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+1625750751,571.5697258813137,1,935.8507384883574 diff --git a/forecasting-gluonmachines/docker_image/Dockerfile b/forecasting-gluonmachines/docker_image/Dockerfile new file mode 100644 index 00000000..93e755d0 --- /dev/null +++ b/forecasting-gluonmachines/docker_image/Dockerfile @@ -0,0 +1,78 @@ +FROM python:3.8.10 +WORKDIR /morphemic_project +#ENV DEBIAN_FRONTEND noninteractive +RUN apt-get clean && apt-get update -y -qq +RUN apt-get install -y --no-install-recommends apt-utils +RUN python -m pip install --upgrade pip +# Install generic packages +# ------------------------ +RUN apt-get install -y \ + build-essential \ + cmake \ + pkg-config \ + wget \ + swig \ + git \ + curl \ + unzip \ + libaio1 \ + nano \ + freetds-dev \ + unixodbc \ + unixodbc-dev \ + libjpeg-dev \ + libtiff5-dev \ + libpng-dev \ + libgtk2.0-dev \ + libavcodec-dev \ + libavformat-dev \ + libswscale-dev \ + libv4l-dev \ + libatlas-base-dev \ + gfortran \ + libhdf5-dev \ + libtbb2 \ + libtbb-dev \ + libgl1-mesa-glx +# Install ML libraries +# ------------------------ +RUN pip install tensorflow keras +RUN pip install scikit-learn +RUN pip install pandas +RUN pip install matplotlib +RUN pip install numpy +RUN pip install plotly +RUN pip install scipy +RUN pip install convertdate +RUN pip install lunarcalendar +RUN pip install holidays +RUN pip install tqdm +# Install prophet +# ------------------------ +RUN pip install pystan==2.19.1.1 +RUN pip install fbprophet +#Install gluonts +#------------------------- +RUN pip install mxnet +RUN pip install gluonts +#Install ActiveMQ integration libraries +#------------------------- +RUN pip install stomp.py +RUN pip install PyYAML +RUN pip install Timeloop +RUN pip install python-slugify +#Install DatasetMaker integration libraries +#------------------------- +RUN pip install filelock +RUN pip install influxDB +# Force the installation of the version 0.10.8.1 of py4j +# This is due to the double connection from docker issue with the version 0.10.9 +# ------------------------ +RUN python -m pip install py4j==0.10.8.1 +RUN rm -rf ~/.cache/pip +#Clone the prophet forecaster code +#------------------------- +RUN git clone https://github.com/dianajlailaty/forecasting_gluonts.git +RUN chmod +x /morphemic_project/forecasting_gluonts/gluonts/main.py +RUN mkdir /logs +CMD python /morphemic_project/forecasting_gluonts/gluonts/main.py > /logs/gluonmachines.log 2>&1 diff --git a/forecasting-gluonmachines/docker_image/run_gluonts.sh b/forecasting-gluonmachines/docker_image/run_gluonts.sh new file mode 100644 index 00000000..0ddaaf9e --- /dev/null +++ b/forecasting-gluonmachines/docker_image/run_gluonts.sh @@ -0,0 +1,15 @@ +# Remove all images +sudo docker rmi -f $(sudo docker images) + +# Build the image from dockerfile and clone the latest version of my code +sudo docker build . -t gitlab.ow2.org:4567/melodic/morphemic-preprocessor/gluonmachines:morphemic-rc1.5 + +# Test the image +#sudo docker run -it --env-file variables.env gitlab.ow2.org:4567/melodic/morphemic-preprocessor/gluonmachines:morphemic-rc1.5 + +# Push the image to gitlab +sudo docker login gitlab.ow2.org:4567 +sudo docker push gitlab.ow2.org:4567/melodic/morphemic-preprocessor/gluonmachines:morphemic-rc1.5 + + + diff --git a/forecasting-gluonmachines/docker_image/variables.env b/forecasting-gluonmachines/docker_image/variables.env new file mode 100755 index 00000000..6982f078 --- /dev/null +++ b/forecasting-gluonmachines/docker_image/variables.env @@ -0,0 +1,12 @@ +ACTIVEMQ_USER=aaa +ACTIVEMQ_PASSWORD=111 +ACTIVEMQ_PORT=61610 +ACTIVEMQ_HOSTNAME=147.102.17.76 +APP_NAME=default_application +METHOD=gluonmachines +DATA_PATH=/morphemic_project/forecasting_gluonts/ +INFLUXDB_HOSTNAME=persistentstorage +INFLUXDB_PORT=8086 +INFLUXDB_USERNAME=morphemic +INFLUXDB_PASSWORD=password +INFLUXDB_DBNAME=morphemic diff --git a/forecasting-gluonmachines/gluonts/__pycache__/dataset_maker.cpython-38.pyc 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+predictionTimes = dict() +models = dict() +#flags = {'avgResponseTime':0 , 'memory': 0} +metrics_processes=dict() +metrics = set() + +directory_path = "/morphemic_project/forecasting_gluonts/gluonts/" + +def worker(self,body,metric): + + timestamp = body['timestamp'] + prediction_horizon = body["prediction_horizon"] + number_of_forward_predictions = body["number_of_forward_predictions"] + epoch_start= body["epoch_start"] + predictionTimes[metric] = epoch_start + + while(True): + #if flags[metric] == 0: + #epoch_start = predictionTimes[metric] + #flags[metric] = 1 + #load the model + if os.path.isfile(directory_path+'models/gluonts_'+metric+".pkl"): + logging.debug("Loading the trained model for metric: " + metric) + with open(directory_path+"models/gluonts_"+metric+".pkl", 'rb') as f: + models[metric] = pickle.load(f) + + + timestamp = int(time()) + if (timestamp >= predictionTimes[metric]): + logging.debug("Start the prediction for metric: " + metric) + predictions=gluonts.predict(models[metric] , number_of_forward_predictions , prediction_horizon , epoch_start , metric) + logging.debug(predictions) + yhats = predictions['values'] + yhat_lowers = predictions['mins'] + yhat_uppers = predictions['maxs'] + + prediction_time= epoch_start+ prediction_horizon + timestamp = int(time()) + + #read probabilities file + probs = np.load(directory_path+'prob_file.npy' , allow_pickle='TRUE').item() + + + logging.debug("Sending predictions for metric: "+ metric) + + + for k in range(0,len(predictions['values'])): + yhat = yhats[k] + yhat_lower = yhat_lowers[k] + yhat_upper = yhat_uppers[k] + + self.connector.send_to_topic('intermediate_prediction.gluonmachines.'+metric, + + { + "metricValue": float(yhat), + "level": 3, + "timestamp": timestamp, + "probability": probs[metric], + "confidence_interval" : [float(yhat_lower),float(yhat_upper)], + "horizon": prediction_horizon, + "predictionTime" : int(prediction_time), + "refersTo": "todo", + "cloud": "todo", + "provider": "todo" + }) + + prediction_time=prediction_time + prediction_horizon + epoch_start = epoch_start+ prediction_horizon + sleep(prediction_horizon-5) + + +class Gluonts(morphemic.handler.ModelHandler,messaging.listener.MorphemicListener): + id = "gluonmachines" + def __init__(self): + self._run = False + self.connector = messaging.morphemic.Connection(ACTIVEMQ_USER,ACTIVEMQ_PASSWORD, host=ACTIVEMQ_HOSTNAME, port=ACTIVEMQ_PORT) + #self.connector = messaging.morphemic.Connection('morphemic','morphemic', host='147.102.17.76', port=61616) + #self.model = morphemic.model.Model(self) + + def run(self): + self.connector.connect() + self.connector.set_listener(self.id, self) + self.connector.topic("start_forecasting.gluonmachines", self.id) + self.connector.topic("stop_forecasting.gluonmachines", self.id) + self.connector.topic("metrics_to_predict", self.id) + + def reconnect(self): + self.connector.disconnect() + self.run() + pass + + def on_start_forecasting_gluonmachines(self, body): + logging.debug("Gluonts Start Forecasting the following metrics :") + sent_metrics = body["metrics"] + logging.debug(sent_metrics) + for metric in sent_metrics: + if metric not in metrics: + metrics.add(metric) + if metric not in metrics_processes: + metrics_processes[metric] = Process(target=worker, args=(self, body, metric,)) + metrics_processes[metric] .start() + + + + def on_metrics_to_predict(self, body): + dataset_preprocessor = CSVData(APP_NAME,start_collection='2h') + dataset_preprocessor.prepare_csv() + logging.debug("DATASET DOWNLOADED") + + for r in body: + metric = r['metric'] + if not os.path.isfile(directory_path+'models/gluonts_'+metric+".pkl"): + logging.debug("Training a GluonTS model for metric : " + metric) + model=gluonts.train(metric) + pkl_path = directory_path+"models/gluonts_"+metric+".pkl" + with open(pkl_path, "wb") as f: + pickle.dump(model, f) + logging.debug("model dumped") + #flags[metric]=1 + metrics.add(metric) + + self.connector .send_to_topic("training_models", + { + + "metrics": list(metrics), + + "forecasting_method": "gluonmachines", + + "timestamp": int(time()) + } + ) + + + def on_stop_forecasting_gluonmachines(self, body): + logging.debug("Gluonts Stop Forecasting the following metrics :") + logging.debug(body["metrics"]) + for metric in body["metrics"]: + if metric in metrics: + #logging.debug("Remove from the list of metrics this metric: " + metric ) + metrics_processes[metric] .terminate() + metrics.remove(metric) + metrics_processes.pop(metric) + + + + def start(self): + logging.debug("Staring Gluonts Forecaster") + self.run() + self._run = True + + + + def on_disconnected(self): + print('Disconnected from ActiveMQ') + self.reconnect() diff --git a/forecasting-gluonmachines/gluonts/gluonts/__pycache__/gluonts.cpython-38.pyc b/forecasting-gluonmachines/gluonts/gluonts/__pycache__/gluonts.cpython-38.pyc new file mode 100644 index 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import ParameterGrid +#from dataset_maker import CSVData +from time import time +from datetime import datetime +from datetime import timedelta +import ast +import pickle +import json +import os +import matplotlib.pyplot as plt + +import mxnet as mx +from mxnet import gluon +from gluonts.dataset.common import ListDataset +from gluonts.model.simple_feedforward import SimpleFeedForwardEstimator +from gluonts.model.deepar import DeepAREstimator +from gluonts.mx.trainer import Trainer +from gluonts.evaluation.backtest import make_evaluation_predictions +from gluonts.evaluation import Evaluator +from gluonts.dataset.util import to_pandas +from gluonts.dataset.field_names import FieldName +from gluonts.model.forecast import SampleForecast +from pandas import Timestamp +import itertools +from sklearn.model_selection import ParameterGrid +import statistics +import math + +directory_path = "/morphemic_project/forecasting_gluonts/gluonts/" + +def train(metric): + data_file_path = os.path.join(os.environ.get("DATA_PATH", "./"), f'{os.environ.get("APP_NAME", "demo")}.csv') + dataset = pd.read_csv(data_file_path) + + #changing the names and the format of the attributes + gluonts_dataset= pd.DataFrame() + gluonts_dataset['ds'] = dataset["time"] + gluonts_dataset['y']=dataset[metric] + gluonts_dataset['y'] = pd.to_numeric(gluonts_dataset['y'], errors='coerce') + for i in range (0,len(gluonts_dataset['ds'])): + gluonts_dataset['ds'] [i]= datetime.fromtimestamp(gluonts_dataset['ds'] [i]) + + for i in range(0,len(gluonts_dataset)): + ds=gluonts_dataset['ds'][i] + gluonts_dataset['ds'][i+1]=ds + timedelta(seconds=60) + + for i in range(0,len(gluonts_dataset['y'])): + if math.isnan(float(gluonts_dataset['y'][i])): + # print("true") + gluonts_dataset['y'][i] = gluonts_dataset['y'].mean() + + size = len(gluonts_dataset) + + logging.debug("STARTED TRAINING FOR: "+ metric) + + #splitting to train and test + test_percentage=0.2 + training_window_size=int(len(gluonts_dataset)-(len(gluonts_dataset)*test_percentage)) + + train=gluonts_dataset[:training_window_size] + validation=gluonts_dataset[training_window_size:] + + train_time = train['ds'][training_window_size-1] + validation_time = gluonts_dataset['ds'][size-1] + freq='1min' + + + gluonts_dataset=gluonts_dataset.set_index('ds') + train_ds = ListDataset([{"start":gluonts_dataset.index[0], "target":gluonts_dataset.y[:train_time]}],freq=freq) + validation_ds = ListDataset([{"start":gluonts_dataset.index[0],"target":gluonts_dataset.y[:validation_time]}],freq=freq) + + + train_entry = next(iter(train_ds)) + train_entry.keys() + + validation_entry = next(iter(validation_ds)) + validation_entry.keys() + + train_series = to_pandas(train_entry) + validation_series = to_pandas(validation_entry) + + prediction_length = len(validation_series) - len(train_series) + + + + #hyperparameter tuning and cross validation + batch_size = [75] + epochs = [5] + num_batches_per_epoch = [10] + learning_rate = [1e-3] + context_length = [5] + + param_grid = {'batch_size': batch_size, + 'epochs': epochs, + 'num_batches_per_epoch': num_batches_per_epoch, + 'learning_rate': learning_rate, + 'context_length': context_length + } + grid = ParameterGrid(param_grid) + cnt = 0 + for p in grid: + cnt = cnt+1 + + all_params = [dict(zip(param_grid.keys(), v)) for v in itertools.product(*param_grid.values())] + agg_metrics_all=list() + item_metrics_all=list() + for params in all_params: + estimator = DeepAREstimator( + prediction_length=prediction_length, + context_length=params['context_length'], + freq=freq, + trainer=Trainer(ctx="cpu", + epochs=params['epochs'], + learning_rate=params['learning_rate'], + num_batches_per_epoch=params['num_batches_per_epoch'] + ) + ) + predictor = estimator.train(training_data = train_ds) + forecast_it, ts_it = make_evaluation_predictions( + dataset=validation_ds, # validationdataset + predictor=predictor, # predictor + num_samples=20, # number of sample paths we want for evaluation + ) + forecasts = list(forecast_it) + tss = list(ts_it) + evaluator = Evaluator(quantiles=[0.1,0.5,0.9]) + agg_metrics, item_metrics = evaluator(iter(tss), iter(forecasts), num_series=len(validation_ds)) + #agg_metrics['num_hidden_dimensions'] = params['num_hidden_dimensions'] + agg_metrics['epochs'] = params['epochs'] + agg_metrics['learning_rate'] = params['learning_rate'] + agg_metrics['num_batches_per_epoch'] = params['num_batches_per_epoch'] + agg_metrics['context_length'] = params['context_length'] + agg_metrics['forecast'] = forecasts + agg_metrics_all.append(agg_metrics) + item_metrics_all.append(item_metrics) + + dataframe = pd.DataFrame(agg_metrics_all) + sorted1 = dataframe.sort_values(by=['MAPE']) + sorted1 = sorted1.reset_index(drop=True) + + estimator1 = DeepAREstimator( + prediction_length=prediction_length, + context_length=sorted1['context_length'][0], + freq=freq, + trainer=Trainer(ctx="cpu", + epochs=sorted1['epochs'][0], + learning_rate=sorted1['learning_rate'][0], + num_batches_per_epoch=sorted1['num_batches_per_epoch'][0] + ) + ) + + + predictor1 = estimator1.train(training_data=validation_ds) + + #checking if probabilities file exist + prob=0.8 + if(os.path.isfile(directory_path+'prob_file.npy')): + probs = np.load(directory_path+'prob_file.npy' , allow_pickle='TRUE').item() + probs[metric] = prob + else: + probs=dict() + probs[metric] = prob + #writing probabilities in a file + npy_filename= directory_path+'prob_file.npy' + f = open(npy_filename, "w+") + np.save(directory_path+'prob_file.npy', probs) + f.close() + return predictor1 + + +def predict(model , number_of_forward_predictions , prediction_horizon , epoch_start , metric ): + + + data_file_path = os.path.join(os.environ.get("DATA_PATH", "./"), f'{os.environ.get("APP_NAME", "demo")}.csv') + dataset = pd.read_csv(data_file_path) + + gluonts_dataset= pd.DataFrame() + gluonts_dataset['ds'] = dataset["time"] + gluonts_dataset['y']=dataset[metric] + for i in range (0,len(gluonts_dataset['ds'])): + gluonts_dataset['ds'] [i]= datetime.fromtimestamp(gluonts_dataset['ds'] [i]) + for i in range(0,len(gluonts_dataset)): + ds=gluonts_dataset['ds'][i] + gluonts_dataset['ds'][i+1]=ds + timedelta(seconds=60) + + + gluonts_dataset['y'] = pd.to_numeric(gluonts_dataset['y'], errors='coerce') + + for i in range(0,len(gluonts_dataset['y'])): + if math.isnan(float(gluonts_dataset['y'][i])): + gluonts_dataset['y'][i] = gluonts_dataset['y'].mean() + + + future = list() + for i in range(1, number_of_forward_predictions+1): + dateInSec = epoch_start + i*prediction_horizon*60 + date=datetime.fromtimestamp(dateInSec) + future.append(date) + future = pd.DataFrame(future) + future.columns = ['ds'] + + target = list(gluonts_dataset.y[-number_of_forward_predictions:]) + + + + for i in range(0,len(target)): + new_row = {'ds': future['ds'][i], 'y':target[i]} + gluonts_dataset=gluonts_dataset.append( new_row , ignore_index=True) + + + gluonts_dataset=gluonts_dataset.set_index('ds') + + test_time = list(future['ds'])[-1] + test_ds = ListDataset([{"start":gluonts_dataset.index[0],"target":gluonts_dataset.y[:test_time]}],freq='1min') + + forecast_it, ts_it = make_evaluation_predictions( + dataset=test_ds, # test dataset + predictor=model, # predictor + num_samples=20, # number of sample paths we want for evaluation + ) + + forecasts = list(forecast_it) + + forecast_entry = forecasts[0] + + logging.debug(forecast_entry) + + predictions=forecast_entry.samples + + mins=list() + maxs=list() + values=list() + + returnDict=dict() + for i in range(0 , number_of_forward_predictions): + mylist=list() + for line in predictions: + mylist.append(line[i]) + + mini = min(mylist) + maxi = max(mylist) + value = statistics.mean(mylist) + + mins.append(mini) + maxs.append(maxi) + values.append(value) + + + + returnDict = {'mins':mins, 'maxs':maxs, 'values':values} + + return returnDict + diff --git a/forecasting-gluonmachines/gluonts/main.py b/forecasting-gluonmachines/gluonts/main.py new file mode 100644 index 00000000..ca644675 --- /dev/null +++ b/forecasting-gluonmachines/gluonts/main.py @@ -0,0 +1,14 @@ +import logging +from forecasting import gluontsv2 + +logger = logging.getLogger() +logger.setLevel(logging.DEBUG) + +e = gluontsv2.Gluonts() +#try: +# e.start() +#except KeyboardInterrupt: +# e.stop() +e.start() +while True: + pass diff --git a/forecasting-gluonmachines/gluonts/messaging/Event.py b/forecasting-gluonmachines/gluonts/messaging/Event.py new file mode 100644 index 00000000..52dbc842 --- /dev/null +++ b/forecasting-gluonmachines/gluonts/messaging/Event.py @@ -0,0 +1,420 @@ + + +class Metric(enumerate): + """ + [0] (current/detected) Metrics & SLOs Events Format: + + + This event is aggregated by EMS and it is persisted in InfluxDB. Moreover, + Prediction Orchestrator will subscribe and receive the current metrics in order to + evaluate the forecasting methods, according to the defined KPIs (e.g., MAPE) + + * Topic: [metric_name] + > (e.g. MaxCPULoad) + + + { + "metricValue": 12.34, + + "level": 1, + + "timestamp": 143532341251, + + "refersTo": "MySQL_12345", + + "cloud": "AWS-Dublin", + + "provider": "AWS" + + } + + + + https://confluence.7bulls.eu/display/MOR/Forecasting+Mechanism+Sub-components+Communication + + """ + TIMESTAMP = "timestamp" + METRIC_VALUE = "metricValue" + REFERS_TO = "refersTo" + CLOUD = "cloud" + PROVIDER = "provider" + + + +class PredictionMetric(enumerate): + + """ + [1] Predicted Metrics & SLOs Events Format + + + This event is produced by the Prediction Orchestrator and reflects the final predicted value for a metric. + + - Topic: prediction.[metric_name] + > (e.g. prediction.MaxCPULoad) + + + { + "metricValue": 12.34, + + "level": 1, + + "timestamp": 143532341251, + + "probability": 0.98, + + "confidence_interval " : [8,15] + + "predictionTime": 143532342, + + "refersTo": "MySQL_12345", + + "cloud": "AWS-Dublin", + + "provider": "AWS" + + } + + + https://confluence.7bulls.eu/display/MOR/Forecasting+Mechanism+Sub-components+Communication + + """ + + _match = "prediction." + + METRICVALUE= "metricValue" + '''Predicted metric value''' + LEVEL= "level" + '''Level of VM where prediction occurred or refers''' + TIMESTAMP= "timestamp" + '''Prediction creation date/time from epoch''' + PROBABILITY= "probability" + '''Probability of the predicted metric value (range 0..1)''' + CONFIDENCE_INTERVAL= "confidence_interval" + '''the probability-confidence interval for the prediction''' + PREDICTION_TIME= "predictionTime" + '''This refers to time point in the imminent future (that is relative to the time + that is needed for reconfiguration) for which the predicted value is considered + valid/accurate (in UNIX Epoch)''' + REFERSTO= "refersTo" + '''The id of the application or component or (VM) host for which the prediction refers to''' + CLOUD= "cloud" + '''Cloud provider of the VM (with location)''' + PROVIDER= "provider" + '''Cloud provider name''' + + + +class MetricsToPredict(enumerate): + + """ + [2] Translator – to – Forecasting Methods/Prediction Orchestrator Events Format + + + This event is produced by the translator, to: + + imform Dataset Maker which metrics should subscribe to in order to aggregate the appropriate tanning dataset in the time-series DB. + instruct each of the Forecasting methods to predict the values of one or more monitoring metrics + inform the Prediction Orchestrator for the metrics which will be forecasted + + * Topic: metrics_to_predict + + + *Note:* This event could be communicated through Mule + + + [ + { + + "metric": "MaxCPULoad", + + "level": 3, + + "publish_rate": 60000, + + }, + + { + + "metric": "MinCPULoad", + + "level": 3, + + "publish_rate": 50000, + + } + + ] + + + https://confluence.7bulls.eu/display/MOR/Forecasting+Mechanism+Sub-components+Communication + + """ + + _match = "metrics_to_predict" + + METRIC = "metric" + '''name of the metric to predict''' + LEVEL = "level" + '''Level of monitoring topology where this metric may be produced/found''' + PUBLISH_RATE = "publish_rate" + '''expected rate for datapoints regarding the specific metric (according to CAMEL)''' + + +class TraningModels(enumerate): + """ + + [3] Forecasting Methods – to – Prediction Orchestrator Events Format + + + This event is produced by each of the Forecasting methods, to inform the + Prediction Orchestrator that the method has (re-)trained its model for one or more metrics. + + * Topic: training_models + + + { + + "metrics": ["MaxCPULoad","MinCPULoad"]", + + "forecasting_method": "ESHybrid", + + "timestamp": 143532341251, + + } + + + https://confluence.7bulls.eu/display/MOR/Forecasting+Mechanism+Sub-components+Communication + + """ + _match = "training_models" + + METRICS = "metrics" + '''metrics for which a certain forecasting method has successfully trained or re-trained its model''' + FORECASTING_METHOD = "forecasting_method" + '''the method that is currently re-training its models''' + TIMESTAMP = "timestamp" + '''date/time of model(s) (re-)training''' + + +class IntermediatePrediction(enumerate): + """ + + [4] Forecasting Methods – to – Prediction Orchestrator Events Format + + + This event is produced by each of the Forecasting methods, and is used by the Prediction Orchestrator to determine the final prediction value for the particular metric. + + + * Topic: intermediate_prediction.[forecasting_method].[metric_name] + * (e.g. intermediate_prediction.ESHybrid.MaxCPULoad) + * We note that any component will be able to subscribe to topics like: + * intermediate_prediction.*.MaxCPULoad → gets MaxCPULoad predictions produced by all forecasting methods or + * intermediate_prediction.ESHybrid.* → gets all metrics predictions from ESHybrid method + * We consider that each forecasting method publishes a static (but configurable) number m of predicted values (under the same timestamp) for time points into the future. These time points into the future are relevant to the reconfiguration time that it is needed (and can also be updated). + * For example if we configure m=5 predictions into the future and the reconfiguration time needed is TR=10 minutes, then at t0 a forecasting method publishes 5 events with the same timestamp and prediction times t0+10, t0+20, t0+30, t0+40, t0+50. + + + + { + "metricValue": 12.34, + + "level": 3, + + "timestamp": 143532341251, + + "probability": 0.98, + + "confidence_interval " : [8,15] + + "predictionTime": 143532342, + + "refersTo": "MySQL_12345", + + "cloud": "AWS-Dublin", + + "provider": "AWS" + + } + + + https://confluence.7bulls.eu/display/MOR/Forecasting+Mechanism+Sub-components+Communication + + """ + + _match="intermediate_prediction." + + METRICVALUE = "metricValue" + '''Predicted metric value (more than one such events will be produced for different time points into the future – this can be valuable to the Prediction Orchestrator in certain situations e.g., forecasting method is unreachable for a time period)''' + + LEVEL = "level" + '''Level of VM where prediction occurred or refers''' + + TIMESTAMP = "timestamp" + '''Prediction creation date/time from epoch''' + + PROBABILITY = "probability" + '''Probability of the predicted metric value (range 0..1)''' + + CONFIDENCE_INTERVAL = "confidence_interval" + '''the probability-confidence interval for the prediction''' + + PREDICTION_TIME = "predictionTime" + '''This refers to time point in the imminent future (that is relative to the time that is needed for reconfiguration) for which the predicted value is considered valid/accurate (in UNIX Epoch)''' + + REFERS_TO = "refersTo" + '''The id of the application or component or (VM) host for which the prediction refers to''' + + CLOUD = "cloud" + '''Cloud provider of the VM (with location)''' + + PROVIDER = "provider" + '''Cloud provider name''' + + + +class Prediction(enumerate): + """ + + [5] Prediction Orchestrator – to – Severity-based SLO Violation Detector Events Format + + + This event is used by the Prediction Orchestrator to inform the SLO Violation Detector about the current values of a metric, which can possibly lead to an SLO Violation detection. + + * Topic: prediction.[metric_name] + * (e.g. prediction.MaxCPULoad) + + + { + "metricValue": 12.34, + + "level": 1, + + "timestamp": 143532341251, + + "probability": 0.98, + + "confidence_interval " : [8,15] + + "predictionTime": 143532342, + + "refersTo": "MySQL_12345", + + "cloud": "AWS-Dublin", + + "provider": "AWS" + + } + + + + https://confluence.7bulls.eu/display/MOR/Forecasting+Mechanism+Sub-components+Communication + + + """ + + _match = "prediction." + + METRICVALUE = "metricValue" + '''Predicted metric value''' + + LEVEL = "level" + '''Level of VM where prediction occurred or refers''' + + TIMESTAMP = "timestamp" + '''Prediction creation date/time from epoch''' + + PROBABILITY = "probability" + '''Probability of the predicted metric value (range 0..1)''' + + CONFIDENCE_INTERVAL = "confidence_interval" + '''the probability-confidence interval for the prediction''' + + PREDICTIONTIME = "predictionTime" + '''This refers to time point in the imminent future (that is relative to the time that is needed for reconfiguration) for which the predicted value is considered valid/accurate (in UNIX Epoch)''' + + REFERSTO = "refersTo" + '''The id of the application or component or (VM) host for which the prediction refers to''' + + CLOUD = "cloud" + '''Cloud provider of the VM (with location)''' + + PROVIDER = "provider" + '''Cloud provider name''' + + +class StopForecasting(enumerate): + """ + [6] Prediction Orchestrator – to – Forecasting Methods Events Format + + + This event is used by the Prediction Orchestrator to instruct a forecasting method to stop producing predicted values for a selection of metrics. + + + * Topic: stop_forecasting.[forecasting_method] + * Each component that implements a specific forecasting method it should subscribe to its relevant topic (e.g. the ES-Hybrid component should subscribe to stop_forecasting.eshybrid topic) + + + { + "metrics": ["MaxCPULoad","MinCPULoad"], + "timestamp": 143532341251, + } + + https://confluence.7bulls.eu/display/MOR/Forecasting+Mechanism+Sub-components+Communication + + + """ + + _match="stop_forecasting." + + METRICS = "metrics" + '''metrics for which a certain method should stop producing predictions (because of poor results)''' + TIMESTAMP = "timestamp" + '''date/time of the command of the orchestrator''' + + +class StartForecasting(enumerate): + """ + + [7] Prediction Orchestrator – to – Forecasting Methods Events Format + + This event is used by the Prediction Orchestrator to instruct a forecasting method to start producing predicted values for a selection of metrics. + + + * Topic: start_forecasting.[forecasting_method] + * Each component that implements a specific forecasting method it should subscribe to its relevant topic (e.g. the ES-Hybrid component should subscribe to start_forecasting.eshybrid topic) + * We consider that each forecasting method should publish a static (but configurable) number m of predicted values (under the same timestamp) for time points into the future. These time points into the future are relevant to the reconfiguration time that it is needed (and can also be updated). + * For example if we configure m=5 predictions into the future and the reconfiguration time needed is TR=10 minutes, then at t0 a forecasting method publishes 5 events with the same timestamp and prediction times t0+10, t0+20, t0+30, t0+40, t0+50. + + + + + { + "metrics": ["MaxCPULoad","MinCPULoad"], + + "timestamp": 143532341251, + + "epoch_start": 143532341252, + + "number_of_forward_predictions": 5, + + "prediction_horizon": 600 + + } + + https://confluence.7bulls.eu/display/MOR/Forecasting+Mechanism+Sub-components+Communication + + + """ + + _match="start_forecasting." + + METRICS = "metrics" + '''metrics for which a certain method should start producing predictions''' + TIMESTAMP = "timestamp" + '''date/time of the command of the orchestrator''' + EPOCH_START = "epoch_start" + '''this time refers to the start time after which all predictions will be considered (i.e. t0)''' + NUMBER_OF_FORWARD_PREDICTIONS = "number_of_forward_predictions" + ''' this is a number that indicates how many time points into the future do we need predictions for.''' + PREDICTION_HORIZON = "prediction_horizon" + '''This time equals to the time (in seconds) that is needed for the platform to implement an application reconfiguration (i.e. TR).''' \ No newline at end of file diff --git a/forecasting-gluonmachines/gluonts/messaging/MorphemicConnection.py b/forecasting-gluonmachines/gluonts/messaging/MorphemicConnection.py new file mode 100644 index 00000000..6063d5ca --- /dev/null +++ b/forecasting-gluonmachines/gluonts/messaging/MorphemicConnection.py @@ -0,0 +1,72 @@ + +import stomp +import logging +import json + +from stomp.listener import PrintingListener + +class Connection: + + subscriptions = [] + + def __init__(self, username, password, + host='localhost', + port=61613, + debug=True): + self.username = username + self.password = password + self.hosts = [(host, port)] + self.conn = stomp.Connection(host_and_ports=self.hosts, auto_content_length=False) + + if debug: + logging.debug("Enabling debug") + self.conn.set_listener('print', PrintingListener()) + + def _build_id(self,topic,id): + return "id.%s-%s" % (topic,id) + + def set_listener(self, id, listener): + if self.conn: + self.conn.set_listener(id,listener) + + def subscribe(self,destination, id, ack='auto'): + if not self.conn: + raise RuntimeError('You need to connect first') + + self.conn.subscribe(destination, id, ack) + + def topic(self,destination, id, ack='auto'): + self.subscribe("/topic/%s" % destination ,self._build_id(destination,id),ack) + + def queue(self,destination, id, ack='auto'): + self.subscribe("/queue/%s" % destination ,self._build_id(destination,id),ack) + + def unsubscribe(self, topic,id): + + if not self.conn: + return + self.conn.unsubscribe(self._build_id(topic,id)) + + + def connect(self, wait=True): + + if not self.conn: + return + + self.conn.connect(self.username, self.password, wait=wait) + + def disconnect(self): + self.conn.disconnect() + + def send_to_topic(self,destination, body, headers={}, **kwargs): + + if not self.conn: + logging.error("Connect first") + return + + str = json.dumps(body) + + self.conn.send(destination="/topic/%s" % destination, + body= str, + content_type="application/json", + headers=headers, **kwargs) diff --git a/forecasting-gluonmachines/gluonts/messaging/MorphemicListener.py b/forecasting-gluonmachines/gluonts/messaging/MorphemicListener.py new file mode 100644 index 00000000..69a2dab5 --- /dev/null +++ b/forecasting-gluonmachines/gluonts/messaging/MorphemicListener.py @@ -0,0 +1,51 @@ +from json import JSONDecodeError + +from stomp.listener import ConnectionListener +import logging +import json +from slugify import slugify + +class MorphemicListener(ConnectionListener): + + def is_topic(self,headers, event): + if not hasattr(event,"_match"): + return False + match = getattr(event,'_match') + return headers.get('destination').startswith(match) + + + def has_topic_name(self,headers, string): + return headers.get('destination').startswith(string) + + def get_topic_name(self,headers): + return headers.get('destination').replace('/topic/','') + + + def has_topic_name(self,headers, string): + return headers.get('destination').startswith(string) + + def get_topic_name(self,headers): + return headers.get('destination').replace('/topic/','') + + + def on(self,headers, res): + logging.debug("Unknown message %s %s ",headers, res) + pass + + def on_message(self, frame): + headers = frame.headers + body = frame.body + logging.debug("Headers %s",headers) + logging.debug(" %s",body) + + try: + res = json.loads(body) + func_name='on_%s' % slugify(headers.get('destination').replace('/topic/',''), separator='_',) + logging.debug("func_name %s", func_name) + if hasattr(self,func_name): + func = getattr(self, func_name) + func(res) + else: + self.on(headers,res) + except JSONDecodeError: + logging.error("Error decoding %s", body) \ No newline at end of file diff --git a/forecasting-gluonmachines/gluonts/messaging/Payloads.py b/forecasting-gluonmachines/gluonts/messaging/Payloads.py new file mode 100644 index 00000000..5de1adc8 --- /dev/null +++ b/forecasting-gluonmachines/gluonts/messaging/Payloads.py @@ -0,0 +1,10 @@ + +class MetricsToPredict: + + + def load(self,body): + self.metrics = body["metrics"] + self.timestamp = body["timestamp"] + self.epoch_start = body["epoch_start"] + self.number_of_forward_predictions = body["number_of_forward_predictions"] + self.prediction_horizon = body["prediction_horizon"] diff --git a/forecasting-gluonmachines/gluonts/messaging/__init__.py b/forecasting-gluonmachines/gluonts/messaging/__init__.py new file mode 100644 index 00000000..45fe25b1 --- /dev/null +++ b/forecasting-gluonmachines/gluonts/messaging/__init__.py @@ -0,0 +1,5 @@ + +from . import MorphemicConnection as morphemic +from . import 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z2v(@6pif|-h@vx!)pEkNkXqa1HNBv?$LiWNybZERpe9)N8kGS&7PzBrL`}NDkv_7S zT!$Y(2N_}DsL2K%z_R@aW@YM`ZqdnuH>NhDmY|7?fo%X)8*}@xSeOeYvH`h(LPkSR zZF#6Hjz&Y@dPcr}cwtYinWNkjcjg&aY@j~6aYeDj&mKdQfRZ=ug39A5odr|N6o2ig zX+xcs+=NHv77`4Ac4V9ngoJKOv>TVD$hlla=^7F=)p)8~MIJ5U*l}uHykVSk=x~}U z?U;nqG+n|;I}rTj^kYy|A((`=A0#`72z`92cN-Ucx-!$d)sDh#pDf_HseA}@h?YhcKp!mRMx zP0y#CL5H{MZmPYPwfDE;ix)AYOnlJ;j_A{_v1Fc4@;%@$n}&t(1ZHKjg--6zweK`= z?{;kY5bu6~|ZCrf>f<2f range(start:-5m) |> filter(fn:(r) => r._measurement == "cpu")' + """ + + url = self._url + username = self._port + password = self._password + database = self._database + application = 'demo' + params = '-sS' + + headers = {'Accept': 'application/csv', 'Content-type': 'application/vnd.flux','Authorization': 'Token '+username+':'+password} + data_post = 'from(bucket:"'+database+'")|> range(start:-5m)|> filter(fn:(r) => r._measurement == "'+application+'")' + + response = requests.post(url+'/api/v2/query',data=json.dumps(data_post),headers=headers) diff --git a/forecasting-gluonmachines/gluonts/morphemic/dataset/__init__.py b/forecasting-gluonmachines/gluonts/morphemic/dataset/__init__.py new file mode 100644 index 00000000..db2c96f2 --- /dev/null +++ b/forecasting-gluonmachines/gluonts/morphemic/dataset/__init__.py @@ -0,0 +1,154 @@ +import os, json, time +from influxdb import InfluxDBClient +import pandas as pd +from datetime import datetime + +url_path_dataset = None + +class Row(): + def __init__(self, features,metricsname): + self.features = features + if "time" in self.features: + time_str = self.features["time"] + _obj = datetime.strptime(time_str,'%Y-%m-%dT%H:%M:%S.%fZ') + self.features["time"] = int(_obj.timestamp()) + if 'application' in metricsname: + metricsname.remove('application') + for field_name in metricsname: + if not field_name in self.features: + self.features[field_name] = None + + def getTime(self): + if "time" in self.features: + return self.features["time"] + if "timestamp" in self.features: + return self.features["timestamp"] + return None + + def makeCsvRow(self): + if "application" in self.features: + del self.features["application"] + result = '' + for key, _value in self.features.items(): + result += "{0},".format(_value) + return result[:-1] + "\n" + +class Dataset(): + def __init__(self): + self.rows = {} + self.size = 0 + def addRow(self,row): + self.rows[row.getTime()] = row + self.size +=1 + def reset(self): + self.rows = {} + self.size = 0 + print("Dataset reset") + def getSize(self): + return self.size + def sortRows(self): + return sorted(list(self.rows.values()), key=lambda x: x.getTime(), reverse=True) + def getRows(self): + return list(self.rows.values()) + def getRow(self,_time, tolerance): + for i in range(tolerance): + if int(_time + i) in self.rows: + return self.rows[int(_time+i)] + return None + def save(self,metricnames,application_name): + if "application" in metricnames: + metricnames.remove("application") + dataset_content = '' + for metric in metricnames: + dataset_content += "{0},".format(metric) + dataset_content = dataset_content[:-1] + "\n" + for row in list(self.rows.values()): + dataset_content += row.makeCsvRow() + _file = open(url_path_dataset + "{0}.csv".format(application_name),'w') + _file.write(dataset_content) + _file.close() + return url_path_dataset + "{0}.csv".format(application_name) + +class DatasetMaker(): + def __init__(self, application, start, configs): + self.application = application + self.start_filter = start + self.influxdb = InfluxDBClient(host=configs['hostname'], port=configs['port'], username=configs['username'], password=configs['password'], database=configs['dbname']) + self.dataset = Dataset() + self.tolerance = 5 + global url_path_dataset + url_path_dataset = configs['path_dataset'] + if url_path_dataset[-1] != "/": + url_path_dataset += "/" + + def getIndex(self, columns, name): + return columns.index(name) + + def makeRow(self,columns, values): + row = {} + index = 0 + for column in columns: + row[column] = values[index] + index +=1 + return row + + def prepareResultSet(self, result_set): + result = [] + columns = result_set["series"][0]["columns"] + series_values = result_set["series"][0]["values"] + index = 0 + for _values in series_values: + row = self.makeRow(columns,_values) + result.append(row) + return result + + def make(self): + try: + self.influxdb.ping() + except Exception as e: + print("Could not establish connexion with InfluxDB, please verify connexion parameters") + print(e) + return {"message": "Could not establish connexion with InfluxDB, please verify connexion parameters"} + if self.getData() == None: + return {"message":"No data found"} + + metricnames, _data = self.getData() + for _row in _data: + row = Row(_row,metricnames) + self.dataset.addRow(row) + + print("Rows construction completed") + print("{0} rows found".format(self.dataset.getSize())) + #self.dataset.sortRows() + url = self.dataset.save(metricnames,self.application) + features = self.getFeatures(url) + if features == None: + return {'status': False, 'message': 'An error occured while building dataset'} + return {'status': True,'url': url, 'application': self.application, 'features': features} + + def getFeatures(self, url): + try: + df = pd.read_csv(url) + return df.columns.to_list() + except Exception as e: + print("Cannot extract data feature list") + return None + + def extractMeasurement(self, _json): + return _json["series"][0]["columns"] + + def getData(self): + query = None + try: + if self.start_filter != None and self.start_filter != "": + query = "SELECT * FROM " + self.application +" WHERE time > now() - "+ self.start_filter + else: + query = "SELECT * FROM " + self.application + result_set = self.influxdb.query(query=query) + series = self.extractMeasurement(result_set.raw) + #self.influxdb.close() #closing connexion + return [series, self.prepareResultSet(result_set.raw)] + except Exception as e: + print("Could not collect query data points") + 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z#f8w6F}^!?^KE#+ZDv+xd5O&3EmomZzsv?>E-~ygGum45fk!SBN&|eo1rKw^?aZJ4 E2O(~oYXATM literal 0 HcmV?d00001 diff --git a/forecasting-gluonmachines/gluonts/morphemic/gradle/wrapper/gradle-wrapper.properties b/forecasting-gluonmachines/gluonts/morphemic/gradle/wrapper/gradle-wrapper.properties new file mode 100644 index 00000000..eb4fef57 --- /dev/null +++ b/forecasting-gluonmachines/gluonts/morphemic/gradle/wrapper/gradle-wrapper.properties @@ -0,0 +1,5 @@ +distributionUrl=https\://services.gradle.org/distributions/gradle-5.2.1-all.zip +distributionBase=GRADLE_USER_HOME +distributionPath=wrapper/dists +zipStorePath=wrapper/dists +zipStoreBase=GRADLE_USER_HOME diff --git a/forecasting-gluonmachines/gluonts/morphemic/gradlew b/forecasting-gluonmachines/gluonts/morphemic/gradlew new file mode 100644 index 00000000..4f906e0c --- /dev/null +++ b/forecasting-gluonmachines/gluonts/morphemic/gradlew @@ -0,0 +1,185 @@ +#!/usr/bin/env sh + +# +# Copyright 2015 the original author or authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +############################################################################## +## +## Gradle start up script for UN*X +## +############################################################################## + +# Attempt to set APP_HOME +# Resolve links: $0 may be a link +PRG="$0" +# Need this for relative symlinks. +while [ -h "$PRG" ] ; do + ls=`ls -ld "$PRG"` + link=`expr "$ls" : '.*-> \(.*\)$'` + if expr "$link" : '/.*' > /dev/null; then + PRG="$link" + else + PRG=`dirname "$PRG"`"/$link" + fi +done +SAVED="`pwd`" +cd "`dirname \"$PRG\"`/" >/dev/null +APP_HOME="`pwd -P`" +cd "$SAVED" >/dev/null + +APP_NAME="Gradle" +APP_BASE_NAME=`basename "$0"` + +# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script. +DEFAULT_JVM_OPTS='"-Xmx64m" "-Xms64m"' + +# Use the maximum available, or set MAX_FD != -1 to use that value. +MAX_FD="maximum" + +warn () { + echo "$*" +} + +die () { + echo + echo "$*" + echo + exit 1 +} + +# OS specific support (must be 'true' or 'false'). +cygwin=false +msys=false +darwin=false +nonstop=false +case "`uname`" in + CYGWIN* ) + cygwin=true + ;; + Darwin* ) + darwin=true + ;; + MINGW* ) + msys=true + ;; + NONSTOP* ) + nonstop=true + ;; +esac + +CLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar + + +# Determine the Java command to use to start the JVM. +if [ -n "$JAVA_HOME" ] ; then + if [ -x "$JAVA_HOME/jre/sh/java" ] ; then + # IBM's JDK on AIX uses strange locations for the executables + JAVACMD="$JAVA_HOME/jre/sh/java" + else + JAVACMD="$JAVA_HOME/bin/java" + fi + if [ ! -x "$JAVACMD" ] ; then + die "ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME + +Please set the JAVA_HOME variable in your environment to match the +location of your Java installation." + fi +else + JAVACMD="java" + which java >/dev/null 2>&1 || die "ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH. + +Please set the JAVA_HOME variable in your environment to match the +location of your Java installation." +fi + +# Increase the maximum file descriptors if we can. +if [ "$cygwin" = "false" -a "$darwin" = "false" -a "$nonstop" = "false" ] ; then + MAX_FD_LIMIT=`ulimit -H -n` + if [ $? -eq 0 ] ; then + if [ "$MAX_FD" = "maximum" -o "$MAX_FD" = "max" ] ; then + MAX_FD="$MAX_FD_LIMIT" + fi + ulimit -n $MAX_FD + if [ $? -ne 0 ] ; then + warn "Could not set maximum file descriptor limit: $MAX_FD" + fi + else + warn "Could not query maximum file descriptor limit: $MAX_FD_LIMIT" + fi +fi + +# For Darwin, add options to specify how the application appears in the dock +if $darwin; then + GRADLE_OPTS="$GRADLE_OPTS \"-Xdock:name=$APP_NAME\" \"-Xdock:icon=$APP_HOME/media/gradle.icns\"" +fi + +# For Cygwin or MSYS, switch paths to Windows format before running java +if [ "$cygwin" = "true" -o "$msys" = "true" ] ; then + APP_HOME=`cygpath --path --mixed "$APP_HOME"` + CLASSPATH=`cygpath --path --mixed "$CLASSPATH"` + + JAVACMD=`cygpath --unix "$JAVACMD"` + + # We build the pattern for arguments to be converted via cygpath + ROOTDIRSRAW=`find -L / -maxdepth 1 -mindepth 1 -type d 2>/dev/null` + SEP="" + for dir in $ROOTDIRSRAW ; do + ROOTDIRS="$ROOTDIRS$SEP$dir" + SEP="|" + done + OURCYGPATTERN="(^($ROOTDIRS))" + # Add a user-defined pattern to the cygpath arguments + if [ "$GRADLE_CYGPATTERN" != "" ] ; then + OURCYGPATTERN="$OURCYGPATTERN|($GRADLE_CYGPATTERN)" + fi + # Now convert the arguments - kludge to limit ourselves to /bin/sh + i=0 + for arg in "$@" ; do + CHECK=`echo "$arg"|egrep -c "$OURCYGPATTERN" -` + CHECK2=`echo "$arg"|egrep -c "^-"` ### Determine if an option + + if [ $CHECK -ne 0 ] && [ $CHECK2 -eq 0 ] ; then ### Added a condition + eval `echo args$i`=`cygpath --path --ignore --mixed "$arg"` + else + eval `echo args$i`="\"$arg\"" + fi + i=`expr $i + 1` + done + case $i in + 0) set -- ;; + 1) set -- "$args0" ;; + 2) set -- "$args0" "$args1" ;; + 3) set -- "$args0" "$args1" "$args2" ;; + 4) set -- "$args0" "$args1" "$args2" "$args3" ;; + 5) set -- "$args0" "$args1" "$args2" "$args3" "$args4" ;; + 6) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" ;; + 7) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" ;; + 8) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" ;; + 9) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" "$args8" ;; + esac +fi + +# Escape application args +save () { + for i do printf %s\\n "$i" | sed "s/'/'\\\\''/g;1s/^/'/;\$s/\$/' \\\\/" ; done + echo " " +} +APP_ARGS=`save "$@"` + +# Collect all arguments for the java command, following the shell quoting and substitution rules +eval set -- $DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS "\"-Dorg.gradle.appname=$APP_BASE_NAME\"" -classpath "\"$CLASSPATH\"" org.gradle.wrapper.GradleWrapperMain "$APP_ARGS" + +exec "$JAVACMD" "$@" diff --git a/forecasting-gluonmachines/gluonts/morphemic/gradlew.bat b/forecasting-gluonmachines/gluonts/morphemic/gradlew.bat new file mode 100644 index 00000000..107acd32 --- /dev/null +++ b/forecasting-gluonmachines/gluonts/morphemic/gradlew.bat @@ -0,0 +1,89 @@ +@rem +@rem Copyright 2015 the original author or authors. +@rem +@rem Licensed under the Apache License, Version 2.0 (the "License"); +@rem you may not use this file except in compliance with the License. +@rem You may obtain a copy of the License at +@rem +@rem https://www.apache.org/licenses/LICENSE-2.0 +@rem +@rem Unless required by applicable law or agreed to in writing, software +@rem distributed under the License is distributed on an "AS IS" BASIS, +@rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +@rem See the License for the specific language governing permissions and +@rem limitations under the License. +@rem + +@if "%DEBUG%" == "" @echo off +@rem ########################################################################## +@rem +@rem Gradle startup script for Windows +@rem +@rem ########################################################################## + +@rem Set local scope for the variables with windows NT shell +if "%OS%"=="Windows_NT" setlocal + +set DIRNAME=%~dp0 +if "%DIRNAME%" == "" set DIRNAME=. +set APP_BASE_NAME=%~n0 +set APP_HOME=%DIRNAME% + +@rem Resolve any "." and ".." in APP_HOME to make it shorter. +for %%i in ("%APP_HOME%") do set APP_HOME=%%~fi + +@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script. +set DEFAULT_JVM_OPTS="-Xmx64m" "-Xms64m" + +@rem Find java.exe +if defined JAVA_HOME goto findJavaFromJavaHome + +set JAVA_EXE=java.exe +%JAVA_EXE% -version >NUL 2>&1 +if "%ERRORLEVEL%" == "0" goto execute + +echo. +echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH. +echo. +echo Please set the JAVA_HOME variable in your environment to match the +echo location of your Java installation. + +goto fail + +:findJavaFromJavaHome +set JAVA_HOME=%JAVA_HOME:"=% +set JAVA_EXE=%JAVA_HOME%/bin/java.exe + +if exist "%JAVA_EXE%" goto execute + +echo. +echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME% +echo. +echo Please set the JAVA_HOME variable in your environment to match the +echo location of your Java installation. + +goto fail + +:execute +@rem Setup the command line + +set CLASSPATH=%APP_HOME%\gradle\wrapper\gradle-wrapper.jar + + +@rem Execute Gradle +"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% "-Dorg.gradle.appname=%APP_BASE_NAME%" -classpath "%CLASSPATH%" org.gradle.wrapper.GradleWrapperMain %* + +:end +@rem End local scope for the variables with windows NT shell +if "%ERRORLEVEL%"=="0" goto mainEnd + +:fail +rem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of +rem the _cmd.exe /c_ return code! +if not "" == "%GRADLE_EXIT_CONSOLE%" exit 1 +exit /b 1 + +:mainEnd +if "%OS%"=="Windows_NT" endlocal + +:omega diff --git a/forecasting-gluonmachines/gluonts/morphemic/handler.py b/forecasting-gluonmachines/gluonts/morphemic/handler.py new file mode 100644 index 00000000..4f1ca27a --- /dev/null +++ b/forecasting-gluonmachines/gluonts/morphemic/handler.py @@ -0,0 +1,5 @@ + + +class ModelHandler: + def on_train(self): + pass diff --git a/forecasting-gluonmachines/gluonts/morphemic/labels.morphemic b/forecasting-gluonmachines/gluonts/morphemic/labels.morphemic new file mode 100644 index 00000000..66730127 --- /dev/null +++ b/forecasting-gluonmachines/gluonts/morphemic/labels.morphemic @@ -0,0 +1 @@ +[{"configs": "docker"}, {"languages": ["python", "go", "java"]}] \ No newline at end of file diff --git a/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/CHANGES.txt b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/CHANGES.txt new file mode 100644 index 00000000..e69de29b diff --git a/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/Dataset_Maker.egg-info/PKG-INFO b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/Dataset_Maker.egg-info/PKG-INFO new file mode 100644 index 00000000..bdce7448 --- /dev/null +++ b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/Dataset_Maker.egg-info/PKG-INFO @@ -0,0 +1,79 @@ +Metadata-Version: 1.0 +Name: Dataset-Maker +Version: 0.0.1 +Summary: Python package for creating a dataset using InfluxDB data points +Home-page: http://git.dac.ds.unipi.gr/morphemic/datasetmaker +Author: Jean-Didier Totow +Author-email: totow@unipi.gr +License: LICENSE.txt +Description: 1. Generality + + Dataset maker is morphemic python library for + building dataset from data points registered into InfluxDB. + Dataset maker receives the name of an application, the start time + and the tolerance interval. More details are provided below. + + 2. InfluxDB format + + Data points in InfluxDB should have the following format for being used + correctly by the dataset maker: + + measurement : "application_name" #mandatory + timestamp : timestamp #optional + fields : dictionnary containing metric exposed by the given application + cpu_usage, memory_consumption, response_time, http_latency + tags : dictionnary of metrics related information + + The JSON describing the above information is the following: + + Ex.: + {"measurement": "application_name", + "timestamp": 155655476.453, + "fields": { + "cpu_usage": 40, + "memory_consumption": 67.9, + "response_time": 28, + "http_latency": 12 + }, + "tags": { + "core": 2 #cpu_usage of 40% is the usage of the cpu core number 2 + } + } + + If data points are presented as the above format, the dataset maker will output + a csv (application_name.csv) file with the following schema: + time, cpu_usage, memory_consumption, response_time, http_latency, core + + 3. Usage + + + Warming : make sure the above variables exist before importing dataset make library + + from morphemic.dataset import DatasetMaker + + data_maker = DatasetMaker(application, start, configs) + response = data_maker.make() + + application, string containing the application name + start, when to start building the dataset + Ex.: '10m' , build dataset containg data point stored the 10 last minute + Ex.: '3h', three hours + Ex.: '4d', four days + leave empty or set to None if you wish all data points stored in your InfluxDB + configs is dictionnary containg parameters + + { + "hostname": hostname or IP of InfluxDB + "port": port of InfluxDB + "username": InfluxDB username + "password": password of the above user + "dbname": database name + "path_dataset": path where the dataset will be saved + } + + the response contains + {'status': True,'url': url, 'application': application_name, 'features': features} + + or if an error occured + {'status': False,'message': "reason of the error"} +Platform: UNKNOWN diff --git a/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/Dataset_Maker.egg-info/SOURCES.txt b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/Dataset_Maker.egg-info/SOURCES.txt new file mode 100644 index 00000000..80dd9ca2 --- /dev/null +++ b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/Dataset_Maker.egg-info/SOURCES.txt @@ -0,0 +1,9 @@ +README.txt +setup.py +Dataset_Maker.egg-info/PKG-INFO +Dataset_Maker.egg-info/SOURCES.txt +Dataset_Maker.egg-info/dependency_links.txt +Dataset_Maker.egg-info/requires.txt +Dataset_Maker.egg-info/top_level.txt +morphemic/__init__.py +morphemic/dataset/__init__.py \ No newline at end of file diff --git a/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/Dataset_Maker.egg-info/dependency_links.txt b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/Dataset_Maker.egg-info/dependency_links.txt new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/Dataset_Maker.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/Dataset_Maker.egg-info/requires.txt b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/Dataset_Maker.egg-info/requires.txt new file mode 100644 index 00000000..e20f7f05 --- /dev/null +++ b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/Dataset_Maker.egg-info/requires.txt @@ -0,0 +1,2 @@ +pandas +influxdb diff --git a/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/Dataset_Maker.egg-info/top_level.txt b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/Dataset_Maker.egg-info/top_level.txt new file mode 100644 index 00000000..047ceb83 --- /dev/null +++ b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/Dataset_Maker.egg-info/top_level.txt @@ -0,0 +1 @@ +morphemic diff --git a/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/LICENCE.txt b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/LICENCE.txt new file mode 100644 index 00000000..2d70f41f --- /dev/null +++ b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/LICENCE.txt @@ -0,0 +1,22 @@ +Copyright (c) 2021 unipi.gr + +MIT License + +Permission is hereby granted, free of charge, to any person obtaining +a copy of this software and associated documentation files (the +"Software"), to deal in the Software without restriction, including +without limitation the rights to use, copy, modify, merge, publish, +distribute, sublicense, and/or sell copies of the Software, and to +permit persons to whom the Software is furnished to do so, subject to +the following conditions: + +The above copyright notice and this permission notice shall be +included in all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE +LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION +OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. \ No newline at end of file diff --git a/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/README.txt b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/README.txt new file mode 100644 index 00000000..94153709 --- /dev/null +++ b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/README.txt @@ -0,0 +1,70 @@ +1. Generality + +Dataset maker is morphemic python library for +building dataset from data points registered into InfluxDB. +Dataset maker receives the name of an application, the start time +and the tolerance interval. More details are provided below. + +2. InfluxDB format + +Data points in InfluxDB should have the following format for being used +correctly by the dataset maker: + +measurement : "application_name" #mandatory +timestamp : timestamp #optional +fields : dictionnary containing metric exposed by the given application + cpu_usage, memory_consumption, response_time, http_latency +tags : dictionnary of metrics related information + +The JSON describing the above information is the following: + +Ex.: + {"measurement": "application_name", + "timestamp": 155655476.453, + "fields": { + "cpu_usage": 40, + "memory_consumption": 67.9, + "response_time": 28, + "http_latency": 12 + }, + "tags": { + "core": 2 #cpu_usage of 40% is the usage of the cpu core number 2 + } + } + +If data points are presented as the above format, the dataset maker will output +a csv (application_name.csv) file with the following schema: +time, cpu_usage, memory_consumption, response_time, http_latency, core + +3. Usage + + +Warming : make sure the above variables exist before importing dataset make library + +from morphemic.dataset import DatasetMaker + +data_maker = DatasetMaker(application, start, configs) +response = data_maker.make() + +application, string containing the application name +start, when to start building the dataset +Ex.: '10m' , build dataset containg data point stored the 10 last minute +Ex.: '3h', three hours +Ex.: '4d', four days +leave empty or set to None if you wish all data points stored in your InfluxDB +configs is dictionnary containg parameters + +{ + "hostname": hostname or IP of InfluxDB + "port": port of InfluxDB + "username": InfluxDB username + "password": password of the above user + "dbname": database name + "path_dataset": path where the dataset will be saved +} + +the response contains +{'status': True,'url': url, 'application': application_name, 'features': features} + +or if an error occured +{'status': False,'message': "reason of the error"} \ No newline at end of file diff --git a/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/datasetmaker.egg-info/PKG-INFO b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/datasetmaker.egg-info/PKG-INFO new file mode 100644 index 00000000..384bb8a3 --- /dev/null +++ b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/datasetmaker.egg-info/PKG-INFO @@ -0,0 +1,79 @@ +Metadata-Version: 1.0 +Name: datasetmaker +Version: 0.0.1 +Summary: Python package for creating a dataset using InfluxDB data points +Home-page: http://git.dac.ds.unipi.gr/morphemic/datasetmaker +Author: Jean-Didier Totow +Author-email: totow@unipi.gr +License: LICENSE.txt +Description: 1. Generality + + Dataset maker is morphemic python library for + building dataset from data points registered into InfluxDB. + Dataset maker receives the name of an application, the start time + and the tolerance interval. More details are provided below. + + 2. InfluxDB format + + Data points in InfluxDB should have the following format for being used + correctly by the dataset maker: + + measurement : "application_name" #mandatory + timestamp : timestamp #optional + fields : dictionnary containing metric exposed by the given application + cpu_usage, memory_consumption, response_time, http_latency + tags : dictionnary of metrics related information + + The JSON describing the above information is the following: + + Ex.: + {"measurement": "application_name", + "timestamp": 155655476.453, + "fields": { + "cpu_usage": 40, + "memory_consumption": 67.9, + "response_time": 28, + "http_latency": 12 + }, + "tags": { + "core": 2 #cpu_usage of 40% is the usage of the cpu core number 2 + } + } + + If data points are presented as the above format, the dataset maker will output + a csv (application_name.csv) file with the following schema: + time, cpu_usage, memory_consumption, response_time, http_latency, core + + 3. Usage + + + Warming : make sure the above variables exist before importing dataset make library + + from morphemic.dataset import DatasetMaker + + data_maker = DatasetMaker(application, start, configs) + response = data_maker.make() + + application, string containing the application name + start, when to start building the dataset + Ex.: '10m' , build dataset containg data point stored the 10 last minute + Ex.: '3h', three hours + Ex.: '4d', four days + leave empty or set to None if you wish all data points stored in your InfluxDB + configs is dictionnary containg parameters + + { + "hostname": hostname or IP of InfluxDB + "port": port of InfluxDB + "username": InfluxDB username + "password": password of the above user + "dbname": database name + "path_dataset": path where the dataset will be saved + } + + the response contains + {'status': True,'url': url, 'application': application_name, 'features': features} + + or if an error occured + {'status': False,'message': "reason of the error"} +Platform: UNKNOWN diff --git a/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/datasetmaker.egg-info/SOURCES.txt b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/datasetmaker.egg-info/SOURCES.txt new file mode 100644 index 00000000..3d8cbf44 --- /dev/null +++ b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/datasetmaker.egg-info/SOURCES.txt @@ -0,0 +1,9 @@ +README.txt +setup.py +datasetmaker.egg-info/PKG-INFO +datasetmaker.egg-info/SOURCES.txt +datasetmaker.egg-info/dependency_links.txt +datasetmaker.egg-info/requires.txt +datasetmaker.egg-info/top_level.txt +morphemic/__init__.py +morphemic/dataset/__init__.py \ No newline at end of file diff --git a/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/datasetmaker.egg-info/dependency_links.txt b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/datasetmaker.egg-info/dependency_links.txt new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/datasetmaker.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/datasetmaker.egg-info/requires.txt b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/datasetmaker.egg-info/requires.txt new file mode 100644 index 00000000..e20f7f05 --- /dev/null +++ b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/datasetmaker.egg-info/requires.txt @@ -0,0 +1,2 @@ +pandas +influxdb diff --git a/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/datasetmaker.egg-info/top_level.txt b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/datasetmaker.egg-info/top_level.txt new file mode 100644 index 00000000..047ceb83 --- /dev/null +++ b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/datasetmaker.egg-info/top_level.txt @@ -0,0 +1 @@ +morphemic diff --git a/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/morphemic/__init__.py b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/morphemic/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/morphemic/__pycache__/__init__.cpython-36.pyc b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/morphemic/__pycache__/__init__.cpython-36.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b4a41f6352cbab9846bba6c984fe6bbaef058d5e GIT binary patch literal 152 zcmXr!<>flbqME<}1dl-k3@`#24nSPY0whuxf*CX!{Z=v*frJsnFIWAH{M=OitklFj z-IUCf%+wg`kf|D$&2_X70h=2h`Aj1KOi&=m~3PUi1CZpd2KczG$)edBQF%UBV09DW?egFUf literal 0 HcmV?d00001 diff --git a/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/morphemic/dataset/__init__.py b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/morphemic/dataset/__init__.py new file mode 100644 index 00000000..db2c96f2 --- /dev/null +++ b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/morphemic/dataset/__init__.py @@ -0,0 +1,154 @@ +import os, json, time +from influxdb import InfluxDBClient +import pandas as pd +from datetime import datetime + +url_path_dataset = None + +class Row(): + def __init__(self, features,metricsname): + self.features = features + if "time" in self.features: + time_str = self.features["time"] + _obj = datetime.strptime(time_str,'%Y-%m-%dT%H:%M:%S.%fZ') + self.features["time"] = int(_obj.timestamp()) + if 'application' in metricsname: + metricsname.remove('application') + for field_name in metricsname: + if not field_name in self.features: + self.features[field_name] = None + + def getTime(self): + if "time" in self.features: + return self.features["time"] + if "timestamp" in self.features: + return self.features["timestamp"] + return None + + def makeCsvRow(self): + if "application" in self.features: + del self.features["application"] + result = '' + for key, _value in self.features.items(): + result += "{0},".format(_value) + return result[:-1] + "\n" + +class Dataset(): + def __init__(self): + self.rows = {} + self.size = 0 + def addRow(self,row): + self.rows[row.getTime()] = row + self.size +=1 + def reset(self): + self.rows = {} + self.size = 0 + print("Dataset reset") + def getSize(self): + return self.size + def sortRows(self): + return sorted(list(self.rows.values()), key=lambda x: x.getTime(), reverse=True) + def getRows(self): + return list(self.rows.values()) + def getRow(self,_time, tolerance): + for i in range(tolerance): + if int(_time + i) in self.rows: + return self.rows[int(_time+i)] + return None + def save(self,metricnames,application_name): + if "application" in metricnames: + metricnames.remove("application") + dataset_content = '' + for metric in metricnames: + dataset_content += "{0},".format(metric) + dataset_content = dataset_content[:-1] + "\n" + for row in list(self.rows.values()): + dataset_content += row.makeCsvRow() + _file = open(url_path_dataset + "{0}.csv".format(application_name),'w') + _file.write(dataset_content) + _file.close() + return url_path_dataset + "{0}.csv".format(application_name) + +class DatasetMaker(): + def __init__(self, application, start, configs): + self.application = application + self.start_filter = start + self.influxdb = InfluxDBClient(host=configs['hostname'], port=configs['port'], username=configs['username'], password=configs['password'], database=configs['dbname']) + self.dataset = Dataset() + self.tolerance = 5 + global url_path_dataset + url_path_dataset = configs['path_dataset'] + if url_path_dataset[-1] != "/": + url_path_dataset += "/" + + def getIndex(self, columns, name): + return columns.index(name) + + def makeRow(self,columns, values): + row = {} + index = 0 + for column in columns: + row[column] = values[index] + index +=1 + return row + + def prepareResultSet(self, result_set): + result = [] + columns = result_set["series"][0]["columns"] + series_values = result_set["series"][0]["values"] + index = 0 + for _values in series_values: + row = self.makeRow(columns,_values) + result.append(row) + return result + + def make(self): + try: + self.influxdb.ping() + except Exception as e: + print("Could not establish connexion with InfluxDB, please verify connexion parameters") + print(e) + return {"message": "Could not establish connexion with InfluxDB, please verify connexion parameters"} + if self.getData() == None: + return {"message":"No data found"} + + metricnames, _data = self.getData() + for _row in _data: + row = Row(_row,metricnames) + self.dataset.addRow(row) + + print("Rows construction completed") + print("{0} rows found".format(self.dataset.getSize())) + #self.dataset.sortRows() + url = self.dataset.save(metricnames,self.application) + features = self.getFeatures(url) + if features == None: + return {'status': False, 'message': 'An error occured while building dataset'} + return {'status': True,'url': url, 'application': self.application, 'features': features} + + def getFeatures(self, url): + try: + df = pd.read_csv(url) + return df.columns.to_list() + except Exception as e: + print("Cannot extract data feature list") + return None + + def extractMeasurement(self, _json): + return _json["series"][0]["columns"] + + def getData(self): + query = None + try: + if self.start_filter != None and self.start_filter != "": + query = "SELECT * FROM " + self.application +" WHERE time > now() - "+ self.start_filter + else: + query = "SELECT * FROM " + self.application + result_set = self.influxdb.query(query=query) + series = self.extractMeasurement(result_set.raw) + #self.influxdb.close() #closing connexion + return [series, self.prepareResultSet(result_set.raw)] + except Exception as e: + print("Could not collect query data points") + print(e) + return None diff --git 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b/forecasting-gluonmachines/gluonts/morphemic/morphemic-datasetmaker/setup.py @@ -0,0 +1,18 @@ +from setuptools import setup + +setup( + name = 'datasetmaker', + version = '0.0.1', + author = 'Jean-Didier Totow', + author_email = 'totow@unipi.gr', + packages = ['morphemic', 'morphemic.dataset'], + scripts = [], + url='http://git.dac.ds.unipi.gr/morphemic/datasetmaker', + license='LICENSE.txt', + description='Python package for creating a dataset using InfluxDB data points', + long_description=open('README.txt').read(), + install_requires=[ + "pandas", + "influxdb", + ], +) diff --git a/forecasting-gluonmachines/gluonts/morphemic/morphemic-performance-model/README.md b/forecasting-gluonmachines/gluonts/morphemic/morphemic-performance-model/README.md new file mode 100644 index 00000000..9b2a2443 --- /dev/null +++ b/forecasting-gluonmachines/gluonts/morphemic/morphemic-performance-model/README.md @@ -0,0 +1,9 @@ +# Performance Model +A task under the [Morphemic project](https://www.morphemic.cloud/). + +This is an experimental repository in which we try to predict the performance of a service. Our model expects as input some configuration variables and provides as output an estimated performance. Our model is trained on history data that contains the configuration variables for a service and the current real resource allocation in course of time. + + +![](images/Morphemic-Page-2.png) + +The performance model image can be built using the script /ml_code/build-performance-model.sh \ No newline at end of file diff --git a/forecasting-gluonmachines/gluonts/morphemic/morphemic-performance-model/data/README.md b/forecasting-gluonmachines/gluonts/morphemic/morphemic-performance-model/data/README.md new file mode 100644 index 00000000..17f01f9b --- /dev/null +++ b/forecasting-gluonmachines/gluonts/morphemic/morphemic-performance-model/data/README.md @@ -0,0 +1,12 @@ +## Directory *data/* + +It's a tidy place where we maintain our datasets, as simple & clear as possible, in order to be clear for all what to use for their experiments. Descriptive titles will be extremely helpful. + +### For the secure document dataset ### + +Metrics: time,AvgResponseTime,nrOfInstances,AvgMemory,AvgCPU,numebrOfRequest,Latency +Variables: nrOfInstances (the name of the file contains the cpu variable and memory variable) +those values should added in the dataset as column. + +This dataset has a very low quality, it is a good opportunity to apply some data imputation technics + diff --git a/forecasting-gluonmachines/gluonts/morphemic/morphemic-performance-model/data/Time series prediction/connected_consumer/result_0.csv b/forecasting-gluonmachines/gluonts/morphemic/morphemic-performance-model/data/Time series prediction/connected_consumer/result_0.csv new file mode 100644 index 00000000..47851406 --- /dev/null +++ b/forecasting-gluonmachines/gluonts/morphemic/morphemic-performance-model/data/Time series prediction/connected_consumer/result_0.csv @@ -0,0 +1,8642 @@ 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