src/hotspot/share/gc/shenandoah/heuristics/shenandoahAdaptiveHeuristics.cpp - openjdk/jdk25u - Git at Google

 /*
  * Copyright (c) 2018, 2019, Red Hat, Inc. All rights reserved.
  * Copyright Amazon.com Inc. or its affiliates. All Rights Reserved.
  * Copyright (c) 2025, Oracle and/or its affiliates. All rights reserved.
  * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
  *
  * This code is free software; you can redistribute it and/or modify it
  * under the terms of the GNU General Public License version 2 only, as
  * published by the Free Software Foundation.
  *
  * This code is distributed in the hope that it will be useful, but WITHOUT
  * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
  * FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License
  * version 2 for more details (a copy is included in the LICENSE file that
  * accompanied this code).
  *
  * You should have received a copy of the GNU General Public License version
  * 2 along with this work; if not, write to the Free Software Foundation,
  * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
  *
  * Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA
  * or visit www.oracle.com if you need additional information or have any
  * questions.
  *
  */


 #include "gc/shared/gcCause.hpp"
 #include "gc/shenandoah/heuristics/shenandoahAdaptiveHeuristics.hpp"
 #include "gc/shenandoah/heuristics/shenandoahHeuristics.hpp"
 #include "gc/shenandoah/heuristics/shenandoahSpaceInfo.hpp"
 #include "gc/shenandoah/shenandoahCollectionSet.hpp"
 #include "gc/shenandoah/shenandoahCollectorPolicy.hpp"
 #include "gc/shenandoah/shenandoahFreeSet.hpp"
 #include "gc/shenandoah/shenandoahHeap.inline.hpp"
 #include "gc/shenandoah/shenandoahHeapRegion.inline.hpp"
 #include "logging/log.hpp"
 #include "logging/logTag.hpp"
 #include "runtime/globals.hpp"
 #include "utilities/quickSort.hpp"

 // These constants are used to adjust the margin of error for the moving
 // average of the allocation rate and cycle time. The units are standard
 // deviations.
 const double ShenandoahAdaptiveHeuristics::FULL_PENALTY_SD = 0.2;
 const double ShenandoahAdaptiveHeuristics::DEGENERATE_PENALTY_SD = 0.1;

 // These are used to decide if we want to make any adjustments at all
 // at the end of a successful concurrent cycle.
 const double ShenandoahAdaptiveHeuristics::LOWEST_EXPECTED_AVAILABLE_AT_END = -0.5;
 const double ShenandoahAdaptiveHeuristics::HIGHEST_EXPECTED_AVAILABLE_AT_END = 0.5;

 // These values are the confidence interval expressed as standard deviations.
 // At the minimum confidence level, there is a 25% chance that the true value of
 // the estimate (average cycle time or allocation rate) is not more than
 // MINIMUM_CONFIDENCE standard deviations away from our estimate. Similarly, the
 // MAXIMUM_CONFIDENCE interval here means there is a one in a thousand chance
 // that the true value of our estimate is outside the interval. These are used
 // as bounds on the adjustments applied at the outcome of a GC cycle.
 const double ShenandoahAdaptiveHeuristics::MINIMUM_CONFIDENCE = 0.319; // 25%
 const double ShenandoahAdaptiveHeuristics::MAXIMUM_CONFIDENCE = 3.291; // 99.9%

 ShenandoahAdaptiveHeuristics::ShenandoahAdaptiveHeuristics(ShenandoahSpaceInfo* space_info) :
   ShenandoahHeuristics(space_info),
   _margin_of_error_sd(ShenandoahAdaptiveInitialConfidence),
   _spike_threshold_sd(ShenandoahAdaptiveInitialSpikeThreshold),
   _last_trigger(OTHER),
   _available(Moving_Average_Samples, ShenandoahAdaptiveDecayFactor) { }

 ShenandoahAdaptiveHeuristics::~ShenandoahAdaptiveHeuristics() {}

 void ShenandoahAdaptiveHeuristics::choose_collection_set_from_regiondata(ShenandoahCollectionSet* cset,
                                                                          RegionData* data, size_t size,
                                                                          size_t actual_free) {
   size_t garbage_threshold = ShenandoahHeapRegion::region_size_bytes() * ShenandoahGarbageThreshold / 100;

   // The logic for cset selection in adaptive is as follows:
   //
   //   1. We cannot get cset larger than available free space. Otherwise we guarantee OOME
   //      during evacuation, and thus guarantee full GC. In practice, we also want to let
   //      application to allocate something. This is why we limit CSet to some fraction of
   //      available space. In non-overloaded heap, max_cset would contain all plausible candidates
   //      over garbage threshold.
   //
   //   2. We should not get cset too low so that free threshold would not be met right
   //      after the cycle. Otherwise we get back-to-back cycles for no reason if heap is
   //      too fragmented. In non-overloaded non-fragmented heap min_garbage would be around zero.
   //
   // Therefore, we start by sorting the regions by garbage. Then we unconditionally add the best candidates
   // before we meet min_garbage. Then we add all candidates that fit with a garbage threshold before
   // we hit max_cset. When max_cset is hit, we terminate the cset selection. Note that in this scheme,
   // ShenandoahGarbageThreshold is the soft threshold which would be ignored until min_garbage is hit.

   size_t capacity    = _space_info->soft_max_capacity();
   size_t max_cset    = (size_t)((1.0 * capacity / 100 * ShenandoahEvacReserve) / ShenandoahEvacWaste);
   size_t free_target = (capacity / 100 * ShenandoahMinFreeThreshold) + max_cset;
   size_t min_garbage = (free_target > actual_free ? (free_target - actual_free) : 0);

   log_info(gc, ergo)("Adaptive CSet Selection. Target Free: %zu%s, Actual Free: "
                      "%zu%s, Max Evacuation: %zu%s, Min Garbage: %zu%s",
                      byte_size_in_proper_unit(free_target), proper_unit_for_byte_size(free_target),
                      byte_size_in_proper_unit(actual_free), proper_unit_for_byte_size(actual_free),
                      byte_size_in_proper_unit(max_cset),    proper_unit_for_byte_size(max_cset),
                      byte_size_in_proper_unit(min_garbage), proper_unit_for_byte_size(min_garbage));

   // Better select garbage-first regions
   QuickSort::sort(data, size, compare_by_garbage);

   size_t cur_cset = 0;
   size_t cur_garbage = 0;

   for (size_t idx = 0; idx < size; idx++) {
     ShenandoahHeapRegion* r = data[idx].get_region();

     size_t new_cset    = cur_cset + r->get_live_data_bytes();
     size_t new_garbage = cur_garbage + r->garbage();

     if (new_cset > max_cset) {
       break;
     }

     if ((new_garbage < min_garbage) || (r->garbage() > garbage_threshold)) {
       cset->add_region(r);
       cur_cset = new_cset;
       cur_garbage = new_garbage;
     }
   }
 }

 void ShenandoahAdaptiveHeuristics::record_cycle_start() {
   ShenandoahHeuristics::record_cycle_start();
   _allocation_rate.allocation_counter_reset();
 }

 void ShenandoahAdaptiveHeuristics::record_success_concurrent() {
   ShenandoahHeuristics::record_success_concurrent();

   size_t available = _space_info->available();

   double z_score = 0.0;
   double available_sd = _available.sd();
   if (available_sd > 0) {
     double available_avg = _available.avg();
     z_score = (double(available) - available_avg) / available_sd;
     log_debug(gc, ergo)("Available: %zu %sB, z-score=%.3f. Average available: %.1f %sB +/- %.1f %sB.",
                         byte_size_in_proper_unit(available), proper_unit_for_byte_size(available),
                         z_score,
                         byte_size_in_proper_unit(available_avg), proper_unit_for_byte_size(available_avg),
                         byte_size_in_proper_unit(available_sd), proper_unit_for_byte_size(available_sd));
   }

   _available.add(double(available));

   // In the case when a concurrent GC cycle completes successfully but with an
   // unusually small amount of available memory we will adjust our trigger
   // parameters so that they are more likely to initiate a new cycle.
   // Conversely, when a GC cycle results in an above average amount of available
   // memory, we will adjust the trigger parameters to be less likely to initiate
   // a GC cycle.
   //
   // The z-score we've computed is in no way statistically related to the
   // trigger parameters, but it has the nice property that worse z-scores for
   // available memory indicate making larger adjustments to the trigger
   // parameters. It also results in fewer adjustments as the application
   // stabilizes.
   //
   // In order to avoid making endless and likely unnecessary adjustments to the
   // trigger parameters, the change in available memory (with respect to the
   // average) at the end of a cycle must be beyond these threshold values.
   if (z_score < LOWEST_EXPECTED_AVAILABLE_AT_END ||
       z_score > HIGHEST_EXPECTED_AVAILABLE_AT_END) {
     // The sign is flipped because a negative z-score indicates that the
     // available memory at the end of the cycle is below average. Positive
     // adjustments make the triggers more sensitive (i.e., more likely to fire).
     // The z-score also gives us a measure of just how far below normal. This
     // property allows us to adjust the trigger parameters proportionally.
     //
     // The `100` here is used to attenuate the size of our adjustments. This
     // number was chosen empirically. It also means the adjustments at the end of
     // a concurrent cycle are an order of magnitude smaller than the adjustments
     // made for a degenerated or full GC cycle (which themselves were also
     // chosen empirically).
     adjust_last_trigger_parameters(z_score / -100);
   }
 }

 void ShenandoahAdaptiveHeuristics::record_success_degenerated() {
   ShenandoahHeuristics::record_success_degenerated();
   // Adjust both trigger's parameters in the case of a degenerated GC because
   // either of them should have triggered earlier to avoid this case.
   adjust_margin_of_error(DEGENERATE_PENALTY_SD);
   adjust_spike_threshold(DEGENERATE_PENALTY_SD);
 }

 void ShenandoahAdaptiveHeuristics::record_success_full() {
   ShenandoahHeuristics::record_success_full();
   // Adjust both trigger's parameters in the case of a full GC because
   // either of them should have triggered earlier to avoid this case.
   adjust_margin_of_error(FULL_PENALTY_SD);
   adjust_spike_threshold(FULL_PENALTY_SD);
 }

 static double saturate(double value, double min, double max) {
   return MAX2(MIN2(value, max), min);
 }

 //  Rationale:
 //    The idea is that there is an average allocation rate and there are occasional abnormal bursts (or spikes) of
 //    allocations that exceed the average allocation rate.  What do these spikes look like?
 //
 //    1. At certain phase changes, we may discard large amounts of data and replace it with large numbers of newly
 //       allocated objects.  This "spike" looks more like a phase change.  We were in steady state at M bytes/sec
 //       allocation rate and now we're in a "reinitialization phase" that looks like N bytes/sec.  We need the "spike"
 //       accommodation to give us enough runway to recalibrate our "average allocation rate".
 //
 //   2. The typical workload changes.  "Suddenly", our typical workload of N TPS increases to N+delta TPS.  This means
 //       our average allocation rate needs to be adjusted.  Once again, we need the "spike" accomodation to give us
 //       enough runway to recalibrate our "average allocation rate".
 //
 //    3. Though there is an "average" allocation rate, a given workload's demand for allocation may be very bursty.  We
 //       allocate a bunch of LABs during the 5 ms that follow completion of a GC, then we perform no more allocations for
 //       the next 150 ms.  It seems we want the "spike" to represent the maximum divergence from average within the
 //       period of time between consecutive evaluation of the should_start_gc() service.  Here's the thinking:
 //
 //       a) Between now and the next time I ask whether should_start_gc(), we might experience a spike representing
 //          the anticipated burst of allocations.  If that would put us over budget, then we should start GC immediately.
 //       b) Between now and the anticipated depletion of allocation pool, there may be two or more bursts of allocations.
 //          If there are more than one of these bursts, we can "approximate" that these will be separated by spans of
 //          time with very little or no allocations so the "average" allocation rate should be a suitable approximation
 //          of how this will behave.
 //
 //    For cases 1 and 2, we need to "quickly" recalibrate the average allocation rate whenever we detect a change
 //    in operation mode.  We want some way to decide that the average rate has changed, while keeping average
 //    allocation rate computation independent.
 bool ShenandoahAdaptiveHeuristics::should_start_gc() {
   size_t capacity = _space_info->soft_max_capacity();
   size_t available = _space_info->soft_available();
   size_t allocated = _space_info->bytes_allocated_since_gc_start();

   log_debug(gc)("should_start_gc? available: %zu, soft_max_capacity: %zu"
                 ", allocated: %zu", available, capacity, allocated);

   if (_start_gc_is_pending) {
     log_trigger("GC start is already pending");
     return true;
   }

   // Track allocation rate even if we decide to start a cycle for other reasons.
   double rate = _allocation_rate.sample(allocated);
   _last_trigger = OTHER;

   size_t min_threshold = min_free_threshold();
   if (available < min_threshold) {
     log_trigger("Free (%zu%s) is below minimum threshold (%zu%s)",
                  byte_size_in_proper_unit(available), proper_unit_for_byte_size(available),
                  byte_size_in_proper_unit(min_threshold), proper_unit_for_byte_size(min_threshold));
     accept_trigger_with_type(OTHER);
     return true;
   }

   // Check if we need to learn a bit about the application
   const size_t max_learn = ShenandoahLearningSteps;
   if (_gc_times_learned < max_learn) {
     size_t init_threshold = capacity / 100 * ShenandoahInitFreeThreshold;
     if (available < init_threshold) {
       log_trigger("Learning %zu of %zu. Free (%zu%s) is below initial threshold (%zu%s)",
                    _gc_times_learned + 1, max_learn,
                    byte_size_in_proper_unit(available), proper_unit_for_byte_size(available),
                    byte_size_in_proper_unit(init_threshold), proper_unit_for_byte_size(init_threshold));
       accept_trigger_with_type(OTHER);
       return true;
     }
   }
   // Check if allocation headroom is still okay. This also factors in:
   //   1. Some space to absorb allocation spikes (ShenandoahAllocSpikeFactor)
   //   2. Accumulated penalties from Degenerated and Full GC
   size_t allocation_headroom = available;

   size_t spike_headroom = capacity / 100 * ShenandoahAllocSpikeFactor;
   size_t penalties      = capacity / 100 * _gc_time_penalties;

   allocation_headroom -= MIN2(allocation_headroom, spike_headroom);
   allocation_headroom -= MIN2(allocation_headroom, penalties);

   double avg_cycle_time = _gc_cycle_time_history->davg() + (_margin_of_error_sd * _gc_cycle_time_history->dsd());
   double avg_alloc_rate = _allocation_rate.upper_bound(_margin_of_error_sd);

   log_debug(gc)("average GC time: %.2f ms, allocation rate: %.0f %s/s",
           avg_cycle_time * 1000, byte_size_in_proper_unit(avg_alloc_rate), proper_unit_for_byte_size(avg_alloc_rate));
   if (avg_cycle_time * avg_alloc_rate > allocation_headroom) {
     log_trigger("Average GC time (%.2f ms) is above the time for average allocation rate (%.0f %sB/s)"
                  " to deplete free headroom (%zu%s) (margin of error = %.2f)",
                  avg_cycle_time * 1000,
                  byte_size_in_proper_unit(avg_alloc_rate), proper_unit_for_byte_size(avg_alloc_rate),
                  byte_size_in_proper_unit(allocation_headroom), proper_unit_for_byte_size(allocation_headroom),
                  _margin_of_error_sd);
     log_info(gc, ergo)("Free headroom: %zu%s (free) - %zu%s (spike) - %zu%s (penalties) = %zu%s",
                        byte_size_in_proper_unit(available),           proper_unit_for_byte_size(available),
                        byte_size_in_proper_unit(spike_headroom),      proper_unit_for_byte_size(spike_headroom),
                        byte_size_in_proper_unit(penalties),           proper_unit_for_byte_size(penalties),
                        byte_size_in_proper_unit(allocation_headroom), proper_unit_for_byte_size(allocation_headroom));
     accept_trigger_with_type(RATE);
     return true;
   }

   bool is_spiking = _allocation_rate.is_spiking(rate, _spike_threshold_sd);
   if (is_spiking && avg_cycle_time > allocation_headroom / rate) {
     log_trigger("Average GC time (%.2f ms) is above the time for instantaneous allocation rate (%.0f %sB/s) to deplete free headroom (%zu%s) (spike threshold = %.2f)",
                  avg_cycle_time * 1000,
                  byte_size_in_proper_unit(rate), proper_unit_for_byte_size(rate),
                  byte_size_in_proper_unit(allocation_headroom), proper_unit_for_byte_size(allocation_headroom),
                  _spike_threshold_sd);
     accept_trigger_with_type(SPIKE);
     return true;
   }

   if (ShenandoahHeuristics::should_start_gc()) {
     _start_gc_is_pending = true;
     return true;
   } else {
     return false;
   }
 }

 void ShenandoahAdaptiveHeuristics::adjust_last_trigger_parameters(double amount) {
   switch (_last_trigger) {
     case RATE:
       adjust_margin_of_error(amount);
       break;
     case SPIKE:
       adjust_spike_threshold(amount);
       break;
     case OTHER:
       // nothing to adjust here.
       break;
     default:
       ShouldNotReachHere();
   }
 }

 void ShenandoahAdaptiveHeuristics::adjust_margin_of_error(double amount) {
   _margin_of_error_sd = saturate(_margin_of_error_sd + amount, MINIMUM_CONFIDENCE, MAXIMUM_CONFIDENCE);
   log_debug(gc, ergo)("Margin of error now %.2f", _margin_of_error_sd);
 }

 void ShenandoahAdaptiveHeuristics::adjust_spike_threshold(double amount) {
   _spike_threshold_sd = saturate(_spike_threshold_sd - amount, MINIMUM_CONFIDENCE, MAXIMUM_CONFIDENCE);
   log_debug(gc, ergo)("Spike threshold now: %.2f", _spike_threshold_sd);
 }

 size_t ShenandoahAdaptiveHeuristics::min_free_threshold() {
   // Note that soft_max_capacity() / 100 * min_free_threshold is smaller than max_capacity() / 100 * min_free_threshold.
   // We want to behave conservatively here, so use max_capacity().  By returning a larger value, we cause the GC to
   // trigger when the remaining amount of free shrinks below the larger threshold.
   return _space_info->max_capacity() / 100 * ShenandoahMinFreeThreshold;
 }

 ShenandoahAllocationRate::ShenandoahAllocationRate() :
   _last_sample_time(os::elapsedTime()),
   _last_sample_value(0),
   _interval_sec(1.0 / ShenandoahAdaptiveSampleFrequencyHz),
   _rate(int(ShenandoahAdaptiveSampleSizeSeconds * ShenandoahAdaptiveSampleFrequencyHz), ShenandoahAdaptiveDecayFactor),
   _rate_avg(int(ShenandoahAdaptiveSampleSizeSeconds * ShenandoahAdaptiveSampleFrequencyHz), ShenandoahAdaptiveDecayFactor) {
 }

 double ShenandoahAllocationRate::sample(size_t allocated) {
   double now = os::elapsedTime();
   double rate = 0.0;
   if (now - _last_sample_time > _interval_sec) {
     if (allocated >= _last_sample_value) {
       rate = instantaneous_rate(now, allocated);
       _rate.add(rate);
       _rate_avg.add(_rate.avg());
     }

     _last_sample_time = now;
     _last_sample_value = allocated;
   }
   return rate;
 }

 double ShenandoahAllocationRate::upper_bound(double sds) const {
   // Here we are using the standard deviation of the computed running
   // average, rather than the standard deviation of the samples that went
   // into the moving average. This is a much more stable value and is tied
   // to the actual statistic in use (moving average over samples of averages).
   return _rate.davg() + (sds * _rate_avg.dsd());
 }

 void ShenandoahAllocationRate::allocation_counter_reset() {
   _last_sample_time = os::elapsedTime();
   _last_sample_value = 0;
 }

 bool ShenandoahAllocationRate::is_spiking(double rate, double threshold) const {
   if (rate <= 0.0) {
     return false;
   }

   double sd = _rate.sd();
   if (sd > 0) {
     // There is a small chance that that rate has already been sampled, but it
     // seems not to matter in practice.
     double z_score = (rate - _rate.avg()) / sd;
     if (z_score > threshold) {
       return true;
     }
   }
   return false;
 }

 double ShenandoahAllocationRate::instantaneous_rate(double time, size_t allocated) const {
   size_t last_value = _last_sample_value;
   double last_time = _last_sample_time;
   size_t allocation_delta = (allocated > last_value) ? (allocated - last_value) : 0;
   double time_delta_sec = time - last_time;
   return (time_delta_sec > 0)  ? (allocation_delta / time_delta_sec) : 0;
 }
	/*
	* Copyright (c) 2018, 2019, Red Hat, Inc. All rights reserved.
	* Copyright Amazon.com Inc. or its affiliates. All Rights Reserved.
	* Copyright (c) 2025, Oracle and/or its affiliates. All rights reserved.
	* DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
	*
	* This code is free software; you can redistribute it and/or modify it
	* under the terms of the GNU General Public License version 2 only, as
	* published by the Free Software Foundation.
	*
	* This code is distributed in the hope that it will be useful, but WITHOUT
	* ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
	* FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
	* version 2 for more details (a copy is included in the LICENSE file that
	* accompanied this code).
	*
	* You should have received a copy of the GNU General Public License version
	* 2 along with this work; if not, write to the Free Software Foundation,
	* Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
	*
	* Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA
	* or visit www.oracle.com if you need additional information or have any
	* questions.
	*
	*/


	#include "gc/shared/gcCause.hpp"
	#include "gc/shenandoah/heuristics/shenandoahAdaptiveHeuristics.hpp"
	#include "gc/shenandoah/heuristics/shenandoahHeuristics.hpp"
	#include "gc/shenandoah/heuristics/shenandoahSpaceInfo.hpp"
	#include "gc/shenandoah/shenandoahCollectionSet.hpp"
	#include "gc/shenandoah/shenandoahCollectorPolicy.hpp"
	#include "gc/shenandoah/shenandoahFreeSet.hpp"
	#include "gc/shenandoah/shenandoahHeap.inline.hpp"
	#include "gc/shenandoah/shenandoahHeapRegion.inline.hpp"
	#include "logging/log.hpp"
	#include "logging/logTag.hpp"
	#include "runtime/globals.hpp"
	#include "utilities/quickSort.hpp"

	// These constants are used to adjust the margin of error for the moving
	// average of the allocation rate and cycle time. The units are standard
	// deviations.
	const double ShenandoahAdaptiveHeuristics::FULL_PENALTY_SD = 0.2;
	const double ShenandoahAdaptiveHeuristics::DEGENERATE_PENALTY_SD = 0.1;

	// These are used to decide if we want to make any adjustments at all
	// at the end of a successful concurrent cycle.
	const double ShenandoahAdaptiveHeuristics::LOWEST_EXPECTED_AVAILABLE_AT_END = -0.5;
	const double ShenandoahAdaptiveHeuristics::HIGHEST_EXPECTED_AVAILABLE_AT_END = 0.5;

	// These values are the confidence interval expressed as standard deviations.
	// At the minimum confidence level, there is a 25% chance that the true value of
	// the estimate (average cycle time or allocation rate) is not more than
	// MINIMUM_CONFIDENCE standard deviations away from our estimate. Similarly, the
	// MAXIMUM_CONFIDENCE interval here means there is a one in a thousand chance
	// that the true value of our estimate is outside the interval. These are used
	// as bounds on the adjustments applied at the outcome of a GC cycle.
	const double ShenandoahAdaptiveHeuristics::MINIMUM_CONFIDENCE = 0.319; // 25%
	const double ShenandoahAdaptiveHeuristics::MAXIMUM_CONFIDENCE = 3.291; // 99.9%

	ShenandoahAdaptiveHeuristics::ShenandoahAdaptiveHeuristics(ShenandoahSpaceInfo* space_info) :
	ShenandoahHeuristics(space_info),
	_margin_of_error_sd(ShenandoahAdaptiveInitialConfidence),
	_spike_threshold_sd(ShenandoahAdaptiveInitialSpikeThreshold),
	_last_trigger(OTHER),
	_available(Moving_Average_Samples, ShenandoahAdaptiveDecayFactor) { }

	ShenandoahAdaptiveHeuristics::~ShenandoahAdaptiveHeuristics() {}

	void ShenandoahAdaptiveHeuristics::choose_collection_set_from_regiondata(ShenandoahCollectionSet* cset,
	RegionData* data, size_t size,
	size_t actual_free) {
	size_t garbage_threshold = ShenandoahHeapRegion::region_size_bytes() * ShenandoahGarbageThreshold / 100;

	// The logic for cset selection in adaptive is as follows:
	//
	// 1. We cannot get cset larger than available free space. Otherwise we guarantee OOME
	// during evacuation, and thus guarantee full GC. In practice, we also want to let
	// application to allocate something. This is why we limit CSet to some fraction of
	// available space. In non-overloaded heap, max_cset would contain all plausible candidates
	// over garbage threshold.
	//
	// 2. We should not get cset too low so that free threshold would not be met right
	// after the cycle. Otherwise we get back-to-back cycles for no reason if heap is
	// too fragmented. In non-overloaded non-fragmented heap min_garbage would be around zero.
	//
	// Therefore, we start by sorting the regions by garbage. Then we unconditionally add the best candidates
	// before we meet min_garbage. Then we add all candidates that fit with a garbage threshold before
	// we hit max_cset. When max_cset is hit, we terminate the cset selection. Note that in this scheme,
	// ShenandoahGarbageThreshold is the soft threshold which would be ignored until min_garbage is hit.

	size_t capacity = _space_info->soft_max_capacity();
	size_t max_cset = (size_t)((1.0 * capacity / 100 * ShenandoahEvacReserve) / ShenandoahEvacWaste);
	size_t free_target = (capacity / 100 * ShenandoahMinFreeThreshold) + max_cset;
	size_t min_garbage = (free_target > actual_free ? (free_target - actual_free) : 0);

	log_info(gc, ergo)("Adaptive CSet Selection. Target Free: %zu%s, Actual Free: "
	"%zu%s, Max Evacuation: %zu%s, Min Garbage: %zu%s",
	byte_size_in_proper_unit(free_target), proper_unit_for_byte_size(free_target),
	byte_size_in_proper_unit(actual_free), proper_unit_for_byte_size(actual_free),
	byte_size_in_proper_unit(max_cset), proper_unit_for_byte_size(max_cset),
	byte_size_in_proper_unit(min_garbage), proper_unit_for_byte_size(min_garbage));

	// Better select garbage-first regions
	QuickSort::sort(data, size, compare_by_garbage);

	size_t cur_cset = 0;
	size_t cur_garbage = 0;

	for (size_t idx = 0; idx < size; idx++) {
	ShenandoahHeapRegion* r = data[idx].get_region();

	size_t new_cset = cur_cset + r->get_live_data_bytes();
	size_t new_garbage = cur_garbage + r->garbage();

	if (new_cset > max_cset) {
	break;
	}

	if ((new_garbage < min_garbage) \|\| (r->garbage() > garbage_threshold)) {
	cset->add_region(r);
	cur_cset = new_cset;
	cur_garbage = new_garbage;
	}
	}
	}

	void ShenandoahAdaptiveHeuristics::record_cycle_start() {
	ShenandoahHeuristics::record_cycle_start();
	_allocation_rate.allocation_counter_reset();
	}

	void ShenandoahAdaptiveHeuristics::record_success_concurrent() {
	ShenandoahHeuristics::record_success_concurrent();

	size_t available = _space_info->available();

	double z_score = 0.0;
	double available_sd = _available.sd();
	if (available_sd > 0) {
	double available_avg = _available.avg();
	z_score = (double(available) - available_avg) / available_sd;
	log_debug(gc, ergo)("Available: %zu %sB, z-score=%.3f. Average available: %.1f %sB +/- %.1f %sB.",
	byte_size_in_proper_unit(available), proper_unit_for_byte_size(available),
	z_score,
	byte_size_in_proper_unit(available_avg), proper_unit_for_byte_size(available_avg),
	byte_size_in_proper_unit(available_sd), proper_unit_for_byte_size(available_sd));
	}

	_available.add(double(available));

	// In the case when a concurrent GC cycle completes successfully but with an
	// unusually small amount of available memory we will adjust our trigger
	// parameters so that they are more likely to initiate a new cycle.
	// Conversely, when a GC cycle results in an above average amount of available
	// memory, we will adjust the trigger parameters to be less likely to initiate
	// a GC cycle.
	//
	// The z-score we've computed is in no way statistically related to the
	// trigger parameters, but it has the nice property that worse z-scores for
	// available memory indicate making larger adjustments to the trigger
	// parameters. It also results in fewer adjustments as the application
	// stabilizes.
	//
	// In order to avoid making endless and likely unnecessary adjustments to the
	// trigger parameters, the change in available memory (with respect to the
	// average) at the end of a cycle must be beyond these threshold values.
	if (z_score < LOWEST_EXPECTED_AVAILABLE_AT_END \|\|
	z_score > HIGHEST_EXPECTED_AVAILABLE_AT_END) {
	// The sign is flipped because a negative z-score indicates that the
	// available memory at the end of the cycle is below average. Positive
	// adjustments make the triggers more sensitive (i.e., more likely to fire).
	// The z-score also gives us a measure of just how far below normal. This
	// property allows us to adjust the trigger parameters proportionally.
	//
	// The `100` here is used to attenuate the size of our adjustments. This
	// number was chosen empirically. It also means the adjustments at the end of
	// a concurrent cycle are an order of magnitude smaller than the adjustments
	// made for a degenerated or full GC cycle (which themselves were also
	// chosen empirically).
	adjust_last_trigger_parameters(z_score / -100);
	}
	}

	void ShenandoahAdaptiveHeuristics::record_success_degenerated() {
	ShenandoahHeuristics::record_success_degenerated();
	// Adjust both trigger's parameters in the case of a degenerated GC because
	// either of them should have triggered earlier to avoid this case.
	adjust_margin_of_error(DEGENERATE_PENALTY_SD);
	adjust_spike_threshold(DEGENERATE_PENALTY_SD);
	}

	void ShenandoahAdaptiveHeuristics::record_success_full() {
	ShenandoahHeuristics::record_success_full();
	// Adjust both trigger's parameters in the case of a full GC because
	// either of them should have triggered earlier to avoid this case.
	adjust_margin_of_error(FULL_PENALTY_SD);
	adjust_spike_threshold(FULL_PENALTY_SD);
	}

	static double saturate(double value, double min, double max) {
	return MAX2(MIN2(value, max), min);
	}

	// Rationale:
	// The idea is that there is an average allocation rate and there are occasional abnormal bursts (or spikes) of
	// allocations that exceed the average allocation rate. What do these spikes look like?
	//
	// 1. At certain phase changes, we may discard large amounts of data and replace it with large numbers of newly
	// allocated objects. This "spike" looks more like a phase change. We were in steady state at M bytes/sec
	// allocation rate and now we're in a "reinitialization phase" that looks like N bytes/sec. We need the "spike"
	// accommodation to give us enough runway to recalibrate our "average allocation rate".
	//
	// 2. The typical workload changes. "Suddenly", our typical workload of N TPS increases to N+delta TPS. This means
	// our average allocation rate needs to be adjusted. Once again, we need the "spike" accomodation to give us
	// enough runway to recalibrate our "average allocation rate".
	//
	// 3. Though there is an "average" allocation rate, a given workload's demand for allocation may be very bursty. We
	// allocate a bunch of LABs during the 5 ms that follow completion of a GC, then we perform no more allocations for
	// the next 150 ms. It seems we want the "spike" to represent the maximum divergence from average within the
	// period of time between consecutive evaluation of the should_start_gc() service. Here's the thinking:
	//
	// a) Between now and the next time I ask whether should_start_gc(), we might experience a spike representing
	// the anticipated burst of allocations. If that would put us over budget, then we should start GC immediately.
	// b) Between now and the anticipated depletion of allocation pool, there may be two or more bursts of allocations.
	// If there are more than one of these bursts, we can "approximate" that these will be separated by spans of
	// time with very little or no allocations so the "average" allocation rate should be a suitable approximation
	// of how this will behave.
	//
	// For cases 1 and 2, we need to "quickly" recalibrate the average allocation rate whenever we detect a change
	// in operation mode. We want some way to decide that the average rate has changed, while keeping average
	// allocation rate computation independent.
	bool ShenandoahAdaptiveHeuristics::should_start_gc() {
	size_t capacity = _space_info->soft_max_capacity();
	size_t available = _space_info->soft_available();
	size_t allocated = _space_info->bytes_allocated_since_gc_start();

	log_debug(gc)("should_start_gc? available: %zu, soft_max_capacity: %zu"
	", allocated: %zu", available, capacity, allocated);

	if (_start_gc_is_pending) {
	log_trigger("GC start is already pending");
	return true;
	}

	// Track allocation rate even if we decide to start a cycle for other reasons.
	double rate = _allocation_rate.sample(allocated);
	_last_trigger = OTHER;

	size_t min_threshold = min_free_threshold();
	if (available < min_threshold) {
	log_trigger("Free (%zu%s) is below minimum threshold (%zu%s)",
	byte_size_in_proper_unit(available), proper_unit_for_byte_size(available),
	byte_size_in_proper_unit(min_threshold), proper_unit_for_byte_size(min_threshold));
	accept_trigger_with_type(OTHER);
	return true;
	}

	// Check if we need to learn a bit about the application
	const size_t max_learn = ShenandoahLearningSteps;
	if (_gc_times_learned < max_learn) {
	size_t init_threshold = capacity / 100 * ShenandoahInitFreeThreshold;
	if (available < init_threshold) {
	log_trigger("Learning %zu of %zu. Free (%zu%s) is below initial threshold (%zu%s)",
	_gc_times_learned + 1, max_learn,
	byte_size_in_proper_unit(available), proper_unit_for_byte_size(available),
	byte_size_in_proper_unit(init_threshold), proper_unit_for_byte_size(init_threshold));
	accept_trigger_with_type(OTHER);
	return true;
	}
	}
	// Check if allocation headroom is still okay. This also factors in:
	// 1. Some space to absorb allocation spikes (ShenandoahAllocSpikeFactor)
	// 2. Accumulated penalties from Degenerated and Full GC
	size_t allocation_headroom = available;

	size_t spike_headroom = capacity / 100 * ShenandoahAllocSpikeFactor;
	size_t penalties = capacity / 100 * _gc_time_penalties;

	allocation_headroom -= MIN2(allocation_headroom, spike_headroom);
	allocation_headroom -= MIN2(allocation_headroom, penalties);

	double avg_cycle_time = _gc_cycle_time_history->davg() + (_margin_of_error_sd * _gc_cycle_time_history->dsd());
	double avg_alloc_rate = _allocation_rate.upper_bound(_margin_of_error_sd);

	log_debug(gc)("average GC time: %.2f ms, allocation rate: %.0f %s/s",
	avg_cycle_time * 1000, byte_size_in_proper_unit(avg_alloc_rate), proper_unit_for_byte_size(avg_alloc_rate));
	if (avg_cycle_time * avg_alloc_rate > allocation_headroom) {
	log_trigger("Average GC time (%.2f ms) is above the time for average allocation rate (%.0f %sB/s)"
	" to deplete free headroom (%zu%s) (margin of error = %.2f)",
	avg_cycle_time * 1000,
	byte_size_in_proper_unit(avg_alloc_rate), proper_unit_for_byte_size(avg_alloc_rate),
	byte_size_in_proper_unit(allocation_headroom), proper_unit_for_byte_size(allocation_headroom),
	_margin_of_error_sd);
	log_info(gc, ergo)("Free headroom: %zu%s (free) - %zu%s (spike) - %zu%s (penalties) = %zu%s",
	byte_size_in_proper_unit(available), proper_unit_for_byte_size(available),
	byte_size_in_proper_unit(spike_headroom), proper_unit_for_byte_size(spike_headroom),
	byte_size_in_proper_unit(penalties), proper_unit_for_byte_size(penalties),
	byte_size_in_proper_unit(allocation_headroom), proper_unit_for_byte_size(allocation_headroom));
	accept_trigger_with_type(RATE);
	return true;
	}

	bool is_spiking = _allocation_rate.is_spiking(rate, _spike_threshold_sd);
	if (is_spiking && avg_cycle_time > allocation_headroom / rate) {
	log_trigger("Average GC time (%.2f ms) is above the time for instantaneous allocation rate (%.0f %sB/s) to deplete free headroom (%zu%s) (spike threshold = %.2f)",
	avg_cycle_time * 1000,
	byte_size_in_proper_unit(rate), proper_unit_for_byte_size(rate),
	byte_size_in_proper_unit(allocation_headroom), proper_unit_for_byte_size(allocation_headroom),
	_spike_threshold_sd);
	accept_trigger_with_type(SPIKE);
	return true;
	}

	if (ShenandoahHeuristics::should_start_gc()) {
	_start_gc_is_pending = true;
	return true;
	} else {
	return false;
	}
	}

	void ShenandoahAdaptiveHeuristics::adjust_last_trigger_parameters(double amount) {
	switch (_last_trigger) {
	case RATE:
	adjust_margin_of_error(amount);
	break;
	case SPIKE:
	adjust_spike_threshold(amount);
	break;
	case OTHER:
	// nothing to adjust here.
	break;
	default:
	ShouldNotReachHere();
	}
	}

	void ShenandoahAdaptiveHeuristics::adjust_margin_of_error(double amount) {
	_margin_of_error_sd = saturate(_margin_of_error_sd + amount, MINIMUM_CONFIDENCE, MAXIMUM_CONFIDENCE);
	log_debug(gc, ergo)("Margin of error now %.2f", _margin_of_error_sd);
	}

	void ShenandoahAdaptiveHeuristics::adjust_spike_threshold(double amount) {
	_spike_threshold_sd = saturate(_spike_threshold_sd - amount, MINIMUM_CONFIDENCE, MAXIMUM_CONFIDENCE);
	log_debug(gc, ergo)("Spike threshold now: %.2f", _spike_threshold_sd);
	}

	size_t ShenandoahAdaptiveHeuristics::min_free_threshold() {
	// Note that soft_max_capacity() / 100 * min_free_threshold is smaller than max_capacity() / 100 * min_free_threshold.
	// We want to behave conservatively here, so use max_capacity(). By returning a larger value, we cause the GC to
	// trigger when the remaining amount of free shrinks below the larger threshold.
	return _space_info->max_capacity() / 100 * ShenandoahMinFreeThreshold;
	}

	ShenandoahAllocationRate::ShenandoahAllocationRate() :
	_last_sample_time(os::elapsedTime()),
	_last_sample_value(0),
	_interval_sec(1.0 / ShenandoahAdaptiveSampleFrequencyHz),
	_rate(int(ShenandoahAdaptiveSampleSizeSeconds * ShenandoahAdaptiveSampleFrequencyHz), ShenandoahAdaptiveDecayFactor),
	_rate_avg(int(ShenandoahAdaptiveSampleSizeSeconds * ShenandoahAdaptiveSampleFrequencyHz), ShenandoahAdaptiveDecayFactor) {
	}

	double ShenandoahAllocationRate::sample(size_t allocated) {
	double now = os::elapsedTime();
	double rate = 0.0;
	if (now - _last_sample_time > _interval_sec) {
	if (allocated >= _last_sample_value) {
	rate = instantaneous_rate(now, allocated);
	_rate.add(rate);
	_rate_avg.add(_rate.avg());
	}

	_last_sample_time = now;
	_last_sample_value = allocated;
	}
	return rate;
	}

	double ShenandoahAllocationRate::upper_bound(double sds) const {
	// Here we are using the standard deviation of the computed running
	// average, rather than the standard deviation of the samples that went
	// into the moving average. This is a much more stable value and is tied
	// to the actual statistic in use (moving average over samples of averages).
	return _rate.davg() + (sds * _rate_avg.dsd());
	}

	void ShenandoahAllocationRate::allocation_counter_reset() {
	_last_sample_time = os::elapsedTime();
	_last_sample_value = 0;
	}

	bool ShenandoahAllocationRate::is_spiking(double rate, double threshold) const {
	if (rate <= 0.0) {
	return false;
	}

	double sd = _rate.sd();
	if (sd > 0) {
	// There is a small chance that that rate has already been sampled, but it
	// seems not to matter in practice.
	double z_score = (rate - _rate.avg()) / sd;
	if (z_score > threshold) {
	return true;
	}
	}
	return false;
	}

	double ShenandoahAllocationRate::instantaneous_rate(double time, size_t allocated) const {
	size_t last_value = _last_sample_value;
	double last_time = _last_sample_time;
	size_t allocation_delta = (allocated > last_value) ? (allocated - last_value) : 0;
	double time_delta_sec = time - last_time;
	return (time_delta_sec > 0) ? (allocation_delta / time_delta_sec) : 0;
	}