| /* |
| * 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 <algorithm> |
| #include <cmath> |
| #include <cstddef> |
| #include <cstdint> |
| #include <cstdio> |
| #include <deque> |
| #include <memory> |
| #include <optional> |
| #include <string> |
| #include <utility> |
| |
| #include "api/field_trials_view.h" |
| #include "api/network_state_predictor.h" |
| #include "api/transport/bandwidth_usage.h" |
| #include "rtc_base/checks.h" |
| #include "rtc_base/experiments/struct_parameters_parser.h" |
| #include "rtc_base/logging.h" |
| #include "rtc_base/numerics/safe_minmax.h" |
| |
| namespace webrtc { |
| |
| namespace { |
| |
| // Parameters for linear least squares fit of regression line to noisy data. |
| constexpr double kDefaultTrendlineSmoothingCoeff = 0.9; |
| constexpr double kDefaultTrendlineThresholdGain = 4.0; |
| const char kBweWindowSizeInPacketsExperiment[] = |
| "WebRTC-BweWindowSizeInPackets"; |
| |
| size_t ReadTrendlineFilterWindowSize(const FieldTrialsView& key_value_config) { |
| std::string experiment_string = |
| key_value_config.Lookup(kBweWindowSizeInPacketsExperiment); |
| size_t window_size; |
| int parsed_values = |
| sscanf(experiment_string.c_str(), "Enabled-%zu", &window_size); |
| if (parsed_values == 1) { |
| if (window_size > 1) |
| return window_size; |
| RTC_LOG(LS_WARNING) << "Window size must be greater than 1."; |
| } |
| RTC_LOG(LS_WARNING) << "Failed to parse parameters for BweWindowSizeInPackets" |
| " experiment from field trial string. Using default."; |
| return TrendlineEstimatorSettings::kDefaultTrendlineWindowSize; |
| } |
| |
| std::optional<double> LinearFitSlope( |
| const std::deque<TrendlineEstimator::PacketTiming>& packets) { |
| RTC_DCHECK(packets.size() >= 2); |
| // Compute the "center of mass". |
| double sum_x = 0; |
| double sum_y = 0; |
| for (const auto& packet : packets) { |
| sum_x += packet.arrival_time_ms; |
| sum_y += packet.smoothed_delay_ms; |
| } |
| double x_avg = sum_x / packets.size(); |
| double y_avg = sum_y / packets.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& packet : packets) { |
| double x = packet.arrival_time_ms; |
| double y = packet.smoothed_delay_ms; |
| numerator += (x - x_avg) * (y - y_avg); |
| denominator += (x - x_avg) * (x - x_avg); |
| } |
| if (denominator == 0) |
| return std::nullopt; |
| return numerator / denominator; |
| } |
| |
| std::optional<double> ComputeSlopeCap( |
| const std::deque<TrendlineEstimator::PacketTiming>& packets, |
| const TrendlineEstimatorSettings& settings) { |
| RTC_DCHECK(1 <= settings.beginning_packets && |
| settings.beginning_packets < packets.size()); |
| RTC_DCHECK(1 <= settings.end_packets && |
| settings.end_packets < packets.size()); |
| RTC_DCHECK(settings.beginning_packets + settings.end_packets <= |
| packets.size()); |
| TrendlineEstimator::PacketTiming early = packets[0]; |
| for (size_t i = 1; i < settings.beginning_packets; ++i) { |
| if (packets[i].raw_delay_ms < early.raw_delay_ms) |
| early = packets[i]; |
| } |
| size_t late_start = packets.size() - settings.end_packets; |
| TrendlineEstimator::PacketTiming late = packets[late_start]; |
| for (size_t i = late_start + 1; i < packets.size(); ++i) { |
| if (packets[i].raw_delay_ms < late.raw_delay_ms) |
| late = packets[i]; |
| } |
| if (late.arrival_time_ms - early.arrival_time_ms < 1) { |
| return std::nullopt; |
| } |
| return (late.raw_delay_ms - early.raw_delay_ms) / |
| (late.arrival_time_ms - early.arrival_time_ms) + |
| settings.cap_uncertainty; |
| } |
| |
| constexpr double kMaxAdaptOffsetMs = 15.0; |
| constexpr double kOverUsingTimeThreshold = 10; |
| constexpr int kMinNumDeltas = 60; |
| constexpr int kDeltaCounterMax = 1000; |
| |
| } // namespace |
| |
| TrendlineEstimatorSettings::TrendlineEstimatorSettings( |
| const FieldTrialsView& key_value_config) { |
| if (key_value_config.IsEnabled(kBweWindowSizeInPacketsExperiment)) { |
| window_size = ReadTrendlineFilterWindowSize(key_value_config); |
| } |
| Parser()->Parse(key_value_config.Lookup(TrendlineEstimatorSettings::kKey)); |
| if (window_size < 10 || 200 < window_size) { |
| RTC_LOG(LS_WARNING) << "Window size must be between 10 and 200 packets"; |
| window_size = kDefaultTrendlineWindowSize; |
| } |
| if (enable_cap) { |
| if (beginning_packets < 1 || end_packets < 1 || |
| beginning_packets > window_size || end_packets > window_size) { |
| RTC_LOG(LS_WARNING) << "Size of beginning and end must be between 1 and " |
| << window_size; |
| enable_cap = false; |
| beginning_packets = end_packets = 0; |
| cap_uncertainty = 0.0; |
| } |
| if (beginning_packets + end_packets > window_size) { |
| RTC_LOG(LS_WARNING) |
| << "Size of beginning plus end can't exceed the window size"; |
| enable_cap = false; |
| beginning_packets = end_packets = 0; |
| cap_uncertainty = 0.0; |
| } |
| if (cap_uncertainty < 0.0 || 0.025 < cap_uncertainty) { |
| RTC_LOG(LS_WARNING) << "Cap uncertainty must be between 0 and 0.025"; |
| cap_uncertainty = 0.0; |
| } |
| } |
| } |
| |
| std::unique_ptr<StructParametersParser> TrendlineEstimatorSettings::Parser() { |
| return StructParametersParser::Create("sort", &enable_sort, // |
| "cap", &enable_cap, // |
| "beginning_packets", |
| &beginning_packets, // |
| "end_packets", &end_packets, // |
| "cap_uncertainty", &cap_uncertainty, // |
| "window_size", &window_size); |
| } |
| |
| TrendlineEstimator::TrendlineEstimator( |
| const FieldTrialsView& key_value_config, |
| NetworkStatePredictor* network_state_predictor) |
| : settings_(key_value_config), |
| smoothing_coef_(kDefaultTrendlineSmoothingCoeff), |
| threshold_gain_(kDefaultTrendlineThresholdGain), |
| num_of_deltas_(0), |
| first_arrival_time_ms_(-1), |
| accumulated_delay_(0), |
| smoothed_delay_(0), |
| delay_hist_(), |
| k_up_(0.0087), |
| k_down_(0.039), |
| overusing_time_threshold_(kOverUsingTimeThreshold), |
| threshold_(12.5), |
| prev_modified_trend_(NAN), |
| last_update_ms_(-1), |
| prev_trend_(0.0), |
| time_over_using_(-1), |
| overuse_counter_(0), |
| hypothesis_(BandwidthUsage::kBwNormal), |
| hypothesis_predicted_(BandwidthUsage::kBwNormal), |
| network_state_predictor_(network_state_predictor) { |
| RTC_LOG(LS_INFO) |
| << "Using Trendline filter for delay change estimation with settings " |
| << settings_.Parser()->Encode() << " and " |
| << (network_state_predictor_ ? "injected" : "no") |
| << " network state predictor"; |
| } |
| |
| TrendlineEstimator::~TrendlineEstimator() {} |
| |
| void TrendlineEstimator::UpdateTrendline(double recv_delta_ms, |
| double send_delta_ms, |
| int64_t /* send_time_ms */, |
| int64_t arrival_time_ms, |
| size_t /* packet_size */) { |
| const double delta_ms = recv_delta_ms - send_delta_ms; |
| ++num_of_deltas_; |
| num_of_deltas_ = std::min(num_of_deltas_, kDeltaCounterMax); |
| if (first_arrival_time_ms_ == -1) |
| first_arrival_time_ms_ = arrival_time_ms; |
| |
| // Exponential backoff filter. |
| accumulated_delay_ += delta_ms; |
| smoothed_delay_ = smoothing_coef_ * smoothed_delay_ + |
| (1 - smoothing_coef_) * accumulated_delay_; |
| |
| // Maintain packet window |
| delay_hist_.emplace_back( |
| static_cast<double>(arrival_time_ms - first_arrival_time_ms_), |
| smoothed_delay_, accumulated_delay_); |
| if (settings_.enable_sort) { |
| for (size_t i = delay_hist_.size() - 1; |
| i > 0 && |
| delay_hist_[i].arrival_time_ms < delay_hist_[i - 1].arrival_time_ms; |
| --i) { |
| std::swap(delay_hist_[i], delay_hist_[i - 1]); |
| } |
| } |
| if (delay_hist_.size() > settings_.window_size) |
| delay_hist_.pop_front(); |
| |
| // Simple linear regression. |
| double trend = prev_trend_; |
| if (delay_hist_.size() == settings_.window_size) { |
| // Update trend_ if it is possible to fit a line to the data. The delay |
| // trend can be seen as an estimate of (send_rate - capacity)/capacity. |
| // 0 < trend < 1 -> the delay increases, queues are filling up |
| // trend == 0 -> the delay does not change |
| // trend < 0 -> the delay decreases, queues are being emptied |
| trend = LinearFitSlope(delay_hist_).value_or(trend); |
| if (settings_.enable_cap) { |
| std::optional<double> cap = ComputeSlopeCap(delay_hist_, settings_); |
| // We only use the cap to filter out overuse detections, not |
| // to detect additional underuses. |
| if (trend >= 0 && cap.has_value() && trend > cap.value()) { |
| trend = cap.value(); |
| } |
| } |
| } |
| |
| Detect(trend, send_delta_ms, arrival_time_ms); |
| } |
| |
| void TrendlineEstimator::Update(double recv_delta_ms, |
| double send_delta_ms, |
| int64_t send_time_ms, |
| int64_t arrival_time_ms, |
| size_t packet_size, |
| bool calculated_deltas) { |
| if (calculated_deltas) { |
| UpdateTrendline(recv_delta_ms, send_delta_ms, send_time_ms, arrival_time_ms, |
| packet_size); |
| } |
| if (network_state_predictor_) { |
| hypothesis_predicted_ = network_state_predictor_->Update( |
| send_time_ms, arrival_time_ms, hypothesis_); |
| } |
| } |
| |
| BandwidthUsage TrendlineEstimator::State() const { |
| return network_state_predictor_ ? hypothesis_predicted_ : hypothesis_; |
| } |
| |
| void TrendlineEstimator::Detect(double trend, double ts_delta, int64_t now_ms) { |
| if (num_of_deltas_ < 2) { |
| hypothesis_ = BandwidthUsage::kBwNormal; |
| return; |
| } |
| const double modified_trend = |
| std::min(num_of_deltas_, kMinNumDeltas) * trend * threshold_gain_; |
| prev_modified_trend_ = modified_trend; |
| if (modified_trend > 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 (trend >= prev_trend_) { |
| time_over_using_ = 0; |
| overuse_counter_ = 0; |
| hypothesis_ = BandwidthUsage::kBwOverusing; |
| } |
| } |
| } else if (modified_trend < -threshold_) { |
| time_over_using_ = -1; |
| overuse_counter_ = 0; |
| hypothesis_ = BandwidthUsage::kBwUnderusing; |
| } else { |
| time_over_using_ = -1; |
| overuse_counter_ = 0; |
| hypothesis_ = BandwidthUsage::kBwNormal; |
| } |
| prev_trend_ = trend; |
| UpdateThreshold(modified_trend, now_ms); |
| } |
| |
| void TrendlineEstimator::UpdateThreshold(double modified_trend, |
| int64_t now_ms) { |
| if (last_update_ms_ == -1) |
| last_update_ms_ = now_ms; |
| |
| if (fabs(modified_trend) > 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_trend) < 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_trend) - threshold_) * time_delta_ms; |
| threshold_ = SafeClamp(threshold_, 6.f, 600.f); |
| last_update_ms_ = now_ms; |
| } |
| |
| } // namespace webrtc |
