8255984: Shenandoah: "adaptive" heuristic is prone to missing load spikes

Reviewed-by: shade

src/hotspot/share/gc/shenandoah/heuristics/shenandoahAdaptiveHeuristics.cpp

@@ -27,14 +27,36 @@
 #include "gc/shenandoah/heuristics/shenandoahAdaptiveHeuristics.hpp"
 #include "gc/shenandoah/shenandoahCollectionSet.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 "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() :
-  ShenandoahHeuristics() {}
+  ShenandoahHeuristics(),
+  _margin_of_error_sd(ShenandoahAdaptiveInitialConfidence),
+  _spike_threshold_sd(ShenandoahAdaptiveInitialSpikeThreshold),
+  _last_trigger(OTHER) { }
 
 ShenandoahAdaptiveHeuristics::~ShenandoahAdaptiveHeuristics() {}
 
@@ -98,20 +120,94 @@ void ShenandoahAdaptiveHeuristics::choose_collection_set_from_regiondata(Shenand
 
 void ShenandoahAdaptiveHeuristics::record_cycle_start() {
   ShenandoahHeuristics::record_cycle_start();
+  _allocation_rate.allocation_counter_reset();
 }
 
-bool ShenandoahAdaptiveHeuristics::should_start_gc() const {
+void ShenandoahAdaptiveHeuristics::record_success_concurrent() {
+  ShenandoahHeuristics::record_success_concurrent();
+
+  size_t available = ShenandoahHeap::heap()->free_set()->available();
+
+  _available.add(available);
+  double z_score = 0.0;
+  if (_available.sd() > 0) {
+    z_score = (available - _available.avg()) / _available.sd();
+  }
+
+  log_debug(gc, ergo)("Available: " SIZE_FORMAT " %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()));
+
+  // 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);
+}
+
+bool ShenandoahAdaptiveHeuristics::should_start_gc() {
   ShenandoahHeap* heap = ShenandoahHeap::heap();
   size_t max_capacity = heap->max_capacity();
   size_t capacity = heap->soft_max_capacity();
   size_t available = heap->free_set()->available();
+  size_t allocated = heap->bytes_allocated_since_gc_start();
 
   // Make sure the code below treats available without the soft tail.
   size_t soft_tail = max_capacity - capacity;
   available = (available > soft_tail) ? (available - soft_tail) : 0;
 
-  // Check if we are falling below the worst limit, time to trigger the GC, regardless of
-  // anything else.
+  // 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 = capacity / 100 * ShenandoahMinFreeThreshold;
   if (available < min_threshold) {
     log_info(gc)("Trigger: Free (" SIZE_FORMAT "%s) is below minimum threshold (" SIZE_FORMAT "%s)",
@@ -120,7 +216,6 @@ bool ShenandoahAdaptiveHeuristics::should_start_gc() const {
     return true;
   }
 
-  // Check if are 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;
@@ -136,7 +231,6 @@ bool ShenandoahAdaptiveHeuristics::should_start_gc() const {
   // Check if allocation headroom is still okay. This also factors in:
   //   1. Some space to absorb allocation spikes
   //   2. Accumulated penalties from Degenerated and Full GC
-
   size_t allocation_headroom = available;
 
   size_t spike_headroom = capacity / 100 * ShenandoahAllocSpikeFactor;
@@ -145,24 +239,127 @@ bool ShenandoahAdaptiveHeuristics::should_start_gc() const {
   allocation_headroom -= MIN2(allocation_headroom, spike_headroom);
   allocation_headroom -= MIN2(allocation_headroom, penalties);
 
-  // TODO: Allocation rate is way too averaged to be useful during state changes
+  double avg_cycle_time = _gc_time_history->davg() + (_margin_of_error_sd * _gc_time_history->dsd());
+  double avg_alloc_rate = _allocation_rate.upper_bound(_margin_of_error_sd);
+  if (avg_cycle_time > allocation_headroom / avg_alloc_rate) {
+    log_info(gc)("Trigger: Average GC time (%.2f ms) is above the time for average allocation rate (%.0f %sB/s) to deplete free headroom (" SIZE_FORMAT "%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);
 
-  double average_gc = _gc_time_history->avg();
-  double time_since_last = time_since_last_gc();
-  double allocation_rate = heap->bytes_allocated_since_gc_start() / time_since_last;
-
-  if (average_gc > allocation_headroom / allocation_rate) {
-    log_info(gc)("Trigger: Average GC time (%.2f ms) is above the time for allocation rate (%.0f %sB/s) to deplete free headroom (" SIZE_FORMAT "%s)",
-                 average_gc * 1000,
-                 byte_size_in_proper_unit(allocation_rate),     proper_unit_for_byte_size(allocation_rate),
-                 byte_size_in_proper_unit(allocation_headroom), proper_unit_for_byte_size(allocation_headroom));
     log_info(gc, ergo)("Free headroom: " SIZE_FORMAT "%s (free) - " SIZE_FORMAT "%s (spike) - " SIZE_FORMAT "%s (penalties) = " SIZE_FORMAT "%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));
+                       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));
+
+    _last_trigger = RATE;
+    return true;
+  }
+
+  bool is_spiking = _allocation_rate.is_spiking(rate, _spike_threshold_sd);
+  if (is_spiking && avg_cycle_time > allocation_headroom / rate) {
+    log_info(gc)("Trigger: Average GC time (%.2f ms) is above the time for instantaneous allocation rate (%.0f %sB/s) to deplete free headroom (" SIZE_FORMAT "%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);
+    _last_trigger = SPIKE;
     return true;
   }
 
   return ShenandoahHeuristics::should_start_gc();
 }
+
+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);
+}
+
+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(size_t allocated) const {
+  return instantaneous_rate(os::elapsedTime(), allocated);
+}
+
+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;
+}

src/hotspot/share/gc/shenandoah/heuristics/shenandoahAdaptiveHeuristics.hpp

@@ -29,6 +29,28 @@
 #include "gc/shenandoah/shenandoahPhaseTimings.hpp"
 #include "utilities/numberSeq.hpp"
 
+class ShenandoahAllocationRate : public CHeapObj<mtGC> {
+ public:
+  explicit ShenandoahAllocationRate();
+  void allocation_counter_reset();
+
+  double sample(size_t allocated);
+
+  double instantaneous_rate(size_t allocated) const;
+  double upper_bound(double sds) const;
+  bool is_spiking(double rate, double threshold) const;
+
+ private:
+
+  double instantaneous_rate(double time, size_t allocated) const;
+
+  double _last_sample_time;
+  size_t _last_sample_value;
+  double _interval_sec;
+  TruncatedSeq _rate;
+  TruncatedSeq _rate_avg;
+};
+
 class ShenandoahAdaptiveHeuristics : public ShenandoahHeuristics {
 public:
   ShenandoahAdaptiveHeuristics();
@@ -40,12 +62,70 @@ public:
                                                      size_t actual_free);
 
   void record_cycle_start();
+  void record_success_concurrent();
+  void record_success_degenerated();
+  void record_success_full();
 
-  virtual bool should_start_gc() const;
+  virtual bool should_start_gc();
 
   virtual const char* name()     { return "Adaptive"; }
   virtual bool is_diagnostic()   { return false; }
   virtual bool is_experimental() { return false; }
+
+ private:
+  // These are used to adjust the margin of error and the spike threshold
+  // in response to GC cycle outcomes. These values are shared, but the
+  // margin of error and spike threshold trend in opposite directions.
+  const static double FULL_PENALTY_SD;
+  const static double DEGENERATE_PENALTY_SD;
+
+  const static double MINIMUM_CONFIDENCE;
+  const static double MAXIMUM_CONFIDENCE;
+
+  const static double LOWEST_EXPECTED_AVAILABLE_AT_END;
+  const static double HIGHEST_EXPECTED_AVAILABLE_AT_END;
+
+  friend class ShenandoahAllocationRate;
+
+  // Used to record the last trigger that signaled to start a GC.
+  // This itself is used to decide whether or not to adjust the margin of
+  // error for the average cycle time and allocation rate or the allocation
+  // spike detection threshold.
+  enum Trigger {
+    SPIKE, RATE, OTHER
+  };
+
+  void adjust_last_trigger_parameters(double amount);
+  void adjust_margin_of_error(double amount);
+  void adjust_spike_threshold(double amount);
+
+  ShenandoahAllocationRate _allocation_rate;
+
+  // The margin of error expressed in standard deviations to add to our
+  // average cycle time and allocation rate. As this value increases we
+  // tend to over estimate the rate at which mutators will deplete the
+  // heap. In other words, erring on the side of caution will trigger more
+  // concurrent GCs.
+  double _margin_of_error_sd;
+
+  // The allocation spike threshold is expressed in standard deviations.
+  // If the standard deviation of the most recent sample of the allocation
+  // rate exceeds this threshold, a GC cycle is started. As this value
+  // decreases the sensitivity to allocation spikes increases. In other
+  // words, lowering the spike threshold will tend to increase the number
+  // of concurrent GCs.
+  double _spike_threshold_sd;
+
+  // Remember which trigger is responsible for the last GC cycle. When the
+  // outcome of the cycle is evaluated we will adjust the parameters for the
+  // corresponding triggers. Note that successful outcomes will raise
+  // the spike threshold and lower the margin of error.
+  Trigger _last_trigger;
+
+  // Keep track of the available memory at the end of a GC cycle. This
+  // establishes what is 'normal' for the application and is used as a
+  // source of feedback to adjust trigger parameters.
+  TruncatedSeq _available;
 };
 
 #endif // SHARE_GC_SHENANDOAH_HEURISTICS_SHENANDOAHADAPTIVEHEURISTICS_HPP

src/hotspot/share/gc/shenandoah/heuristics/shenandoahAggressiveHeuristics.cpp

@@ -56,7 +56,7 @@ void ShenandoahAggressiveHeuristics::choose_collection_set_from_regiondata(Shena
   }
 }
 
-bool ShenandoahAggressiveHeuristics::should_start_gc() const {
+bool ShenandoahAggressiveHeuristics::should_start_gc() {
   log_info(gc)("Trigger: Start next cycle immediately");
   return true;
 }

src/hotspot/share/gc/shenandoah/heuristics/shenandoahAggressiveHeuristics.hpp

@@ -35,7 +35,7 @@ public:
                                                      RegionData* data, size_t size,
                                                      size_t free);
 
-  virtual bool should_start_gc() const;
+  virtual bool should_start_gc();
 
   virtual bool should_unload_classes();
 

src/hotspot/share/gc/shenandoah/heuristics/shenandoahCompactHeuristics.cpp

@@ -44,7 +44,7 @@ ShenandoahCompactHeuristics::ShenandoahCompactHeuristics() : ShenandoahHeuristic
   SHENANDOAH_ERGO_OVERRIDE_DEFAULT(ShenandoahGarbageThreshold,     10);
 }
 
-bool ShenandoahCompactHeuristics::should_start_gc() const {
+bool ShenandoahCompactHeuristics::should_start_gc() {
   ShenandoahHeap* heap = ShenandoahHeap::heap();
 
   size_t max_capacity = heap->max_capacity();

src/hotspot/share/gc/shenandoah/heuristics/shenandoahCompactHeuristics.hpp

@@ -31,7 +31,7 @@ class ShenandoahCompactHeuristics : public ShenandoahHeuristics {
 public:
   ShenandoahCompactHeuristics();
 
-  virtual bool should_start_gc() const;
+  virtual bool should_start_gc();
 
   virtual void choose_collection_set_from_regiondata(ShenandoahCollectionSet* cset,
                                                      RegionData* data, size_t size,

src/hotspot/share/gc/shenandoah/heuristics/shenandoahHeuristics.cpp

@@ -50,7 +50,7 @@ ShenandoahHeuristics::ShenandoahHeuristics() :
   _last_cycle_end(0),
   _gc_times_learned(0),
   _gc_time_penalties(0),
-  _gc_time_history(new TruncatedSeq(5)),
+  _gc_time_history(new TruncatedSeq(10, ShenandoahAdaptiveDecayFactor)),
   _metaspace_oom()
 {
   // No unloading during concurrent mark? Communicate that to heuristics
@@ -182,7 +182,7 @@ void ShenandoahHeuristics::record_cycle_end() {
   _last_cycle_end = os::elapsedTime();
 }
 
-bool ShenandoahHeuristics::should_start_gc() const {
+bool ShenandoahHeuristics::should_start_gc() {
   // Perform GC to cleanup metaspace
   if (has_metaspace_oom()) {
     // Some of vmTestbase/metaspace tests depend on following line to count GC cycles

src/hotspot/share/gc/shenandoah/heuristics/shenandoahHeuristics.hpp

@@ -104,7 +104,7 @@ public:
 
   virtual void record_cycle_end();
 
-  virtual bool should_start_gc() const;
+  virtual bool should_start_gc();
 
   virtual bool should_degenerate_cycle();
 

src/hotspot/share/gc/shenandoah/heuristics/shenandoahPassiveHeuristics.cpp

@@ -31,7 +31,7 @@
 #include "logging/log.hpp"
 #include "logging/logTag.hpp"
 
-bool ShenandoahPassiveHeuristics::should_start_gc() const {
+bool ShenandoahPassiveHeuristics::should_start_gc() {
   // Never do concurrent GCs.
   return false;
 }

src/hotspot/share/gc/shenandoah/heuristics/shenandoahPassiveHeuristics.hpp

@@ -29,7 +29,7 @@
 
 class ShenandoahPassiveHeuristics : public ShenandoahHeuristics {
 public:
-  virtual bool should_start_gc() const;
+  virtual bool should_start_gc();
 
   virtual bool should_unload_classes();
 

src/hotspot/share/gc/shenandoah/heuristics/shenandoahStaticHeuristics.cpp

@@ -39,7 +39,7 @@ ShenandoahStaticHeuristics::ShenandoahStaticHeuristics() : ShenandoahHeuristics(
 
 ShenandoahStaticHeuristics::~ShenandoahStaticHeuristics() {}
 
-bool ShenandoahStaticHeuristics::should_start_gc() const {
+bool ShenandoahStaticHeuristics::should_start_gc() {
   ShenandoahHeap* heap = ShenandoahHeap::heap();
 
   size_t max_capacity = heap->max_capacity();

src/hotspot/share/gc/shenandoah/heuristics/shenandoahStaticHeuristics.hpp

@@ -33,7 +33,7 @@ public:
 
   virtual ~ShenandoahStaticHeuristics();
 
-  virtual bool should_start_gc() const;
+  virtual bool should_start_gc();
 
   virtual void choose_collection_set_from_regiondata(ShenandoahCollectionSet* cset,
                                                      RegionData* data, size_t size,

src/hotspot/share/gc/shenandoah/shenandoah_globals.hpp

@@ -127,6 +127,34 @@
           "to 100 effectively disables the shortcut.")                      \
           range(0,100)                                                      \
                                                                             \
+  product(uintx, ShenandoahAdaptiveSampleFrequencyHz, 10, EXPERIMENTAL,     \
+          "The number of times per second to update the allocation rate "   \
+          "moving average.")                                                \
+                                                                            \
+  product(uintx, ShenandoahAdaptiveSampleSizeSeconds, 10, EXPERIMENTAL,     \
+          "The size of the moving window over which the average "           \
+          "allocation rate is maintained. The total number of samples "     \
+          "is the product of this number and the sample frequency.")        \
+                                                                            \
+  product(double, ShenandoahAdaptiveInitialConfidence, 1.8, EXPERIMENTAL,   \
+          "The number of standard deviations used to determine an initial " \
+          "margin of error for the average cycle time and average "         \
+          "allocation rate. Increasing this value will cause the "          \
+          "heuristic to initiate more concurrent cycles." )                 \
+                                                                            \
+  product(double, ShenandoahAdaptiveInitialSpikeThreshold, 1.8, EXPERIMENTAL, \
+          "If the most recently sampled allocation rate is more than "      \
+          "this many standard deviations away from the moving average, "    \
+          "then a cycle is initiated. This value controls how sensitive "   \
+          "the heuristic is to allocation spikes. Decreasing this number "  \
+          "increases the sensitivity. ")                                    \
+                                                                            \
+  product(double, ShenandoahAdaptiveDecayFactor, 0.5, EXPERIMENTAL,         \
+          "The decay factor (alpha) used for values in the weighted "       \
+          "moving average of cycle time and allocation rate. "              \
+          "Larger values give more weight to recent values.")               \
+          range(0,1.0)                                                      \
+                                                                            \
   product(uintx, ShenandoahGuaranteedGCInterval, 5*60*1000, EXPERIMENTAL,   \
           "Many heuristics would guarantee a concurrent GC cycle at "       \
           "least with this interval. This is useful when large idle "       \