diff options
Diffstat (limited to 'ml/Host.cc')
| -rw-r--r-- | ml/Host.cc | 387 |
1 files changed, 0 insertions, 387 deletions
diff --git a/ml/Host.cc b/ml/Host.cc deleted file mode 100644 index a5f276a80..000000000 --- a/ml/Host.cc +++ /dev/null @@ -1,387 +0,0 @@ -// SPDX-License-Identifier: GPL-3.0-or-later - -#include "Config.h" -#include "Host.h" -#include "Queue.h" -#include "ADCharts.h" - -#include "json/single_include/nlohmann/json.hpp" - -using namespace ml; - -void Host::addChart(Chart *C) { - std::lock_guard<Mutex> L(M); - Charts[C->getRS()] = C; -} - -void Host::removeChart(Chart *C) { - std::lock_guard<Mutex> L(M); - Charts.erase(C->getRS()); -} - -void Host::getConfigAsJson(nlohmann::json &Json) const { - Json["version"] = 1; - - Json["enabled"] = Cfg.EnableAnomalyDetection; - - Json["min-train-samples"] = Cfg.MinTrainSamples; - Json["max-train-samples"] = Cfg.MaxTrainSamples; - Json["train-every"] = Cfg.TrainEvery; - - Json["diff-n"] = Cfg.DiffN; - Json["smooth-n"] = Cfg.SmoothN; - Json["lag-n"] = Cfg.LagN; - - Json["random-sampling-ratio"] = Cfg.RandomSamplingRatio; - Json["max-kmeans-iters"] = Cfg.MaxKMeansIters; - - Json["dimension-anomaly-score-threshold"] = Cfg.DimensionAnomalyScoreThreshold; - - Json["host-anomaly-rate-threshold"] = Cfg.HostAnomalyRateThreshold; - Json["anomaly-detection-grouping-method"] = group_method2string(Cfg.AnomalyDetectionGroupingMethod); - Json["anomaly-detection-query-duration"] = Cfg.AnomalyDetectionQueryDuration; - - Json["hosts-to-skip"] = Cfg.HostsToSkip; - Json["charts-to-skip"] = Cfg.ChartsToSkip; -} - -void Host::getModelsAsJson(nlohmann::json &Json) { - std::lock_guard<Mutex> L(M); - - for (auto &CP : Charts) { - Chart *C = CP.second; - C->getModelsAsJson(Json); - } -} - -#define WORKER_JOB_DETECTION_PREP 0 -#define WORKER_JOB_DETECTION_DIM_CHART 1 -#define WORKER_JOB_DETECTION_HOST_CHART 2 -#define WORKER_JOB_DETECTION_STATS 3 -#define WORKER_JOB_DETECTION_RESOURCES 4 - -void Host::detectOnce() { - worker_is_busy(WORKER_JOB_DETECTION_PREP); - - MLS = {}; - MachineLearningStats MLSCopy = {}; - TrainingStats TSCopy = {}; - - { - std::lock_guard<Mutex> L(M); - - /* - * prediction/detection stats - */ - for (auto &CP : Charts) { - Chart *C = CP.second; - - if (!C->isAvailableForML()) - continue; - - MachineLearningStats ChartMLS = C->getMLS(); - - MLS.NumMachineLearningStatusEnabled += ChartMLS.NumMachineLearningStatusEnabled; - MLS.NumMachineLearningStatusDisabledUE += ChartMLS.NumMachineLearningStatusDisabledUE; - MLS.NumMachineLearningStatusDisabledSP += ChartMLS.NumMachineLearningStatusDisabledSP; - - MLS.NumMetricTypeConstant += ChartMLS.NumMetricTypeConstant; - MLS.NumMetricTypeVariable += ChartMLS.NumMetricTypeVariable; - - MLS.NumTrainingStatusUntrained += ChartMLS.NumTrainingStatusUntrained; - MLS.NumTrainingStatusPendingWithoutModel += ChartMLS.NumTrainingStatusPendingWithoutModel; - MLS.NumTrainingStatusTrained += ChartMLS.NumTrainingStatusTrained; - MLS.NumTrainingStatusPendingWithModel += ChartMLS.NumTrainingStatusPendingWithModel; - - MLS.NumAnomalousDimensions += ChartMLS.NumAnomalousDimensions; - MLS.NumNormalDimensions += ChartMLS.NumNormalDimensions; - } - - HostAnomalyRate = 0.0; - size_t NumActiveDimensions = MLS.NumAnomalousDimensions + MLS.NumNormalDimensions; - if (NumActiveDimensions) - HostAnomalyRate = static_cast<double>(MLS.NumAnomalousDimensions) / NumActiveDimensions; - - MLSCopy = MLS; - - /* - * training stats - */ - TSCopy = TS; - - TS.QueueSize = 0; - TS.NumPoppedItems = 0; - - TS.AllottedUT = 0; - TS.ConsumedUT = 0; - TS.RemainingUT = 0; - - TS.TrainingResultOk = 0; - TS.TrainingResultInvalidQueryTimeRange = 0; - TS.TrainingResultNotEnoughCollectedValues = 0; - TS.TrainingResultNullAcquiredDimension = 0; - TS.TrainingResultChartUnderReplication = 0; - } - - // Calc the avg values - if (TSCopy.NumPoppedItems) { - TSCopy.QueueSize /= TSCopy.NumPoppedItems; - TSCopy.AllottedUT /= TSCopy.NumPoppedItems; - TSCopy.ConsumedUT /= TSCopy.NumPoppedItems; - TSCopy.RemainingUT /= TSCopy.NumPoppedItems; - - TSCopy.TrainingResultOk /= TSCopy.NumPoppedItems; - TSCopy.TrainingResultInvalidQueryTimeRange /= TSCopy.NumPoppedItems; - TSCopy.TrainingResultNotEnoughCollectedValues /= TSCopy.NumPoppedItems; - TSCopy.TrainingResultNullAcquiredDimension /= TSCopy.NumPoppedItems; - TSCopy.TrainingResultChartUnderReplication /= TSCopy.NumPoppedItems; - } else { - TSCopy.QueueSize = 0; - TSCopy.AllottedUT = 0; - TSCopy.ConsumedUT = 0; - TSCopy.RemainingUT = 0; - } - - if(!RH) - return; - - worker_is_busy(WORKER_JOB_DETECTION_DIM_CHART); - updateDimensionsChart(RH, MLSCopy); - - worker_is_busy(WORKER_JOB_DETECTION_HOST_CHART); - updateHostAndDetectionRateCharts(RH, HostAnomalyRate * 10000.0); - -#ifdef NETDATA_ML_RESOURCE_CHARTS - worker_is_busy(WORKER_JOB_DETECTION_RESOURCES); - struct rusage PredictionRU; - getrusage(RUSAGE_THREAD, &PredictionRU); - updateResourceUsageCharts(RH, PredictionRU, TSCopy.TrainingRU); -#endif - - worker_is_busy(WORKER_JOB_DETECTION_STATS); - updateTrainingStatisticsChart(RH, TSCopy); -} - -class AcquiredDimension { -public: - static AcquiredDimension find(RRDHOST *RH, STRING *ChartId, STRING *DimensionId) { - RRDDIM_ACQUIRED *AcqRD = nullptr; - Dimension *D = nullptr; - - RRDSET *RS = rrdset_find(RH, string2str(ChartId)); - if (RS) { - AcqRD = rrddim_find_and_acquire(RS, string2str(DimensionId)); - if (AcqRD) { - RRDDIM *RD = rrddim_acquired_to_rrddim(AcqRD); - if (RD) - D = reinterpret_cast<Dimension *>(RD->ml_dimension); - } - } - - return AcquiredDimension(AcqRD, D); - } - -private: - AcquiredDimension(RRDDIM_ACQUIRED *AcqRD, Dimension *D) : AcqRD(AcqRD), D(D) {} - -public: - TrainingResult train(const TrainingRequest &TR) { - if (!D) - return TrainingResult::NullAcquiredDimension; - - return D->trainModel(TR); - } - - ~AcquiredDimension() { - if (AcqRD) - rrddim_acquired_release(AcqRD); - } - -private: - RRDDIM_ACQUIRED *AcqRD; - Dimension *D; -}; - -void Host::scheduleForTraining(TrainingRequest TR) { - TrainingQueue.push(TR); -} - -#define WORKER_JOB_TRAINING_FIND 0 -#define WORKER_JOB_TRAINING_TRAIN 1 -#define WORKER_JOB_TRAINING_STATS 2 - -void Host::train() { - worker_register("MLTRAIN"); - worker_register_job_name(WORKER_JOB_TRAINING_FIND, "find"); - worker_register_job_name(WORKER_JOB_TRAINING_TRAIN, "train"); - worker_register_job_name(WORKER_JOB_TRAINING_STATS, "stats"); - - service_register(SERVICE_THREAD_TYPE_NETDATA, NULL, (force_quit_t )ml_cancel_anomaly_detection_threads, RH, true); - - while (service_running(SERVICE_ML_TRAINING)) { - auto P = TrainingQueue.pop(); - TrainingRequest TrainingReq = P.first; - size_t Size = P.second; - - if (ThreadsCancelled) { - info("Stopping training thread because it was cancelled."); - break; - } - - usec_t AllottedUT = (Cfg.TrainEvery * RH->rrd_update_every * USEC_PER_SEC) / Size; - if (AllottedUT > USEC_PER_SEC) - AllottedUT = USEC_PER_SEC; - - usec_t StartUT = now_monotonic_usec(); - TrainingResult TrainingRes; - { - worker_is_busy(WORKER_JOB_TRAINING_FIND); - AcquiredDimension AcqDim = AcquiredDimension::find(RH, TrainingReq.ChartId, TrainingReq.DimensionId); - - worker_is_busy(WORKER_JOB_TRAINING_TRAIN); - TrainingRes = AcqDim.train(TrainingReq); - - string_freez(TrainingReq.ChartId); - string_freez(TrainingReq.DimensionId); - } - usec_t ConsumedUT = now_monotonic_usec() - StartUT; - - worker_is_busy(WORKER_JOB_TRAINING_STATS); - - usec_t RemainingUT = 0; - if (ConsumedUT < AllottedUT) - RemainingUT = AllottedUT - ConsumedUT; - - { - std::lock_guard<Mutex> L(M); - - if (TS.AllottedUT == 0) { - struct rusage TRU; - getrusage(RUSAGE_THREAD, &TRU); - TS.TrainingRU = TRU; - } - - TS.QueueSize += Size; - TS.NumPoppedItems += 1; - - TS.AllottedUT += AllottedUT; - TS.ConsumedUT += ConsumedUT; - TS.RemainingUT += RemainingUT; - - switch (TrainingRes) { - case TrainingResult::Ok: - TS.TrainingResultOk += 1; - break; - case TrainingResult::InvalidQueryTimeRange: - TS.TrainingResultInvalidQueryTimeRange += 1; - break; - case TrainingResult::NotEnoughCollectedValues: - TS.TrainingResultNotEnoughCollectedValues += 1; - break; - case TrainingResult::NullAcquiredDimension: - TS.TrainingResultNullAcquiredDimension += 1; - break; - case TrainingResult::ChartUnderReplication: - TS.TrainingResultChartUnderReplication += 1; - break; - } - } - - worker_is_idle(); - std::this_thread::sleep_for(std::chrono::microseconds{RemainingUT}); - worker_is_busy(0); - } -} - -void Host::detect() { - worker_register("MLDETECT"); - worker_register_job_name(WORKER_JOB_DETECTION_PREP, "prep"); - worker_register_job_name(WORKER_JOB_DETECTION_DIM_CHART, "dim chart"); - worker_register_job_name(WORKER_JOB_DETECTION_HOST_CHART, "host chart"); - worker_register_job_name(WORKER_JOB_DETECTION_STATS, "stats"); - worker_register_job_name(WORKER_JOB_DETECTION_RESOURCES, "resources"); - - service_register(SERVICE_THREAD_TYPE_NETDATA, NULL, (force_quit_t )ml_cancel_anomaly_detection_threads, RH, true); - - heartbeat_t HB; - heartbeat_init(&HB); - - while (service_running((SERVICE_TYPE)(SERVICE_ML_PREDICTION | SERVICE_COLLECTORS))) { - worker_is_idle(); - heartbeat_next(&HB, (RH ? RH->rrd_update_every : default_rrd_update_every) * USEC_PER_SEC); - detectOnce(); - } -} - -void Host::getDetectionInfoAsJson(nlohmann::json &Json) const { - Json["version"] = 1; - Json["anomalous-dimensions"] = MLS.NumAnomalousDimensions; - Json["normal-dimensions"] = MLS.NumNormalDimensions; - Json["total-dimensions"] = MLS.NumAnomalousDimensions + MLS.NumNormalDimensions; - Json["trained-dimensions"] = MLS.NumTrainingStatusTrained + MLS.NumTrainingStatusPendingWithModel; -} - -void *train_main(void *Arg) { - Host *H = reinterpret_cast<Host *>(Arg); - H->train(); - return nullptr; -} - -void *detect_main(void *Arg) { - Host *H = reinterpret_cast<Host *>(Arg); - H->detect(); - return nullptr; -} - -void Host::startAnomalyDetectionThreads() { - if (ThreadsRunning) { - error("Anomaly detections threads for host %s are already-up and running.", rrdhost_hostname(RH)); - return; - } - - ThreadsRunning = true; - ThreadsCancelled = false; - ThreadsJoined = false; - - char Tag[NETDATA_THREAD_TAG_MAX + 1]; - -// #define ML_DISABLE_JOINING - - snprintfz(Tag, NETDATA_THREAD_TAG_MAX, "MLTR[%s]", rrdhost_hostname(RH)); - netdata_thread_create(&TrainingThread, Tag, NETDATA_THREAD_OPTION_JOINABLE, train_main, static_cast<void *>(this)); - - snprintfz(Tag, NETDATA_THREAD_TAG_MAX, "MLDT[%s]", rrdhost_hostname(RH)); - netdata_thread_create(&DetectionThread, Tag, NETDATA_THREAD_OPTION_JOINABLE, detect_main, static_cast<void *>(this)); -} - -void Host::stopAnomalyDetectionThreads(bool join) { - if (!ThreadsRunning) { - error("Anomaly detections threads for host %s have already been stopped.", rrdhost_hostname(RH)); - return; - } - - if(!ThreadsCancelled) { - ThreadsCancelled = true; - - // Signal the training queue to stop popping-items - TrainingQueue.signal(); - netdata_thread_cancel(TrainingThread); - netdata_thread_cancel(DetectionThread); - } - - if (join && !ThreadsJoined) { - ThreadsJoined = true; - ThreadsRunning = false; - - // these fail on alpine linux and our CI hangs forever - // failing to compile static builds - - // commenting them, until we find a solution - - // to enable again: - // NETDATA_THREAD_OPTION_DEFAULT needs to become NETDATA_THREAD_OPTION_JOINABLE - - netdata_thread_join(TrainingThread, nullptr); - netdata_thread_join(DetectionThread, nullptr); - } -} |