1 /*
2 * Copyright (c) 2018, 2019, Red Hat, Inc. All rights reserved.
3 * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
4 *
5 * This code is free software; you can redistribute it and/or modify it
6 * under the terms of the GNU General Public License version 2 only, as
7 * published by the Free Software Foundation.
8 *
9 * This code is distributed in the hope that it will be useful, but WITHOUT
10 * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
11 * FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
12 * version 2 for more details (a copy is included in the LICENSE file that
13 * accompanied this code).
14 *
15 * You should have received a copy of the GNU General Public License version
16 * 2 along with this work; if not, write to the Free Software Foundation,
17 * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
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19 * Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA
20 * or visit www.oracle.com if you need additional information or have any
21 * questions.
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23 */
24
25 #include "precompiled.hpp"
26
27 #include "gc/shenandoah/heuristics/shenandoahAdaptiveHeuristics.hpp"
28 #include "gc/shenandoah/shenandoahCollectionSet.hpp"
29 #include "gc/shenandoah/shenandoahFreeSet.hpp"
30 #include "gc/shenandoah/shenandoahGeneration.hpp"
31 #include "gc/shenandoah/shenandoahHeapRegion.inline.hpp"
32 #include "gc/shenandoah/shenandoahYoungGeneration.hpp"
33 #include "logging/log.hpp"
34 #include "logging/logTag.hpp"
35 #include "utilities/quickSort.hpp"
36
37 // These constants are used to adjust the margin of error for the moving
38 // average of the allocation rate and cycle time. The units are standard
39 // deviations.
40 const double ShenandoahAdaptiveHeuristics::FULL_PENALTY_SD = 0.2;
41 const double ShenandoahAdaptiveHeuristics::DEGENERATE_PENALTY_SD = 0.1;
42
43 // These are used to decide if we want to make any adjustments at all
44 // at the end of a successful concurrent cycle.
45 const double ShenandoahAdaptiveHeuristics::LOWEST_EXPECTED_AVAILABLE_AT_END = -0.5;
46 const double ShenandoahAdaptiveHeuristics::HIGHEST_EXPECTED_AVAILABLE_AT_END = 0.5;
47
48 // These values are the confidence interval expressed as standard deviations.
49 // At the minimum confidence level, there is a 25% chance that the true value of
50 // the estimate (average cycle time or allocation rate) is not more than
51 // MINIMUM_CONFIDENCE standard deviations away from our estimate. Similarly, the
52 // MAXIMUM_CONFIDENCE interval here means there is a one in a thousand chance
53 // that the true value of our estimate is outside the interval. These are used
54 // as bounds on the adjustments applied at the outcome of a GC cycle.
55 const double ShenandoahAdaptiveHeuristics::MINIMUM_CONFIDENCE = 0.319; // 25%
56 const double ShenandoahAdaptiveHeuristics::MAXIMUM_CONFIDENCE = 3.291; // 99.9%
57
58 ShenandoahAdaptiveHeuristics::ShenandoahAdaptiveHeuristics(ShenandoahGeneration* generation) :
59 ShenandoahHeuristics(generation),
60 _margin_of_error_sd(ShenandoahAdaptiveInitialConfidence),
61 _spike_threshold_sd(ShenandoahAdaptiveInitialSpikeThreshold),
62 _last_trigger(OTHER) { }
63
64 ShenandoahAdaptiveHeuristics::~ShenandoahAdaptiveHeuristics() {}
65
66 void ShenandoahAdaptiveHeuristics::choose_collection_set_from_regiondata(ShenandoahCollectionSet* cset,
67 RegionData* data, size_t size,
68 size_t actual_free) {
69 size_t garbage_threshold = ShenandoahHeapRegion::region_size_bytes() * ShenandoahGarbageThreshold / 100;
70
71 // The logic for cset selection in adaptive is as follows:
72 //
73 // 1. We cannot get cset larger than available free space. Otherwise we guarantee OOME
74 // during evacuation, and thus guarantee full GC. In practice, we also want to let
75 // application to allocate something. This is why we limit CSet to some fraction of
76 // available space. In non-overloaded heap, max_cset would contain all plausible candidates
77 // over garbage threshold.
78 //
79 // 2. We should not get cset too low so that free threshold would not be met right
80 // after the cycle. Otherwise we get back-to-back cycles for no reason if heap is
81 // too fragmented. In non-overloaded non-fragmented heap min_garbage would be around zero.
82 //
83 // Therefore, we start by sorting the regions by garbage. Then we unconditionally add the best candidates
84 // before we meet min_garbage. Then we add all candidates that fit with a garbage threshold before
85 // we hit max_cset. When max_cset is hit, we terminate the cset selection. Note that in this scheme,
86 // ShenandoahGarbageThreshold is the soft threshold which would be ignored until min_garbage is hit.
87
88 size_t max_cset = (ShenandoahHeap::heap()->get_young_evac_reserve() / ShenandoahEvacWaste);
89 size_t capacity = ShenandoahHeap::heap()->young_generation()->soft_max_capacity();
90
91 // As currently implemented, we are not enforcing that new_garbage > min_garbage
92 // size_t free_target = (capacity / 100) * ShenandoahMinFreeThreshold + max_cset;
93 // size_t min_garbage = (free_target > actual_free ? (free_target - actual_free) : 0);
94
95 log_info(gc, ergo)("Adaptive CSet Selection. Max CSet: " SIZE_FORMAT "%s, Actual Free: " SIZE_FORMAT "%s.",
96 byte_size_in_proper_unit(max_cset), proper_unit_for_byte_size(max_cset),
97 byte_size_in_proper_unit(actual_free), proper_unit_for_byte_size(actual_free));
98
99 // Better select garbage-first regions
100 QuickSort::sort<RegionData>(data, (int)size, compare_by_garbage, false);
101
102 size_t cur_cset = 0;
103 // size_t cur_garbage = 0;
104
105 // In generational mode, the sort order within the data array is not strictly descending amounts of garbage. In
106 // particular, regions that have reached tenure age will be sorted into this array before younger regions that contain
107 // more garbage. This represents one of the reasons why we keep looking at regions even after we decide, for example,
108 // to exclude one of the regions because it might require evacuation of too much live data.
109
110 for (size_t idx = 0; idx < size; idx++) {
111 ShenandoahHeapRegion* r = data[idx]._region;
112 size_t biased_garbage = data[idx]._garbage;
113
114 size_t new_cset = cur_cset + r->get_live_data_bytes();
115
116 // As currently implemented, we are not enforcing that new_garbage > min_garbage
117 // size_t new_garbage = cur_garbage + r->garbage();
118
119 // Note that live data bytes within a region is not the same as heap_region_size - garbage. This is because
120 // each region contains a combination of used memory (which is garbage plus live) and unused memory, which has not
121 // yet been allocated. It may be the case that the region on this iteration has too much live data to be added to
122 // the collection set while one or more regions seen on subsequent iterations of this loop can be added to the collection
123 // set because they have smaller live memory, even though they also have smaller garbage (and necessarily a larger
124 // amount of unallocated memory).
125
126 // BANDAID: In an earlier version of this code, this was written:
127 // if ((new_cset <= max_cset) && ((new_garbage < min_garbage) || (r->garbage() > garbage_threshold)))
128 // The problem with the original code is that in some cases the collection set would include hundreds of regions,
129 // each with less than 100 bytes of garbage. Evacuating these regions is counterproductive.
130
131 // TODO: Think about changing the description and defaults for ShenandoahGarbageThreshold and ShenandoahMinFreeThreshold.
132 // If "customers" want to evacuate regions with smaller amounts of garbage contained therein, they should specify a lower
133 // value of ShenandoahGarbageThreshold. As implemented currently, we may experience back-to-back collections if there is
134 // not enough memory to be reclaimed. Let's not let pursuit of min_garbage drive us to make poor decisions. Maybe we
135 // want yet another global parameter to allow a region to be placed into the collection set if
136 // (((new_garbage < min_garbage) && (r->garbage() > ShenandoahSmallerGarbageThreshold)) || (r->garbage() > garbage_threshold))
137
138 if ((new_cset <= max_cset) && ((r->garbage() > garbage_threshold) || (r->age() >= InitialTenuringThreshold))) {
139 cset->add_region(r);
140 cur_cset = new_cset;
141 // cur_garbage = new_garbage;
142 } else if (biased_garbage == 0) {
143 break;
144 }
145 }
146 }
147
148 void ShenandoahAdaptiveHeuristics::record_cycle_start() {
149 ShenandoahHeuristics::record_cycle_start();
150 _allocation_rate.allocation_counter_reset();
151 }
152
153 void ShenandoahAdaptiveHeuristics::record_success_concurrent(bool abbreviated) {
154 ShenandoahHeuristics::record_success_concurrent(abbreviated);
155
156 size_t available = ShenandoahHeap::heap()->free_set()->available();
157
158 _available.add(available);
159 double z_score = 0.0;
160 if (_available.sd() > 0) {
161 z_score = (available - _available.avg()) / _available.sd();
162 }
163
164 log_debug(gc, ergo)("Available: " SIZE_FORMAT " %sB, z-score=%.3f. Average available: %.1f %sB +/- %.1f %sB.",
165 byte_size_in_proper_unit(available), proper_unit_for_byte_size(available),
166 z_score,
167 byte_size_in_proper_unit(_available.avg()), proper_unit_for_byte_size(_available.avg()),
168 byte_size_in_proper_unit(_available.sd()), proper_unit_for_byte_size(_available.sd()));
169
170 // In the case when a concurrent GC cycle completes successfully but with an
171 // unusually small amount of available memory we will adjust our trigger
172 // parameters so that they are more likely to initiate a new cycle.
173 // Conversely, when a GC cycle results in an above average amount of available
174 // memory, we will adjust the trigger parameters to be less likely to initiate
175 // a GC cycle.
176 //
177 // The z-score we've computed is in no way statistically related to the
178 // trigger parameters, but it has the nice property that worse z-scores for
179 // available memory indicate making larger adjustments to the trigger
180 // parameters. It also results in fewer adjustments as the application
181 // stabilizes.
182 //
183 // In order to avoid making endless and likely unnecessary adjustments to the
184 // trigger parameters, the change in available memory (with respect to the
185 // average) at the end of a cycle must be beyond these threshold values.
186 if (z_score < LOWEST_EXPECTED_AVAILABLE_AT_END ||
187 z_score > HIGHEST_EXPECTED_AVAILABLE_AT_END) {
188 // The sign is flipped because a negative z-score indicates that the
189 // available memory at the end of the cycle is below average. Positive
190 // adjustments make the triggers more sensitive (i.e., more likely to fire).
191 // The z-score also gives us a measure of just how far below normal. This
192 // property allows us to adjust the trigger parameters proportionally.
193 //
194 // The `100` here is used to attenuate the size of our adjustments. This
195 // number was chosen empirically. It also means the adjustments at the end of
196 // a concurrent cycle are an order of magnitude smaller than the adjustments
197 // made for a degenerated or full GC cycle (which themselves were also
198 // chosen empirically).
199 adjust_last_trigger_parameters(z_score / -100);
200 }
201 }
202
203 void ShenandoahAdaptiveHeuristics::record_success_degenerated() {
204 ShenandoahHeuristics::record_success_degenerated();
205 // Adjust both trigger's parameters in the case of a degenerated GC because
206 // either of them should have triggered earlier to avoid this case.
207 adjust_margin_of_error(DEGENERATE_PENALTY_SD);
208 adjust_spike_threshold(DEGENERATE_PENALTY_SD);
209 }
210
211 void ShenandoahAdaptiveHeuristics::record_success_full() {
212 ShenandoahHeuristics::record_success_full();
213 // Adjust both trigger's parameters in the case of a full GC because
214 // either of them should have triggered earlier to avoid this case.
215 adjust_margin_of_error(FULL_PENALTY_SD);
216 adjust_spike_threshold(FULL_PENALTY_SD);
217 }
218
219 static double saturate(double value, double min, double max) {
220 return MAX2(MIN2(value, max), min);
221 }
222
223 bool ShenandoahAdaptiveHeuristics::should_start_gc() {
224 size_t max_capacity = _generation->max_capacity();
225 size_t capacity = _generation->soft_max_capacity();
226 size_t available = _generation->available();
227 size_t allocated = _generation->bytes_allocated_since_gc_start();
228
229 log_debug(gc)("should_start_gc (%s)? available: " SIZE_FORMAT ", soft_max_capacity: " SIZE_FORMAT
230 ", max_capacity: " SIZE_FORMAT ", allocated: " SIZE_FORMAT,
231 _generation->name(), available, capacity, max_capacity, allocated);
232
233 // Make sure the code below treats available without the soft tail.
234 size_t soft_tail = max_capacity - capacity;
235 available = (available > soft_tail) ? (available - soft_tail) : 0;
236
237 // Track allocation rate even if we decide to start a cycle for other reasons.
238 double rate = _allocation_rate.sample(allocated);
239 _last_trigger = OTHER;
240
241 size_t min_threshold = capacity / 100 * ShenandoahMinFreeThreshold;
242
243 if (available < min_threshold) {
244 log_info(gc)("Trigger (%s): Free (" SIZE_FORMAT "%s) is below minimum threshold (" SIZE_FORMAT "%s)",
245 _generation->name(),
246 byte_size_in_proper_unit(available), proper_unit_for_byte_size(available),
247 byte_size_in_proper_unit(min_threshold), proper_unit_for_byte_size(min_threshold));
248 return true;
249 }
250
251 // Check if we need to learn a bit about the application
252 const size_t max_learn = ShenandoahLearningSteps;
253 if (_gc_times_learned < max_learn) {
254 size_t init_threshold = capacity / 100 * ShenandoahInitFreeThreshold;
255 if (available < init_threshold) {
256 log_info(gc)("Trigger (%s): Learning " SIZE_FORMAT " of " SIZE_FORMAT ". Free (" SIZE_FORMAT "%s) is below initial threshold (" SIZE_FORMAT "%s)",
257 _generation->name(), _gc_times_learned + 1, max_learn,
258 byte_size_in_proper_unit(available), proper_unit_for_byte_size(available),
259 byte_size_in_proper_unit(init_threshold), proper_unit_for_byte_size(init_threshold));
260 return true;
261 }
262 }
263
264 // Check if allocation headroom is still okay. This also factors in:
265 // 1. Some space to absorb allocation spikes
266 // 2. Accumulated penalties from Degenerated and Full GC
267 size_t allocation_headroom = available;
268
269 // ShenandoahAllocSpikeFactor is the percentage of capacity that we endeavor to assure to be free at the end of the GC
270 // cycle.
271 // TODO: Correct the representation of this quantity
272 // (and dive deeper into _gc_time_penalties as this may also need to be corrected)
273 //
274 // Allocation spikes are a characteristic of both the application ahd the JVM configuration. On the JVM command line,
275 // the application developer may want to supply a hint of the nature of spikes that are inherent in the application
276 // workload, and this information would normally be independent of heap size (not a percentage thereof). On the
277 // other hand, some allocation spikes are correlated with JVM configuration. For example, there are allocation
278 // spikes at the starts of concurrent marking and evacuation to refresh all local allocation buffers. The nature
279 // of these spikes is determined by LAB min and max sizes and numbers of threads, but also on frequency of GC passes,
280 // and on "periodic" behavior of these threads If GC frequency is much higher than the periodic trigger for mutator
281 // threads, then many of the mutator threads may be able to "sit out" of most GC passes. Though the thread's stack
282 // must be scanned, the thread does not need to refresh its LABs if it sits idle throughout the duration of the GC
283 // pass. The best prediction for this aspect of spikes in allocation patterns is probably recent past history.
284 //
285 // Rationale:
286 // The idea is that there is an average allocation rate and there are occasional abnormal bursts (or spikes) of
287 // allocations that exceed the average allocation rate. What do these spikes look like?
288 //
289 // 1. At certain phase changes, we may discard large amounts of data and replace it with large numbers of newly
290 // allocated objects. This "spike" looks more like a phase change. We were in steady state at M bytes/sec
291 // allocation rate and now we're in a "reinitialization phase" that looks like N bytes/sec. We need the "spike"
292 // accomodation to give us enough runway to recalibrate our "average allocation rate".
293 //
294 // 2. The typical workload changes. "Suddenly", our typical workload of N TPS increases to N+delta TPS. This means
295 // our average allocation rate needs to be adjusted. Once again, we need the "spike" accomodation to give us
296 // enough runway to recalibrate our "average allocation rate".
297 //
298 // 3. Though there is an "average" allocation rate, a given workload's demand for allocation may be very bursty. We
299 // allocate a bunch of LABs during the 5 ms that follow completion of a GC, then we perform no more allocations for
300 // the next 150 ms. It seems we want the "spike" to represent the maximum divergence from average within the
301 // period of time between consecutive evaluation of the should_start_gc() service. Here's the thinking:
302 //
303 // a) Between now and the next time I ask whether should_start_gc(), we might experience a spike representing
304 // the anticipated burst of allocations. If that would put us over budget, then we should start GC immediately.
305 // b) Between now and the anticipated depletion of allocation pool, there may be two or more bursts of allocations.
306 // If there are more than one of these bursts, we can "approximate" that these will be separated by spans of
307 // time with very little or no allocations so the "average" allocation rate should be a suitable approximation
308 // of how this will behave.
309 //
310 // For cases 1 and 2, we need to "quickly" recalibrate the average allocation rate whenever we detect a change
311 // in operation mode. We want some way to decide that the average rate has changed. Make average allocation rate
312 // computations an independent effort.
313
314 size_t spike_headroom = capacity / 100 * ShenandoahAllocSpikeFactor;
315 size_t penalties = capacity / 100 * _gc_time_penalties;
316
317 // TODO: Account for inherent delays in responding to GC triggers
318 // 1. It has been observed that delays of 200 ms or greater are common between the moment we return true from should_start_gc()
319 // and the moment at which we begin execution of the concurrent reset phase. Add this time into the calculation of
320 // avg_cycle_time below. (What is "this time"? Perhaps we should remember recent history of this delay for the
321 // running workload and use the maximum delay recently seen for "this time".)
322 // 2. The frequency of inquiries to should_start_gc() is adaptive, ranging between ShenandoahControlIntervalMin and
323 // ShenandoahControlIntervalMax. The current control interval (or the max control interval) should also be added into
324 // the calculation of avg_cycle_time below.
325
326 allocation_headroom -= MIN2(allocation_headroom, spike_headroom);
327 allocation_headroom -= MIN2(allocation_headroom, penalties);
328
329 double avg_cycle_time = _gc_time_history->davg() + (_margin_of_error_sd * _gc_time_history->dsd());
330
331 size_t last_live_memory = get_last_live_memory();
332 size_t penultimate_live_memory = get_penultimate_live_memory();
333 double original_cycle_time = avg_cycle_time;
334 if ((penultimate_live_memory < last_live_memory) && (penultimate_live_memory != 0)) {
335 // If the live-memory size is growing, our estimates of cycle time are based on lighter workload, so adjust.
336 // TODO: Be more precise about how to scale when live memory is growing. Existing code is a very rough approximation
337 // tuned with very limited workload observations.
338 avg_cycle_time = (avg_cycle_time * 2 * last_live_memory) / penultimate_live_memory;
339 } else {
340 int degen_cycles = degenerated_cycles_in_a_row();
341 if (degen_cycles > 0) {
342 // If we've degenerated recently, we might be waiting too long between triggers so adjust trigger forward.
343 // TODO: Be more precise about how to scale when we've experienced recent degenerated GC. Existing code is a very
344 // rough approximation tuned with very limited workload observations.
345 avg_cycle_time += degen_cycles * avg_cycle_time;
346 }
347 }
348
349 double avg_alloc_rate = _allocation_rate.upper_bound(_margin_of_error_sd);
350 log_debug(gc)("%s: average GC time: %.2f ms, allocation rate: %.0f %s/s",
351 _generation->name(), avg_cycle_time * 1000, byte_size_in_proper_unit(avg_alloc_rate), proper_unit_for_byte_size(avg_alloc_rate));
352
353 if (avg_cycle_time > allocation_headroom / avg_alloc_rate) {
354 if (avg_cycle_time > original_cycle_time) {
355 log_debug(gc)("%s: average GC time adjusted from: %.2f ms to %.2f ms because upward trend in live memory retention",
356 _generation->name(), original_cycle_time, avg_cycle_time);
357 }
358
359 log_info(gc)("Trigger (%s): 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)",
360 _generation->name(), avg_cycle_time * 1000,
361 byte_size_in_proper_unit(avg_alloc_rate), proper_unit_for_byte_size(avg_alloc_rate),
362 byte_size_in_proper_unit(allocation_headroom), proper_unit_for_byte_size(allocation_headroom),
363 _margin_of_error_sd);
364
365 log_info(gc, ergo)("Free headroom: " SIZE_FORMAT "%s (free) - " SIZE_FORMAT "%s (spike) - " SIZE_FORMAT "%s (penalties) = " SIZE_FORMAT "%s",
366 byte_size_in_proper_unit(available), proper_unit_for_byte_size(available),
367 byte_size_in_proper_unit(spike_headroom), proper_unit_for_byte_size(spike_headroom),
368 byte_size_in_proper_unit(penalties), proper_unit_for_byte_size(penalties),
369 byte_size_in_proper_unit(allocation_headroom), proper_unit_for_byte_size(allocation_headroom));
370
371 _last_trigger = RATE;
372 return true;
373 }
374
375 bool is_spiking = _allocation_rate.is_spiking(rate, _spike_threshold_sd);
376 if (is_spiking && avg_cycle_time > allocation_headroom / rate) {
377 log_info(gc)("Trigger (%s): 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)",
378 _generation->name(), avg_cycle_time * 1000,
379 byte_size_in_proper_unit(rate), proper_unit_for_byte_size(rate),
380 byte_size_in_proper_unit(allocation_headroom), proper_unit_for_byte_size(allocation_headroom),
381
382 _spike_threshold_sd);
383 _last_trigger = SPIKE;
384 return true;
385 }
386
387 return ShenandoahHeuristics::should_start_gc();
388 }
389
390 void ShenandoahAdaptiveHeuristics::adjust_last_trigger_parameters(double amount) {
391 switch (_last_trigger) {
392 case RATE:
393 adjust_margin_of_error(amount);
394 break;
395 case SPIKE:
396 adjust_spike_threshold(amount);
397 break;
398 case OTHER:
399 // nothing to adjust here.
400 break;
401 default:
402 ShouldNotReachHere();
403 }
404 }
405
406 void ShenandoahAdaptiveHeuristics::adjust_margin_of_error(double amount) {
407 _margin_of_error_sd = saturate(_margin_of_error_sd + amount, MINIMUM_CONFIDENCE, MAXIMUM_CONFIDENCE);
408 log_debug(gc, ergo)("Margin of error now %.2f", _margin_of_error_sd);
409 }
410
411 void ShenandoahAdaptiveHeuristics::adjust_spike_threshold(double amount) {
412 _spike_threshold_sd = saturate(_spike_threshold_sd - amount, MINIMUM_CONFIDENCE, MAXIMUM_CONFIDENCE);
413 log_debug(gc, ergo)("Spike threshold now: %.2f", _spike_threshold_sd);
414 }
415
416 ShenandoahAllocationRate::ShenandoahAllocationRate() :
417 _last_sample_time(os::elapsedTime()),
418 _last_sample_value(0),
419 _interval_sec(1.0 / ShenandoahAdaptiveSampleFrequencyHz),
420 _rate(int(ShenandoahAdaptiveSampleSizeSeconds * ShenandoahAdaptiveSampleFrequencyHz), ShenandoahAdaptiveDecayFactor),
421 _rate_avg(int(ShenandoahAdaptiveSampleSizeSeconds * ShenandoahAdaptiveSampleFrequencyHz), ShenandoahAdaptiveDecayFactor) {
422 }
423
424 double ShenandoahAllocationRate::sample(size_t allocated) {
425 double now = os::elapsedTime();
426 double rate = 0.0;
427 if (now - _last_sample_time > _interval_sec) {
428 if (allocated >= _last_sample_value) {
429 rate = instantaneous_rate(now, allocated);
430 _rate.add(rate);
431 _rate_avg.add(_rate.avg());
432 }
433
434 _last_sample_time = now;
435 _last_sample_value = allocated;
436 }
437 return rate;
438 }
439
440 double ShenandoahAllocationRate::upper_bound(double sds) const {
441 // Here we are using the standard deviation of the computed running
442 // average, rather than the standard deviation of the samples that went
443 // into the moving average. This is a much more stable value and is tied
444 // to the actual statistic in use (moving average over samples of averages).
445 return _rate.davg() + (sds * _rate_avg.dsd());
446 }
447
448 void ShenandoahAllocationRate::allocation_counter_reset() {
449 _last_sample_time = os::elapsedTime();
450 _last_sample_value = 0;
451 }
452
453 bool ShenandoahAllocationRate::is_spiking(double rate, double threshold) const {
454 if (rate <= 0.0) {
455 return false;
456 }
457
458 double sd = _rate.sd();
459 if (sd > 0) {
460 // There is a small chance that that rate has already been sampled, but it
461 // seems not to matter in practice.
462 double z_score = (rate - _rate.avg()) / sd;
463 if (z_score > threshold) {
464 return true;
465 }
466 }
467 return false;
468 }
469
470 double ShenandoahAllocationRate::instantaneous_rate(size_t allocated) const {
471 return instantaneous_rate(os::elapsedTime(), allocated);
472 }
473
474 double ShenandoahAllocationRate::instantaneous_rate(double time, size_t allocated) const {
475 size_t last_value = _last_sample_value;
476 double last_time = _last_sample_time;
477 size_t allocation_delta = (allocated > last_value) ? (allocated - last_value) : 0;
478 double time_delta_sec = time - last_time;
479 return (time_delta_sec > 0) ? (allocation_delta / time_delta_sec) : 0;
480 }