The WebRTC project authors. All Rights Reserved.
*
* Use of this source code is governed by a BSD-style license
* that can be found in the LICENSE file in the root of the source
* tree. An additional intellectual property rights grant can be found
* in the file PATENTS. All contributing project authors may
* be found in the AUTHORS file in the root of the source tree.
*/
#include "modules/congestion_controller/goog_cc/trendline_estimator.h"
#include <math.h>
#include <algorithm>
#include "api/optional.h"
#include "modules/remote_bitrate_estimator/test/bwe_test_logging.h"
#include "rtc_base/checks.h"
#include "rtc_base/numerics/safe_minmax.h"
namespace webrtc {
namespace webrtc_cc {
namespace {
rtc::Optional<double> LinearFitSlope(
const std::deque<std::pair<double, double>>& points) {
RTC_DCHECK(points.size() >= 2);
// Compute the "center of mass".
double sum_x = 0;
double sum_y = 0;
for (const auto& point : points) {
sum_x += point.first;
sum_y += point.second;
}
double x_avg = sum_x / points.size();
double y_avg = sum_y / points.size();
// Compute the slope k = \sum (x_i-x_avg)(y_i-y_avg) / \sum (x_i-x_avg)^2
double numerator = 0;
double denominator = 0;
for (const auto& point : points) {
numerator += (point.first - x_avg) * (point.second - y_avg);
denominator += (point.first - x_avg) * (point.first - x_avg);
}
if (denominator == 0)
return rtc::nullopt;
return numerator / denominator;
}
constexpr double kMaxAdaptOffsetMs = 15.0;
constexpr double kOverUsingTimeThreshold = 10;
constexpr int kMinNumDeltas = 60;
} // namespace
enum { kDeltaCounterMax = 1000 };
TrendlineEstimator::TrendlineEstimator(size_t window_size,
double smoothing_coef,
double threshold_gain)
: window_size_(window_size),
smoothing_coef_(smoothing_coef),
threshold_gain_(threshold_gain),
num_of_deltas_(0),
first_arrival_time_ms_(-1),
accumulated_delay_(0),
smoothed_delay_(0),
delay_hist_(),
trendline_(0),
k_up_(0.0087),
k_down_(0.039),
overusing_time_threshold_(kOverUsingTimeThreshold),
threshold_(12.5),
last_update_ms_(-1),
prev_offset_(0.0),
time_over_using_(-1),
overuse_counter_(0),
hypothesis_(BandwidthUsage::kBwNormal) {}
TrendlineEstimator::~TrendlineEstimator() {}
void TrendlineEstimator::Update(double recv_delta_ms,
double send_delta_ms,
int64_t arrival_time_ms) {
const double delta_ms = recv_delta_ms - send_delta_ms;
++num_of_deltas_;
if (num_of_deltas_ > kDeltaCounterMax)
num_of_deltas_ = kDeltaCounterMax;
if (first_arrival_time_ms_ == -1)
first_arrival_time_ms_ = arrival_time_ms;
// Exponential backoff filter.
accumulated_delay_ += delta_ms;
BWE_TEST_LOGGING_PLOT(1, "accumulated_delay_ms", arrival_time_ms,
accumulated_delay_);
smoothed_delay_ = smoothing_coef_ * smoothed_delay_ +
(1 - smoothing_coef_) * accumulated_delay_;
BWE_TEST_LOGGING_PLOT(1, "smoothed_delay_ms", arrival_time_ms,
smoothed_delay_);
// Simple linear regression.
delay_hist_.push_back(std::make_pair(
static_cast<double>(arrival_time_ms - first_arrival_time_ms_),
smoothed_delay_));
if (delay_hist_.size() > window_size_)
delay_hist_.pop_front();
if (delay_hist_.size() == window_size_) {
// Only update trendline_ if it is possible to fit a line to the data.
trendline_ = LinearFitSlope(delay_hist_).value_or(trendline_);
}
BWE_TEST_LOGGING_PLOT(1, "trendline_slope", arrival_time_ms, trendline_);
Detect(trendline_slope(), send_delta_ms, num_of_deltas(), arrival_time_ms);
}
BandwidthUsage TrendlineEstimator::State() const {
return hypothesis_;
}
void TrendlineEstimator::Detect(double offset,
double ts_delta,
int num_of_deltas,
int64_t now_ms) {
if (num_of_deltas < 2) {
hypothesis_ = BandwidthUsage::kBwNormal;
return;
}
const double T = std::min(num_of_deltas, kMinNumDeltas) * offset;
BWE_TEST_LOGGING_PLOT(1, "T", now_ms, T);
BWE_TEST_LOGGING_PLOT(1, "threshold", now_ms, threshold_);
if (T > threshold_) {
if (time_over_using_ == -1) {
// Initialize the timer. Assume that we've been
// over-using half of the time since the previous
// sample.
time_over_using_ = ts_delta / 2;
} else {
// Increment timer
time_over_using_ += ts_delta;
}
overuse_counter_++;
if (time_over_using_ > overusing_time_threshold_ && overuse_counter_ > 1) {
if (offset >= prev_offset_) {
time_over_using_ = 0;
overuse_counter_ = 0;
hypothesis_ = BandwidthUsage::kBwOverusing;
}
}
} else if (T < -threshold_) {
time_over_using_ = -1;
overuse_counter_ = 0;
hypothesis_ = BandwidthUsage::kBwUnderusing;
} else {
time_over_using_ = -1;
overuse_counter_ = 0;
hypothesis_ = BandwidthUsage::kBwNormal;
}
prev_offset_ = offset;
UpdateThreshold(T, now_ms);
}
void TrendlineEstimator::UpdateThreshold(double modified_offset,
int64_t now_ms) {
if (last_update_ms_ == -1)
last_update_ms_ = now_ms;
if (fabs(modified_offset) > threshold_ + kMaxAdaptOffsetMs) {
// Avoid adapting the threshold to big latency spikes, caused e.g.,
// by a sudden capacity drop.
last_update_ms_ = now_ms;
return;
}
const double k = fabs(modified_offset) < threshold_ ? k_down_ : k_up_;
const int64_t kMaxTimeDeltaMs = 100;
int64_t time_delta_ms = std::min(now_ms - last_update_ms_, kMaxTimeDeltaMs);
threshold_ += k * (fabs(modified_offset) - threshold_) * time_delta_ms;
threshold_ = rtc::SafeClamp(threshold_, 6.f, 600.f);
last_update_ms_ = now_ms;
}
} // namespace webrtc_cc
} // namespace webrtc