diff --git a/morphemic-performance-model/.DS_Store b/morphemic-performance-model/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..2f9d9c1dd5794c72e36466d9c07b9c6834dde627 Binary files /dev/null and b/morphemic-performance-model/.DS_Store differ diff --git a/morphemic-performance-model/ml_code/.DS_Store b/morphemic-performance-model/ml_code/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..b59e5d077f5d5669b42c98c306c92b36d10fb680 Binary files /dev/null and b/morphemic-performance-model/ml_code/.DS_Store differ diff --git a/morphemic-performance-model/ml_code/deployment/.DS_Store b/morphemic-performance-model/ml_code/deployment/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..f95115bbe6b096a1db5e4229d61ee4e3427555d1 Binary files /dev/null and b/morphemic-performance-model/ml_code/deployment/.DS_Store differ diff --git a/morphemic-performance-model/ml_code/deployment/supervisord.conf b/morphemic-performance-model/ml_code/deployment/supervisord.conf index b8ce48019d6a94a73f2a954d54f4c60aaa0c793a..004c7704a3e90ec32fd4323d0ca171bf17b577be 100755 --- a/morphemic-performance-model/ml_code/deployment/supervisord.conf +++ b/morphemic-performance-model/ml_code/deployment/supervisord.conf @@ -43,3 +43,10 @@ stdout_logfile=/dev/stdout stdout_logfile_maxbytes=0 stderr_logfile=/dev/stderr stderr_logfile_maxbytes=0 + +[program:activemq_listener] +command=/app/start_activemq_listener.sh +stdout_logfile=/dev/stdout +stdout_logfile_maxbytes=0 +stderr_logfile=/dev/stderr +stderr_logfile_maxbytes=0 diff --git a/morphemic-performance-model/ml_code/example/simple-2.py b/morphemic-performance-model/ml_code/example/simple-2.py index 2aa3e3df2638c98f01d6e882969eda7744ebc223..b7e9b6b8675ad6d737bafeed73d1d10427bd7d1f 100644 --- a/morphemic-performance-model/ml_code/example/simple-2.py +++ b/morphemic-performance-model/ml_code/example/simple-2.py @@ -17,6 +17,7 @@ features_4 = ["EstimatedRemainingTimeContext","SimulationLeftNumber","Simulation #post_data = {'url_file': url_file, 'application': 'application-1','target':'performance','features': features} #post_data_2 = {'url_file': "", 'application': 'demo','target':'response_time','features': features_2, "variant": "vm"} #post_data_3 = {'url_file': url_file_3, 'application': 'application-3','target':'performance','features': features_3} +# "default_application" post_data_4 = {'url_file': url_file_4, 'application': 'genome','target':'EstimatedRemainingTimeContext','features': features_4,'variant':'vm'} #print("Get model") #response = requests.post(url+"/api/v1/model", data='{"application":"application-2"}', headers={'Content-Type':'application/json'}) @@ -34,4 +35,29 @@ print(response) #features = {'cpu_usage': 31, "memory": 4500.23, 'latency': 2.1, 'level': 1} #post_data = {'application': 'fcr','target':'response_time','features':features} #response = requests.post(url+"/api/v1/model/predict", data=json.dumps(post_data),headers={'Content-Type':'application/json'}).text -#print(response) \ No newline at end of file +#print(response) + +# utility : input (configs) - Mem , Cpu, Number vm provided to an application +# utility : metrics (formula) - datasets +# ?? +# utility : output (utility value) +""" +Training +topic : performance_module_train +data: [ + {'application': 'genome','target':'EstimatedRemainingTimeContext','features': features_4,'variant':'vm', 'sender_id':'solver_id'}, + {'application': 'genome','target':'SimulationLeftNumber','features': features_4,'variant':'vm'}, +] + +Predict +topic : performance_module_predict +data : {'application': 'fcr','target':'response_time','features':features, 'variant': 'vm', 'sender_id':'solver_id'} + +Prediction response +topic : performance_module_predict_feedback +data : {'application': 'app_name', 'target': 'targeted_field', 'variant': 'vm', 'value':'value_predicted (float)', 'sender_id': 'solver-id'} + +Feedback +topic : performance_module_train_feedback +data : {'application': 'application_name', 'target': 'SimulationLeftNumber', 'variant': 'vm', 'sender_id':'solver_id'} +""" \ No newline at end of file diff --git a/morphemic-performance-model/ml_code/service_pb2.py b/morphemic-performance-model/ml_code/service_pb2.py index c7d5fb537718f3fc94dbff0687e43650d06b98d2..30f85b915e531701ac02acbfbc96aef89c0b48c4 100644 --- a/morphemic-performance-model/ml_code/service_pb2.py +++ b/morphemic-performance-model/ml_code/service_pb2.py @@ -1,9 +1,9 @@ +# -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. 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], - serialized_start=544, - serialized_end=595, -) -_MODELREPLY = _descriptor.Descriptor( - name='ModelReply', - full_name='proto.ModelReply', - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name='results', full_name='proto.ModelReply.results', index=0, - number=1, type=11, cpp_type=10, label=2, - has_default_value=False, default_value=None, - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR), - ], - extensions=[ - ], - nested_types=[], - enum_types=[ - ], - serialized_options=None, - is_extendable=False, - syntax='proto2', - extension_ranges=[], - oneofs=[ - ], - serialized_start=597, - serialized_end=645, -) - - -_LISTVALUE = _descriptor.Descriptor( - name='ListValue', - full_name='proto.ListValue', - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name='listOfStrings', full_name='proto.ListValue.listOfStrings', index=0, - number=1, type=11, cpp_type=10, label=3, - has_default_value=False, default_value=[], - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR), - ], - extensions=[ - ], - nested_types=[], - enum_types=[ - ], - serialized_options=None, - is_extendable=False, - syntax='proto2', - extension_ranges=[], - oneofs=[ - ], - serialized_start=647, - serialized_end=703, -) - - -_LISTOFSTRINGS = _descriptor.Descriptor( - name='ListOfStrings', - full_name='proto.ListOfStrings', - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name='strings', full_name='proto.ListOfStrings.strings', index=0, - number=1, type=9, cpp_type=9, label=3, - has_default_value=False, default_value=[], - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR), - ], - extensions=[ - ], - nested_types=[], - enum_types=[ - ], - serialized_options=None, - is_extendable=False, - syntax='proto2', - extension_ranges=[], - oneofs=[ - ], - serialized_start=705, - serialized_end=737, -) - - -_TRAINREQUEST = _descriptor.Descriptor( - name='TrainRequest', - full_name='proto.TrainRequest', - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name='application', full_name='proto.TrainRequest.application', index=0, - number=1, type=9, cpp_type=9, label=2, - has_default_value=False, default_value=_b("").decode('utf-8'), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR), - _descriptor.FieldDescriptor( - name='url_file', full_name='proto.TrainRequest.url_file', index=1, - number=2, type=9, cpp_type=9, label=2, - has_default_value=False, default_value=_b("").decode('utf-8'), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR), - _descriptor.FieldDescriptor( - name='features', full_name='proto.TrainRequest.features', index=2, - number=3, type=11, cpp_type=10, label=2, - has_default_value=False, default_value=None, - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR), - _descriptor.FieldDescriptor( - name='target', full_name='proto.TrainRequest.target', index=3, - number=4, type=9, cpp_type=9, label=2, - has_default_value=False, default_value=_b("").decode('utf-8'), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR), - _descriptor.FieldDescriptor( - name='variant', full_name='proto.TrainRequest.variant', index=4, - number=5, type=9, cpp_type=9, label=2, - has_default_value=False, default_value=_b("").decode('utf-8'), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR), - ], - extensions=[ - ], - nested_types=[], - enum_types=[ - ], - serialized_options=None, - is_extendable=False, - syntax='proto2', - extension_ranges=[], - oneofs=[ - ], - serialized_start=739, - serialized_end=865, -) - - -_TRAINREPLY = _descriptor.Descriptor( - name='TrainReply', - full_name='proto.TrainReply', - filename=None, - file=DESCRIPTOR, - containing_type=None, - fields=[ - _descriptor.FieldDescriptor( - name='status', full_name='proto.TrainReply.status', index=0, - number=1, type=8, cpp_type=7, label=2, - has_default_value=False, default_value=False, - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR), - _descriptor.FieldDescriptor( - name='message', full_name='proto.TrainReply.message', index=1, - number=2, type=9, cpp_type=9, label=2, - has_default_value=False, default_value=_b("").decode('utf-8'), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR), - _descriptor.FieldDescriptor( - name='application', full_name='proto.TrainReply.application', index=2, - number=3, type=9, cpp_type=9, label=2, - has_default_value=False, default_value=_b("").decode('utf-8'), - message_type=None, enum_type=None, containing_type=None, - is_extension=False, extension_scope=None, - serialized_options=None, file=DESCRIPTOR), - ], - extensions=[ - ], - nested_types=[], - enum_types=[ - ], - serialized_options=None, - is_extendable=False, - syntax='proto2', - extension_ranges=[], - oneofs=[ - ], - serialized_start=867, - serialized_end=933, -) - -_ANY.fields_by_name['list_value'].message_type = _LISTOFSTRINGS -_ANY.oneofs_by_name['kind'].fields.append( - _ANY.fields_by_name['int_value']) -_ANY.fields_by_name['int_value'].containing_oneof = _ANY.oneofs_by_name['kind'] -_ANY.oneofs_by_name['kind'].fields.append( - _ANY.fields_by_name['float_value']) -_ANY.fields_by_name['float_value'].containing_oneof = _ANY.oneofs_by_name['kind'] -_ANY.oneofs_by_name['kind'].fields.append( - _ANY.fields_by_name['string_value']) -_ANY.fields_by_name['string_value'].containing_oneof = _ANY.oneofs_by_name['kind'] -_ANY.oneofs_by_name['kind'].fields.append( - _ANY.fields_by_name['list_value']) -_ANY.fields_by_name['list_value'].containing_oneof = _ANY.oneofs_by_name['kind'] -_PAIR.fields_by_name['value'].message_type = _ANY -_DICTIONARY_FIELDSENTRY.fields_by_name['value'].message_type = _ANY -_DICTIONARY_FIELDSENTRY.containing_type = _DICTIONARY -_DICTIONARY.fields_by_name['fields'].message_type = _DICTIONARY_FIELDSENTRY -_PREDICTREQUEST.fields_by_name['features'].message_type = _DICTIONARY -_PREDICTREPLY.fields_by_name['results'].message_type = _DICTIONARY -_PREDICTREPLY.fields_by_name['ml'].message_type = _DICTIONARY -_MODELREPLY.fields_by_name['results'].message_type = _DICTIONARY -_LISTVALUE.fields_by_name['listOfStrings'].message_type = _LISTOFSTRINGS -_TRAINREQUEST.fields_by_name['features'].message_type = _LISTOFSTRINGS -DESCRIPTOR.message_types_by_name['Any'] = _ANY -DESCRIPTOR.message_types_by_name['Pair'] = _PAIR -DESCRIPTOR.message_types_by_name['Dictionary'] = _DICTIONARY -DESCRIPTOR.message_types_by_name['PredictRequest'] = _PREDICTREQUEST -DESCRIPTOR.message_types_by_name['PredictReply'] = _PREDICTREPLY -DESCRIPTOR.message_types_by_name['ModelRequest'] = _MODELREQUEST -DESCRIPTOR.message_types_by_name['ModelReply'] = _MODELREPLY -DESCRIPTOR.message_types_by_name['ListValue'] = _LISTVALUE -DESCRIPTOR.message_types_by_name['ListOfStrings'] = _LISTOFSTRINGS -DESCRIPTOR.message_types_by_name['TrainRequest'] = _TRAINREQUEST -DESCRIPTOR.message_types_by_name['TrainReply'] = _TRAINREPLY -_sym_db.RegisterFileDescriptor(DESCRIPTOR) - -Any = _reflection.GeneratedProtocolMessageType('Any', (_message.Message,), dict( - DESCRIPTOR = _ANY, - __module__ = 'service_pb2' +_ANY = DESCRIPTOR.message_types_by_name['Any'] +_PAIR = DESCRIPTOR.message_types_by_name['Pair'] +_DICTIONARY = DESCRIPTOR.message_types_by_name['Dictionary'] +_DICTIONARY_FIELDSENTRY = _DICTIONARY.nested_types_by_name['FieldsEntry'] +_PREDICTREQUEST = DESCRIPTOR.message_types_by_name['PredictRequest'] +_PREDICTREPLY = DESCRIPTOR.message_types_by_name['PredictReply'] +_MODELREQUEST = DESCRIPTOR.message_types_by_name['ModelRequest'] +_MODELREPLY = DESCRIPTOR.message_types_by_name['ModelReply'] +_LISTVALUE = DESCRIPTOR.message_types_by_name['ListValue'] +_LISTOFSTRINGS = DESCRIPTOR.message_types_by_name['ListOfStrings'] +_TRAINREQUEST = DESCRIPTOR.message_types_by_name['TrainRequest'] +_TRAINREPLY = DESCRIPTOR.message_types_by_name['TrainReply'] +Any = _reflection.GeneratedProtocolMessageType('Any', (_message.Message,), { + 'DESCRIPTOR' : _ANY, + '__module__' : 'service_pb2' # @@protoc_insertion_point(class_scope:proto.Any) - )) + }) _sym_db.RegisterMessage(Any) -Pair = _reflection.GeneratedProtocolMessageType('Pair', (_message.Message,), dict( - DESCRIPTOR = _PAIR, - __module__ = 'service_pb2' +Pair = _reflection.GeneratedProtocolMessageType('Pair', (_message.Message,), { + 'DESCRIPTOR' : _PAIR, + '__module__' : 'service_pb2' # @@protoc_insertion_point(class_scope:proto.Pair) - )) + }) _sym_db.RegisterMessage(Pair) -Dictionary = _reflection.GeneratedProtocolMessageType('Dictionary', (_message.Message,), dict( +Dictionary = _reflection.GeneratedProtocolMessageType('Dictionary', (_message.Message,), { - FieldsEntry = _reflection.GeneratedProtocolMessageType('FieldsEntry', (_message.Message,), dict( - DESCRIPTOR = _DICTIONARY_FIELDSENTRY, - __module__ = 'service_pb2' + 'FieldsEntry' : _reflection.GeneratedProtocolMessageType('FieldsEntry', (_message.Message,), { + 'DESCRIPTOR' : _DICTIONARY_FIELDSENTRY, + '__module__' : 'service_pb2' # @@protoc_insertion_point(class_scope:proto.Dictionary.FieldsEntry) - )) + }) , - DESCRIPTOR = _DICTIONARY, - __module__ = 'service_pb2' + 'DESCRIPTOR' : _DICTIONARY, + '__module__' : 'service_pb2' # @@protoc_insertion_point(class_scope:proto.Dictionary) - )) + }) _sym_db.RegisterMessage(Dictionary) _sym_db.RegisterMessage(Dictionary.FieldsEntry) -PredictRequest = _reflection.GeneratedProtocolMessageType('PredictRequest', (_message.Message,), dict( - DESCRIPTOR = _PREDICTREQUEST, - __module__ = 'service_pb2' +PredictRequest = _reflection.GeneratedProtocolMessageType('PredictRequest', (_message.Message,), { + 'DESCRIPTOR' : _PREDICTREQUEST, + '__module__' : 'service_pb2' # @@protoc_insertion_point(class_scope:proto.PredictRequest) - )) + }) _sym_db.RegisterMessage(PredictRequest) -PredictReply = _reflection.GeneratedProtocolMessageType('PredictReply', (_message.Message,), dict( - DESCRIPTOR = _PREDICTREPLY, - __module__ = 'service_pb2' +PredictReply = _reflection.GeneratedProtocolMessageType('PredictReply', (_message.Message,), { + 'DESCRIPTOR' : _PREDICTREPLY, + '__module__' : 'service_pb2' # @@protoc_insertion_point(class_scope:proto.PredictReply) - )) + }) _sym_db.RegisterMessage(PredictReply) -ModelRequest = _reflection.GeneratedProtocolMessageType('ModelRequest', (_message.Message,), dict( - DESCRIPTOR = _MODELREQUEST, - __module__ = 'service_pb2' +ModelRequest = _reflection.GeneratedProtocolMessageType('ModelRequest', (_message.Message,), { + 'DESCRIPTOR' : _MODELREQUEST, + '__module__' : 'service_pb2' # @@protoc_insertion_point(class_scope:proto.ModelRequest) - )) + }) _sym_db.RegisterMessage(ModelRequest) -ModelReply = _reflection.GeneratedProtocolMessageType('ModelReply', (_message.Message,), dict( - DESCRIPTOR = _MODELREPLY, - __module__ = 'service_pb2' +ModelReply = _reflection.GeneratedProtocolMessageType('ModelReply', (_message.Message,), { + 'DESCRIPTOR' : _MODELREPLY, + '__module__' : 'service_pb2' # @@protoc_insertion_point(class_scope:proto.ModelReply) - )) + }) _sym_db.RegisterMessage(ModelReply) -ListValue = _reflection.GeneratedProtocolMessageType('ListValue', (_message.Message,), dict( - DESCRIPTOR = _LISTVALUE, - __module__ = 'service_pb2' +ListValue = _reflection.GeneratedProtocolMessageType('ListValue', (_message.Message,), { + 'DESCRIPTOR' : _LISTVALUE, + '__module__' : 'service_pb2' # @@protoc_insertion_point(class_scope:proto.ListValue) - )) + }) _sym_db.RegisterMessage(ListValue) -ListOfStrings = _reflection.GeneratedProtocolMessageType('ListOfStrings', (_message.Message,), dict( - DESCRIPTOR = _LISTOFSTRINGS, - __module__ = 'service_pb2' +ListOfStrings = _reflection.GeneratedProtocolMessageType('ListOfStrings', (_message.Message,), { + 'DESCRIPTOR' : _LISTOFSTRINGS, + '__module__' : 'service_pb2' # @@protoc_insertion_point(class_scope:proto.ListOfStrings) - )) + }) _sym_db.RegisterMessage(ListOfStrings) -TrainRequest = _reflection.GeneratedProtocolMessageType('TrainRequest', (_message.Message,), dict( - DESCRIPTOR = _TRAINREQUEST, - __module__ = 'service_pb2' +TrainRequest = _reflection.GeneratedProtocolMessageType('TrainRequest', (_message.Message,), { + 'DESCRIPTOR' : _TRAINREQUEST, + '__module__' : 'service_pb2' # @@protoc_insertion_point(class_scope:proto.TrainRequest) - )) + }) _sym_db.RegisterMessage(TrainRequest) -TrainReply = _reflection.GeneratedProtocolMessageType('TrainReply', (_message.Message,), dict( - DESCRIPTOR = _TRAINREPLY, - __module__ = 'service_pb2' +TrainReply = _reflection.GeneratedProtocolMessageType('TrainReply', (_message.Message,), { + 'DESCRIPTOR' : _TRAINREPLY, + '__module__' : 'service_pb2' # @@protoc_insertion_point(class_scope:proto.TrainReply) - )) + }) _sym_db.RegisterMessage(TrainReply) - -_DICTIONARY_FIELDSENTRY._options = None - -_PREDICT = _descriptor.ServiceDescriptor( - name='Predict', - full_name='proto.Predict', - file=DESCRIPTOR, - index=0, - serialized_options=None, - serialized_start=936, - serialized_end=1123, - methods=[ - _descriptor.MethodDescriptor( - name='PredictPerformance', - full_name='proto.Predict.PredictPerformance', - index=0, - containing_service=None, - input_type=_PREDICTREQUEST, - output_type=_PREDICTREPLY, - serialized_options=None, - ), - _descriptor.MethodDescriptor( - name='getModel', - full_name='proto.Predict.getModel', - index=1, - containing_service=None, - input_type=_MODELREQUEST, - output_type=_MODELREPLY, - serialized_options=None, - ), - _descriptor.MethodDescriptor( - name='trainModel', - full_name='proto.Predict.trainModel', - index=2, - containing_service=None, - input_type=_TRAINREQUEST, - output_type=_TRAINREPLY, - serialized_options=None, - ), -]) -_sym_db.RegisterServiceDescriptor(_PREDICT) - -DESCRIPTOR.services_by_name['Predict'] = _PREDICT - +_PREDICT = DESCRIPTOR.services_by_name['Predict'] +if _descriptor._USE_C_DESCRIPTORS == False: + + DESCRIPTOR._options = None + _DICTIONARY_FIELDSENTRY._options = None + _DICTIONARY_FIELDSENTRY._serialized_options = b'8\001' + _ANY._serialized_start=24 + _ANY._serialized_end=149 + _PAIR._serialized_start=151 + _PAIR._serialized_end=197 + _DICTIONARY._serialized_start=199 + _DICTIONARY._serialized_end=317 + _DICTIONARY_FIELDSENTRY._serialized_start=260 + _DICTIONARY_FIELDSENTRY._serialized_end=317 + _PREDICTREQUEST._serialized_start=320 + _PREDICTREQUEST._serialized_end=464 + _PREDICTREPLY._serialized_start=467 + _PREDICTREPLY._serialized_end=600 + _MODELREQUEST._serialized_start=602 + _MODELREQUEST._serialized_end=688 + _MODELREPLY._serialized_start=690 + _MODELREPLY._serialized_end=738 + _LISTVALUE._serialized_start=740 + _LISTVALUE._serialized_end=796 + _LISTOFSTRINGS._serialized_start=798 + _LISTOFSTRINGS._serialized_end=830 + _TRAINREQUEST._serialized_start=833 + _TRAINREQUEST._serialized_end=996 + _TRAINREPLY._serialized_start=998 + _TRAINREPLY._serialized_end=1083 + _PREDICT._serialized_start=1086 + _PREDICT._serialized_end=1273 # @@protoc_insertion_point(module_scope) diff --git a/morphemic-performance-model/ml_code/service_pb2_grpc.py b/morphemic-performance-model/ml_code/service_pb2_grpc.py index e950b9c25d5d160e8482cbf1496fe3d4ab4dc5b5..1bd30fcdc53d5c19a84c80a1102a8e241829b713 100644 --- a/morphemic-performance-model/ml_code/service_pb2_grpc.py +++ b/morphemic-performance-model/ml_code/service_pb2_grpc.py @@ -1,80 +1,132 @@ # Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! +"""Client and server classes corresponding to protobuf-defined services.""" import grpc import service_pb2 as service__pb2 class PredictStub(object): - # missing associated documentation comment in .proto file - pass - - def __init__(self, channel): - """Constructor. - - Args: - channel: A grpc.Channel. - """ - self.PredictPerformance = channel.unary_unary( - '/proto.Predict/PredictPerformance', - request_serializer=service__pb2.PredictRequest.SerializeToString, - response_deserializer=service__pb2.PredictReply.FromString, - ) - self.getModel = channel.unary_unary( - '/proto.Predict/getModel', - request_serializer=service__pb2.ModelRequest.SerializeToString, - response_deserializer=service__pb2.ModelReply.FromString, - ) - self.trainModel = channel.unary_unary( - '/proto.Predict/trainModel', - request_serializer=service__pb2.TrainRequest.SerializeToString, - response_deserializer=service__pb2.TrainReply.FromString, - ) + """Missing associated documentation comment in .proto file.""" + + def __init__(self, channel): + """Constructor. + + Args: + channel: A grpc.Channel. + """ + self.PredictPerformance = channel.unary_unary( + '/proto.Predict/PredictPerformance', + request_serializer=service__pb2.PredictRequest.SerializeToString, + response_deserializer=service__pb2.PredictReply.FromString, + ) + self.getModel = channel.unary_unary( + '/proto.Predict/getModel', + request_serializer=service__pb2.ModelRequest.SerializeToString, + response_deserializer=service__pb2.ModelReply.FromString, + ) + self.trainModel = channel.unary_unary( + '/proto.Predict/trainModel', + request_serializer=service__pb2.TrainRequest.SerializeToString, + response_deserializer=service__pb2.TrainReply.FromString, + ) class PredictServicer(object): - # missing associated documentation comment in .proto file - pass - - def PredictPerformance(self, request, context): - # missing associated documentation comment in .proto file - pass - context.set_code(grpc.StatusCode.UNIMPLEMENTED) - context.set_details('Method not implemented!') - raise NotImplementedError('Method not implemented!') - - def getModel(self, request, context): - # missing associated documentation comment in .proto file - pass - context.set_code(grpc.StatusCode.UNIMPLEMENTED) - context.set_details('Method not implemented!') - raise NotImplementedError('Method not implemented!') - - def trainModel(self, request, context): - # missing associated documentation comment in .proto file - pass - context.set_code(grpc.StatusCode.UNIMPLEMENTED) - context.set_details('Method not implemented!') - raise NotImplementedError('Method not implemented!') + """Missing associated documentation comment in .proto file.""" + + def PredictPerformance(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def getModel(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def trainModel(self, request, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') def add_PredictServicer_to_server(servicer, server): - rpc_method_handlers = { - 'PredictPerformance': grpc.unary_unary_rpc_method_handler( - servicer.PredictPerformance, - request_deserializer=service__pb2.PredictRequest.FromString, - response_serializer=service__pb2.PredictReply.SerializeToString, - ), - 'getModel': grpc.unary_unary_rpc_method_handler( - servicer.getModel, - request_deserializer=service__pb2.ModelRequest.FromString, - response_serializer=service__pb2.ModelReply.SerializeToString, - ), - 'trainModel': grpc.unary_unary_rpc_method_handler( - servicer.trainModel, - request_deserializer=service__pb2.TrainRequest.FromString, - response_serializer=service__pb2.TrainReply.SerializeToString, - ), - } - generic_handler = grpc.method_handlers_generic_handler( - 'proto.Predict', rpc_method_handlers) - server.add_generic_rpc_handlers((generic_handler,)) + rpc_method_handlers = { + 'PredictPerformance': grpc.unary_unary_rpc_method_handler( + servicer.PredictPerformance, + request_deserializer=service__pb2.PredictRequest.FromString, + response_serializer=service__pb2.PredictReply.SerializeToString, + ), + 'getModel': grpc.unary_unary_rpc_method_handler( + servicer.getModel, + request_deserializer=service__pb2.ModelRequest.FromString, + response_serializer=service__pb2.ModelReply.SerializeToString, + ), + 'trainModel': grpc.unary_unary_rpc_method_handler( + servicer.trainModel, + request_deserializer=service__pb2.TrainRequest.FromString, + response_serializer=service__pb2.TrainReply.SerializeToString, + ), + } + generic_handler = grpc.method_handlers_generic_handler( + 'proto.Predict', rpc_method_handlers) + server.add_generic_rpc_handlers((generic_handler,)) + + + # This class is part of an EXPERIMENTAL API. +class Predict(object): + """Missing associated documentation comment in .proto file.""" + + @staticmethod + def PredictPerformance(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary(request, target, '/proto.Predict/PredictPerformance', + service__pb2.PredictRequest.SerializeToString, + service__pb2.PredictReply.FromString, + options, channel_credentials, + insecure, call_credentials, compression, wait_for_ready, timeout, metadata) + + @staticmethod + def getModel(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary(request, target, '/proto.Predict/getModel', + service__pb2.ModelRequest.SerializeToString, + service__pb2.ModelReply.FromString, + options, channel_credentials, + insecure, call_credentials, compression, wait_for_ready, timeout, metadata) + + @staticmethod + def trainModel(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary(request, target, '/proto.Predict/trainModel', + service__pb2.TrainRequest.SerializeToString, + service__pb2.TrainReply.FromString, + options, channel_credentials, + insecure, call_credentials, compression, wait_for_ready, timeout, metadata) diff --git a/morphemic-performance-model/ml_code/src/.DS_Store b/morphemic-performance-model/ml_code/src/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..2a4f397ec1f2a5db30df5ed27edba11e46b3d161 Binary files /dev/null and b/morphemic-performance-model/ml_code/src/.DS_Store differ diff --git a/morphemic-performance-model/ml_code/src/__pycache__/ml_module.cpython-39.pyc b/morphemic-performance-model/ml_code/src/__pycache__/ml_module.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ad8c459089c1a210f91dca06c17407b7e0a07423 Binary files /dev/null and b/morphemic-performance-model/ml_code/src/__pycache__/ml_module.cpython-39.pyc differ diff --git a/morphemic-performance-model/ml_code/src/activemq/Event.py b/morphemic-performance-model/ml_code/src/activemq/Event.py new file mode 100644 index 0000000000000000000000000000000000000000..52dbc842ef3c729386bed8f8e7cd4b92bc9420c6 --- /dev/null +++ b/morphemic-performance-model/ml_code/src/activemq/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/morphemic-performance-model/ml_code/src/activemq/MorphemicConnection.py b/morphemic-performance-model/ml_code/src/activemq/MorphemicConnection.py new file mode 100644 index 0000000000000000000000000000000000000000..0bb86791f45598cd2b3056824a468a61fb2bf23c --- /dev/null +++ b/morphemic-performance-model/ml_code/src/activemq/MorphemicConnection.py @@ -0,0 +1,71 @@ +import stomp +import logging +import json + +from stomp.listener import PrintingListener + +class Connection: + + subscriptions = [] + + def __init__(self, username, password, + host='localhost', + port=61613, + debug=False): + 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, content_type, 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=content_type, + headers=headers, **kwargs) diff --git a/morphemic-performance-model/ml_code/src/activemq/MorphemicListener.py b/morphemic-performance-model/ml_code/src/activemq/MorphemicListener.py new file mode 100644 index 0000000000000000000000000000000000000000..35e2eeeb41cf864f2ca4d47dded4ee75e3c51721 --- /dev/null +++ b/morphemic-performance-model/ml_code/src/activemq/MorphemicListener.py @@ -0,0 +1,48 @@ +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, headers, 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='_',) + 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/morphemic-performance-model/ml_code/src/activemq/Payloads.py b/morphemic-performance-model/ml_code/src/activemq/Payloads.py new file mode 100644 index 0000000000000000000000000000000000000000..5de1adc844e289db185d74f7c7d76bca0045d686 --- /dev/null +++ b/morphemic-performance-model/ml_code/src/activemq/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/morphemic-performance-model/ml_code/src/activemq/__pycache__/MorphemicConnection.cpython-39.pyc b/morphemic-performance-model/ml_code/src/activemq/__pycache__/MorphemicConnection.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e0466453b571931bf6506a5c0cee0d941c11656b Binary files /dev/null and b/morphemic-performance-model/ml_code/src/activemq/__pycache__/MorphemicConnection.cpython-39.pyc differ diff --git a/morphemic-performance-model/ml_code/src/activemq_logic.py b/morphemic-performance-model/ml_code/src/activemq_logic.py new file mode 100644 index 0000000000000000000000000000000000000000..9cc7401b2cfda19936cc446c4c1e604f64a1377a --- /dev/null +++ b/morphemic-performance-model/ml_code/src/activemq_logic.py @@ -0,0 +1,234 @@ +from email.mime import application +import os, time, subprocess, json +from shutil import ExecError +from threading import Thread +from datetime import datetime +from activemq.MorphemicConnection import Connection +import stomp +#from initial_ML_module import Predictor +from morphemic.dataset import DatasetMaker +from ml_module import Trainer, Predictor + +activemq_username = os.environ.get("ACTIVEMQ_USER","aaa") +activemq_password = os.environ.get("ACTIVEMQ_PASSWORD","111") +activemq_hostname = os.environ.get("ACTIVEMQ_HOST","158.39.75.191") # +activemq_port = int(os.environ.get("ACTIVEMQ_PORT","61613")) + +influxdb_hostname = os.environ.get("INFLUXDB_HOSTNAME","localhost") +influxdb_port = int(os.environ.get("INFLUXDB_PORT","8086")) +influxdb_username = os.environ.get("INFLUXDB_USERNAME","morphemic") +influxdb_password = os.environ.get("INFLUXDB_PASSWORD","password") +influxdb_dbname = os.environ.get("INFLUXDB_DBNAME","morphemic") +influxdb_org = os.environ.get("INFLUXDB_ORG","morphemic") + +dataset_local_repository = os.environ.get("./datasets/") + +performance_module_train_topic = os.environ.get("PERFORMANCE_MODULE_TRAIN_TOPIC","/topic/performance_module_train") +performance_module_train_feedback_topic = os.environ.get("PERFORMANCE_MODULE_TRAIN_FEEDBACK_TOPIC","performance_module_train_feedback") +performance_module_predict_topic = os.environ.get("PERFORMANCE_MODULE_PREDICT_TOPIC","/topic/performance_module_predict") +performance_module_predict_feedback_topic = os.environ.get("PERFORMANCE_MODULE_PREDICT_FEEDBACK_TOPIC","performance_module_predict_feedback") + + +class Listener(object): + def __init__(self, conn,handler): + self.conn = conn + self.handler = handler + + def on_error(self, headers, message): + now = datetime.now() + _time_str = now.strftime("%m/%d/%Y, %H:%M:%S") + print('received an error {0} at {1}'.format(message,headers, _time_str)) + + def on_message(self, frame): + self.handler(frame.body) + +class Consumer(Thread): + def __init__(self, handler, queue): + self.handler = handler + self.queue = queue + super(Consumer,self).__init__() + + def run(self): + connected = False + while not connected: + try: + print('Subscribe to the topic {0}'.format(self.queue)) + #conn = stomp.Connection([(activemq_hostname,activemq_port)]) + conn = Connection(username=activemq_username, password=activemq_password, host=activemq_hostname,port=activemq_port,debug=True) + conn.connect() + #conn.connect(username=activemq_username,password=activemq_password) + conn.set_listener('', Listener(conn, self.handler)) + conn.subscribe(destination=self.queue, id=1, ack='auto') + connected = True + except Exception as e: + print("Could not subscribe") + print(e) + connected = False + time.sleep(2) + +class Publisher(Thread): + def __init__(self): + self.message = None + self.destination = None + self.client = None + self.content_type = "application/json" + super(Publisher, self).__init__() + + def setParameters(self, message, queue, content_type="application/json"): + self.message = message + self.queue = queue + self.content_type = content_type + + def run(self): + self.connect() + while True: + time.sleep(2) + + def connect(self): + while True: + try: + print('The publisher tries to connect to ActiveMQ broker') + #self.client = stomp.Connection([(activemq_hostname,activemq_port)]) + self.client = Connection(username=activemq_username, password=activemq_password, host=activemq_hostname,port=activemq_port, debug=True) + #self.client = Connection(host=activemq_hostname,port=activemq_port, debug=True) + self.client.connect() + #self.client.connect(username=activemq_username,password=activemq_password) + print("connection established") + return True + except Exception as e: + print(e) + print("Could not connect the publisher") + time.sleep(2) + + def send(self): + if self.message == None or self.queue == None: + print("Message or queue is None") + return False + try: + #self.client.send(body=json.dumps(self.message), destination=self.queue, persistent='false', auto_content_length=False, content_type="application/json") + self.client.send_to_topic(self.queue, self.message, self.content_type) + return True + except Exception as e: + print(e) + self.client.disconnect() + print("Reconnection in 10s ...") + time.sleep(10) + self.connect() + self.send() + +class MessagingManager(Thread): + def __init__(self): + self.consumer_train = Consumer(self.trainHandler, performance_module_train_topic) + self.consumer_predict = Consumer(self.predictHandler, performance_module_predict_topic) + self.publisher = Publisher() + super(MessagingManager, self).__init__() + + def trainHandler(self, data): + print("Data received=",data) + """ + data: [ + {'application': 'genome','target':'EstimatedRemainingTimeContext','features': features_4,'variant':'vm', 'sender_id':'solver_id'}, + {'application': 'genome','target':'SimulationLeftNumber','features': features_4,'variant':'vm'}, + ] + """ + _json = None + try: + _json = json.loads(data) + except Exception as e: + print("Could not parse json format") + print(data) + if not _json: + return None + for train in _json: + mandatory_fields = ["application","target","features","variant","sender_id"] + error_message = None + for field in mandatory_fields: + if not field in train: + error_message = {"status": False, "message": "Field <<{0}>> is missing".format(field)} + if error_message: + self.publisher.setParameters(error_message,performance_module_train_feedback_topic,content_type="application/json") + self.publisher.send() + continue + application_name = train['application'] + target = train['target'] + features = train['features'] + variant = train['variant'] + sender_id = train['sender_id'] + hw = None + if "hw" in train: + hw = train["hw"] + else: + hw = "cpu" + self.trainModel(application_name,target,features,variant, sender_id, hw) + + def trainModel(self, application, target, features, variant,sender_id,hw): + #data = {'application': application, 'target': target, 'variant': variant, 'sender_id':sender_id} + configs = {'hostname': influxdb_hostname, + 'port': influxdb_port, + 'username': influxdb_username, + 'password': influxdb_password, + 'dbname': influxdb_dbname, + 'path_dataset': dataset_local_repository + } + datasetmaker = DatasetMaker(application,None,configs) + response = datasetmaker.make() + #response = requests.post(url=url_output_api,data='{"application":"'+application+'", "start":"10m"}', headers={'Content-type':'application/json'}).json() + + url_file = response['url'] + #url_file = "./datasets/dataset.csv" + trainer = Trainer(url_file,target,application,features,variant,sender_id,hw) + out = trainer.train() + print(out) + self.publisher.setParameters(out, performance_module_train_feedback_topic,content_type="application/json") + self.publisher.send() + + def predictHandler(self, data): + print("Prediction = ", data) + #data : {'application': 'fcr','target':'response_time','features':features, 'variant': 'vm', 'sender_id':'solver_id'} + _json = None + try: + _json = json.loads(data) + except Exception as e: + print(e) + error_message = None + if not "sender_id" in _json: + error_message = {"status": False, "message": "field <> is missing", "results":{},"ml":{}} + missing_field = None + mandatory_fields = ["application","target","variant","hw","sender_id"] + + for field in mandatory_fields: + if not field in _json: + missing_field = field + + if missing_field: + _json = None + error_message = {"status": False, "message": "field <<{0}>> is missing".format(missing_field), "results":{},"ml":{},"sender_id": _json['sender_id']} + + if error_message: + self.publisher.setParameters(error_message, performance_module_predict_feedback_topic,content_type="application/json") + self.publisher.send() + + if _json: + features = {} + for key in _json: + if key not in mandatory_fields: + features[key] = _json[key] + predictor = Predictor(_json['application'],features,_json['target'],_json['variant'],_json['sender_id']) + data = predictor.predict() + print(data) + self.publisher.setParameters(data, performance_module_predict_feedback_topic) + self.publisher.send() + + def run(self): + print("Messaging manager started ...") + self.consumer_train.start() + self.consumer_predict.start() + self.publisher.start() + while True: + time.sleep(10) + +if __name__ == "__main__": + messaging = MessagingManager() + messaging.start() + + diff --git a/morphemic-performance-model/ml_code/src/app_model/.DS_Store b/morphemic-performance-model/ml_code/src/app_model/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..56a111e7e1f5a466f0b81966070cba252d52ec6e Binary files /dev/null and b/morphemic-performance-model/ml_code/src/app_model/.DS_Store differ diff --git a/morphemic-performance-model/ml_code/src/app_model/genome-vm-cpu-ETPercentile/model.pickle b/morphemic-performance-model/ml_code/src/app_model/genome-vm-cpu-ETPercentile/model.pickle new file mode 100644 index 0000000000000000000000000000000000000000..59c824fbed718f835101a2856ab100cc6c8c47e2 Binary files /dev/null and b/morphemic-performance-model/ml_code/src/app_model/genome-vm-cpu-ETPercentile/model.pickle differ diff --git a/morphemic-performance-model/ml_code/src/datasets/.DS_Store b/morphemic-performance-model/ml_code/src/datasets/.DS_Store new file mode 100644 index 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os.environ.get("ACTIVEMQ_HOST","localhost") -activemq_port = int(os.environ.get("ACTIVEMQ_PORT","61613")) +activemq_port = int(os.environ.get("ACTIVEMQ_PORT","61610")) + persistence_storage_queue = "/queue/persistent_storage" subscription_topic = 'performance_model_evaluator' ps_management_queue = os.environ.get("PS_MANAGEMENT_QUEUE","persistent_storage") @@ -75,10 +77,15 @@ class Evaluation(Thread): super(Evaluation,self).__init__() def createSubscription(self, application): - conn = stomp.Connection(host_and_ports = [(activemq_hostname, activemq_port)]) - conn.connect(login=activemq_username,passcode=activemq_password) + #conn = stomp.Connection(host_and_ports = [(activemq_hostname, activemq_port)]) + #conn.connect(login=activemq_username,passcode=activemq_password) + conn = Connection(username=activemq_username, password=activemq_password, host=activemq_hostname,port=activemq_port, debug=True) + conn.connect() + #conn.set_listener('', Listener(conn, self.handler)) + #conn.subscribe(destination=self.queue, id=1, ack='auto') data = {'request':'subscribe','application':application,'metrics':[],'queue': subscription_topic,'name': 'performance_model'} - conn.send(body=json.dumps(data), destination=persistence_storage_queue, persistent='false') + #conn.send(body=json.dumps(data), destination=persistence_storage_queue, persistent='false') + conn.send_to_topic(persistence_storage_queue, body=json.dumps(data)) print("Subscription request sent for application {0}".format(application)) return True diff --git a/morphemic-performance-model/ml_code/src/initial_ML_module.py b/morphemic-performance-model/ml_code/src/initial_ML_module.py index 9e3898895df8b1257f7649fef4ded2855a7bbeb7..dddefb18076a0e7d8e90607524022f24c3c35551 100644 --- a/morphemic-performance-model/ml_code/src/initial_ML_module.py +++ b/morphemic-performance-model/ml_code/src/initial_ML_module.py @@ -83,13 +83,14 @@ class Pipeline(): return data class Trainer(): - def __init__(self, url_file, target, application, features_list, variant): + def __init__(self, url_file, target, application, features_list, variant, sender_id): self.url_file = url_file self.target = target self.features_list = features_list self.features_list.sort() self.application = application self.ml_file_link = None + self.sender_id = sender_id self.variant = variant self.model_manager = MLModelManager() @@ -418,12 +419,13 @@ class Trainer(): return {'file': self.ml_file_link, 'application': self.application, 'target': self.target} class Predictor(): - def __init__(self, application, features_dict, target, variant): + def __init__(self, application, features_dict, target, variant, sender_id): self.application = application self.features_dict = features_dict self.features_dict = dict(sorted(self.features_dict.items())) self.target = target self.prediction = None + self.sender_id = sender_id self.variant = variant self.model_manager = MLModelManager() @@ -584,20 +586,20 @@ class Predictor(): # το prediction #Jean-didier's code if type(self.application) != type(""): - response = {"status": False, "message": "Bad request application field must python string compatible", "results":{},"ml":{}} + response = {"status": False, "message": "Bad request application field must python string compatible", "results":{},"ml":{},"sender_id": self.sender_id} return json.dumps(response, default=self.converter) if type(self.target) != type(""): - response = {"status": False, "message": "Bad request target field must python string compatible", "results":{},"ml":{}} + response = {"status": False, "message": "Bad request target field must python string compatible", "results":{},"ml":{},"sender_id": self.sender_id} return json.dumps(response, default=self.converter) if type(self.features_dict) != type({}): - response = {"status": False, "message": "Bad request feature field must python dict compatible", "results":{},"ml":{}} + response = {"status": False, "message": "Bad request feature field must python dict compatible", "results":{},"ml":{},"sender_id": self.sender_id} return json.dumps(response, default=self.converter) training_data = self.model_manager.getModelTrainingData(self.application,self.target) if training_data == None: - response = {"status": False, "message": "No ML model found for the application {0} with target {1}".format(self.application, self.target), "results":{},"ml":{}} + response = {"status": False, "message": "No ML model found for the application {0} with target {1}".format(self.application, self.target), "results":{},"ml":{},"sender_id": self.sender_id} return json.dumps(response, default=self.converter) """ completed_feature_list = self.completeFeaturesList(training_data) @@ -639,7 +641,7 @@ class Predictor(): else: prediction = self.trained_algorithm.predict(new_sample) - response = {'status': True,'results':{"prediction": prediction,"target": self.target,"application": self.application},'ml': training_data,'message':""} + response = {'status': True,'results':{"prediction": prediction,"target": self.target,"application": self.application},'ml': training_data,'message':"","sender_id": self.sender_id} self.addCandidate(self.application,self.target,self.features_dict,float(prediction), self.variant) return json.dumps(response, default=self.converter) diff --git a/morphemic-performance-model/ml_code/src/ml_model/.DS_Store b/morphemic-performance-model/ml_code/src/ml_model/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..7f1927d001ba7bd7b818da5fea04dd2d36bf1c90 Binary files /dev/null and b/morphemic-performance-model/ml_code/src/ml_model/.DS_Store differ diff --git a/morphemic-performance-model/ml_code/src/ml_model/genome-vm-cpu-ETPercentile/keras_metadata.pb b/morphemic-performance-model/ml_code/src/ml_model/genome-vm-cpu-ETPercentile/keras_metadata.pb new file mode 100644 index 0000000000000000000000000000000000000000..2a8f5824a7389ec21c173093e216b7f373f4f4b1 --- /dev/null +++ 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0000000000000000000000000000000000000000..01de0e3a9e5f0c090ce16ecfbc1f43096ac9d560 Binary files /dev/null and b/morphemic-performance-model/ml_code/src/ml_model/genome-vm-cpu-ETPercentile/variables/variables.index differ diff --git a/morphemic-performance-model/ml_code/src/ml_module.py b/morphemic-performance-model/ml_code/src/ml_module.py new file mode 100644 index 0000000000000000000000000000000000000000..ef2b569b032c5903493e8ea0b6825816fa83da85 --- /dev/null +++ b/morphemic-performance-model/ml_code/src/ml_module.py @@ -0,0 +1,362 @@ +import os, time, json, pickle +from threading import Thread +import pandas as pd +import numpy as np +from sklearn.impute import SimpleImputer +from sklearn.preprocessing import StandardScaler, OrdinalEncoder +from tensorflow.keras import Sequential +from tensorflow import keras +from tensorflow.keras.layers import LSTM, Dropout, Flatten, Dense, Conv1D +from tensorflow.python.keras.layers import MaxPooling1D +from sklearn.model_selection import train_test_split +from math import sqrt +from sklearn.metrics import mean_squared_error, r2_score + +ml_model_folder = os.environ.get("ML_MODEL_FOLDER","./ml_model") +app_model_folder = os.environ.get("APP_MODEL_FOLDER","./app_model") +retrain_interval = int(os.environ.get("RETRAIN_INTERVAL","600")) #10 minutes + +def CNN_model(n_features, X, y): + # define model + model = Sequential() + model.add(Conv1D(filters=64, kernel_size=2, activation='relu', input_shape=(n_features, 1))) + model.add(MaxPooling1D(pool_size=2)) + model.add(Flatten()) + model.add(Dense(50, activation='relu')) + model.add(Dense(1)) + model.compile(optimizer='adam', loss='mse') + # fit model + model.fit(X, y, epochs=30, verbose=2) + return model + +def LSTM_model(n_features, train_X, train_y, test_X, test_y): + # design network + model = Sequential() + model.add(LSTM(90, return_sequences = True, input_shape=(n_features,1))) # 1 , 2 + model.add(Dropout(0.2)) + model.add(LSTM(60, return_sequences = False )) + model.add(Dropout(0.2)) + #model.add(LSTM(30, return_sequences = False )) + #model.add(Dropout(0.2)) + #model.add(LSTM(15, return_sequences = False )) + #model.add(Dense(50)) + #model.add(Dropout(0.2)) + model.add(Dense(1)) + model.compile(loss='mse', optimizer='adam') + # fit network + model.fit(train_X, train_y, epochs=150, batch_size=128, validation_data=(test_X, test_y),verbose=2, shuffle=False) + return model + +def prediction_and_score_for_CNN(model, x_input,test_y): + #x_input = x_input.reshape((1, n_steps, n_features)) + yhat = model.predict(x_input, verbose=2) + rmse = sqrt(mean_squared_error(test_y, yhat)) + r2score = r2_score(test_y, yhat) + return rmse, r2score + +def load_data(url_file, features): + # Check if we received a file that does not exist + if os.path.isfile(url_file): + pass + else: + print("Sorry, file does not exists.") + raise ValueError("Sorry, file does not exists.") + name, extension = os.path.splitext(url_file) + # check if it is not a csv file + if extension != '.csv': + print("Oops! That was no a csv file. Try again...") + raise ValueError("Oops! That was no a csv file. Try again...") + else: + pass + # check if it is an empty file + if os.stat(url_file).st_size == 0: + print("Sorry! That file is empty. Try again...") + raise ValueError("Sorry! That file is empty. Try again...") + else: + pass + # Load the file + data = pd.read_csv(url_file) + data = data[features] + if data.iloc[0].values[1] == 'Unnamed 0': + data = data.drop(columns=['Unnamed 0']) + if type(data.iloc[0].values[1]) == str: + comparison = data.iloc[0].values == data.columns.values + if comparison.all() == True: + return data.iloc[1:] + else: + return data + else: + return data + +def missing_values_imputer(data,target): + if data.isna().any().any() == False: + pass + elif data.isna().any().any() == True: + names = data.columns + # example + imp = SimpleImputer(missing_values=np.nan, strategy='mean') + imp.fit(data.values) + data = imp.transform(data.values) + data = pd.DataFrame(data, columns=names) + return data + +def normalization(data, target): + # data = self.load_data() + scaler = StandardScaler() + data_target = data[target] + data = data.drop(columns=[target]) + names = data.columns + scaler.fit(data) + data = scaler.transform(data) + data = pd.DataFrame(data, columns=names) + data[target] = data_target + return data + +def encode_categorical_values(data,target): + # data = self.load_data() + a = [] + j = 0 + for i in data.dtypes: + if i == np.dtype('str') or i == np.dtype('object'): + a.append(data.dtypes.index[j]) + j += 1 + elif i != np.dtype('str') or i != np.dtype('object'): + j += 1 + + # TODO ----> I shall add more encoder functions, not only Ordinal Encoder + + oe = OrdinalEncoder() + categorical_data = data[a] + names = categorical_data.columns + oe.fit(categorical_data) + categorical_data = oe.transform(categorical_data) + categorical_data = pd.DataFrame(categorical_data, columns=names) + # replace categorical with encoded data + for i in a: + data[i] = categorical_data[i].replace(',', '-') + return data + +class ApplicationModel(): + def __init__(self): + self.application = None + self.variant = None + self.hw = None + self.target = None + self.features = None + self.train_time = None + self.status = "NotStated" + self.ml_model_url = None + self.dataset_url = None + + def setParameters(self, application, variant, hw, target): + self.application = application + self.variant = variant + self.hw = hw + self.target = target + + def setFeatures(self, features): + self.features = features + def getFeatures(self): + return self.features + def setTrainTime(self, _time): + self.train_time = _time + def getTrainTime(self): + return self.train_time + def getStatus(self): + return self.status + def getTarget(self): + return self.target + def setStatus(self, status): + self.status = status + def setModelUrl(self, url): + self.ml_model_url = url + def getModelUrl(self): + return self.ml_model_url + def setDatasetUrl(self, url): + self.dataset_url = url + def getDatasetUrl(self): + return self.dataset_url + def getKey(self): + return "{0}-{1}-{2}-{3}".format(self.application, self.variant, self.hw, self.target) + + +class ApplicationModelsManager(): + def __init__(self): + self.models = {} + + def loadModel(self, application, variant, hw, target): + key = "{0}-{1}-{2}-{3}".format(application,variant,hw,target) + _path = app_model_folder+"/"+key+"/model.pickle" + if os.path.exists(_path): + _file = open(_path,"rb") + model = pickle.load(_file) + _file.close() + return model + return False + + def createModel(self, application, variant, hw, target): + model = ApplicationModel() + model.setParameters(application,variant,hw, target) + key = model.getKey() + self.models[key] = model + print('Model application = {0}, variant = {1}, HW = {2}, target = {3} created'.format(application,variant,hw,target)) + return model + + def saveModel(self, application, variant, hw, target): + key = "{0}-{1}-{2}-{3}".format(application,variant,hw,target) + if key in self.models: + model = self.models[key] + filename = app_model_folder+"/"+key+"/model.pickle" + os.makedirs(os.path.dirname(filename), exist_ok=True) + _file = open(filename,"wb") + pickle.dump(model, _file) + _file.close() + return True + return False + + def getModel(self, application, variant, hw, target): + key = "{0}-{1}-{2}-{3}".format(application,variant,hw,target) + if key in self.models: + return self.models[key] + model = self.loadModel(application,variant,hw,target) + if model: + self.models[key] = model + return model + return None + +class Pipeline(): + def __init__(self, functions): + self.functions = functions + def execute(self, data, target): + for f in self.functions: + data = f(data,target) + return data + +class Predictor(): + def __init__(self, application, features_dict, target, variant, sender_id, hw="CPU"): + self.application = application + self.features_dict = dict(sorted(features_dict.items())) + self.target = target + self.hw = hw + self.sender_id = sender_id + self.variant = variant + self.model_manager = ApplicationModelsManager() + + def predict(self): + if type(self.application) != type(""): + return {"status": False, "message": "Bad request application field must python string compatible", "results":{},"ml":{},"sender_id": self.sender_id} + + if type(self.target) != type(""): + return {"status": False, "message": "Bad request target field must python string compatible", "results":{},"ml":{},"sender_id": self.sender_id} + + if type(self.features_dict) != type({}): + return {"status": False, "message": "Bad request feature field must python dict compatible", "results":{},"ml":{},"sender_id": self.sender_id} + + app_model = self.model_manager.getModel(self.application, self.variant,self.hw,self.target) + if app_model == None: + return {"status": False, "message": "No ML model found for the application {0} with target {1}, variant={2}, HW={3}".format(self.application, self.target, self.variant,self.hw), "results":{},"ml":{},"sender_id": self.sender_id} + feature_list = list(self.features_dict.keys()) + model_feature_list = app_model.getFeatures() + missing = [] + for feature in model_feature_list: + if not feature in feature_list: + missing.append(feature) + + if len(missing) > 0: + return {"status": False, "message": "The following feature fields are missing = {0}".format(missing), "results":{},"ml":{},"sender_id": self.sender_id} + feature_list.append(self.target) + df = load_data(app_model.getDatasetUrl(),feature_list) + #concatenate feature_dict to the data + self.features_dict[self.target] = 1 + #df2 = pd.DataFrame(feature_dict,index=0) + #df = pd.concat([df2,df1]) + df.loc[0] = list(self.features_dict.values()) + df.replace(r'[a-zA-Z%]', np.nan, regex=True, inplace=True) + df = missing_values_imputer(df,self.target) + data = normalization(df,self.target) + data = data.drop(columns=[self.target]) + new_sample = data.iloc[[0]] + model = keras.models.load_model(app_model.getModelUrl()) + prediction = float(model.predict(new_sample.values)[0][0]) + return {"status": True, "message": "", "results":{"prediction": prediction},"ml":{"algorithm":"CNN"},"sender_id": self.sender_id} + + +class Trainer(): + def __init__(self, url_file, target, application, features_list, variant, sender_id, hw="cpu"): + self.url_file = url_file + self.target = target + self.features_list = features_list + self.features_list.sort() + self.hw = hw + self.application = application + self.ml_file_link = None + self.sender_id = sender_id + self.variant = variant + self.model_manager = ApplicationModelsManager() + + def train(self): + #creation of dataset + app_model = self.model_manager.getModel(self.application, self.variant, self.hw, self.target) + if not app_model: + app_model = self.model_manager.createModel(self.application, self.variant, self.hw, self.target) + if app_model.getStatus() == "Trained" and time.time() - app_model.getTrainTime() < retrain_interval: + return {"status": False, "message": "Model exists and trained", "application": self.application,"sender_id": self.sender_id } + data = None + error = False + try: + data = load_data(self.url_file, self.features_list) + except: + error = True + if error: + return {"status": False, "message": "could not load the dataset", "application": self.application,"sender_id": self.sender_id } + app_model.setDatasetUrl(self.url_file) + pipeline = Pipeline([missing_values_imputer,encode_categorical_values,normalization]) + data = pipeline.execute(data, self.target) + data = data.dropna() + Y = data[self.target].values + self.features_list.remove(self.target) + app_model.setFeatures(self.features_list) + X = data[self.features_list].values + X = X.reshape((X.shape[0], X.shape[1], 1)) + x_train, x_test, y_train, y_test = train_test_split(X, Y) + ml_model = CNN_model(len(self.features_list),x_train,y_train) + rmse, r2score = prediction_and_score_for_CNN(ml_model, x_test, y_test) + app_model.setStatus("Trained") + app_model.setTrainTime(time.time()) + print("RMSE = ", rmse) + print("R2 SCORE = ", r2score) + key = key = "{0}-{1}-{2}-{3}".format(self.application,self.variant,self.hw,self.target) + _path = ml_model_folder +"/"+ key + ml_model.save(_path) + app_model.setModelUrl(_path) + self.model_manager.saveModel(self.application,self.variant,self.hw,self.target) + return {"status": True, "message": "Train finished", "application": self.application,"sender_id": self.sender_id } + +"""" +if __name__ == "__main__": + application, variant, hw, sender_id = "genome", "vm", "cpu", 123 + url_file = "./datasets/dataset.csv" + target = 'ETPercentile' + feature_list = ['ETPercentile','EstimatedRemainingTimeContext','SimulationLeftNumber','SimulationElapsedTime','NotFinishedOnTime','MinimumCoresContext','NotFinished','WillFinishTooSoonContext','NotFinishedOnTimeContext','MinimumCores','RemainingSimulationTimeMetric','TotalCores'] + trainer = Trainer(url_file, target, application,feature_list,variant,sender_id) + trainer.train() + feature_dict = {} + feature_dict['EstimatedRemainingTimeContext']= float(6.316824e+03) + feature_dict['SimulationLeftNumber'] = float(5.566486e+02) + feature_dict['SimulationElapsedTime'] = float(3.273800e+04) + feature_dict['NotFinishedOnTime'] = float(3.550871e+04) + feature_dict['MinimumCoresContext'] = float(-1.983463e+00) + feature_dict['NotFinished'] = float(3.550871e+04) + feature_dict['WillFinishTooSoonContext'] = float(2.967053e+04) + feature_dict['NotFinishedOnTimeContext'] = float(3.550871e+04) + feature_dict['MinimumCores'] = float(-1.983463e+00) + ETPercentile = float(9.205543e+01) + feature_dict['RemainingSimulationTimeMetric'] = float(-2.553800e+04) + feature_dict['TotalCores'] = float(1.000000e+00) + predictor = Predictor("genome",feature_dict,"ETPercentile","vm","123","cpu") + v = predictor.predict() + prediction = v['results']['prediction'] + error = (prediction - ETPercentile)**2 + print("Prediction = {0}, precision = {1}".format(prediction,error)) """ + + \ No newline at end of file diff --git a/morphemic-performance-model/ml_code/src/predictgrpc.py b/morphemic-performance-model/ml_code/src/predictgrpc.py index 2ba2439effa33864823298be981f76b4b8555446..913026c84c50e81bf73f5a12478d4f2cfbdf7e3f 100644 --- a/morphemic-performance-model/ml_code/src/predictgrpc.py +++ b/morphemic-performance-model/ml_code/src/predictgrpc.py @@ -1,5 +1,5 @@ import service_pb2, service_pb2_grpc, os, requests, json -from initial_ML_module import Predictor, MLModelManager, Trainer +from ml_module import Trainer, Predictor, ApplicationModelsManager from morphemic.dataset import DatasetMaker import subprocess from google.protobuf.json_format import MessageToJson @@ -23,7 +23,7 @@ class PredictRPC(service_pb2_grpc.PredictServicer): final_features = {} for k, _value in features.items(): final_features[k] = _value['floatValue'] - predictor = Predictor(request.application, final_features, request.target, request.variant) + predictor = Predictor(request.application,features,request.target,request.variant,request.sender_id,request.hardware) response = json.loads(predictor.predict()) #dict_result = service_pb2.Dictionary() @@ -37,11 +37,12 @@ class PredictRPC(service_pb2_grpc.PredictServicer): reply.results.fields['results'].string_value = json.dumps(response['results']) reply.ml.fields['ml'].string_value = json.dumps(response['ml']) reply.message = response['message'] + reply.sender_id = response['sender_id'] return reply def getModel(self, request, context): - model_manager = MLModelManager() - response = model_manager.getModelTrainingData(request.application, request.target) + model_manager = ApplicationModelsManager() + response = model_manager.getModel(request.application,request.variant,request.hardware,request.target) reply = service_pb2.ModelReply() if response != None: reply.results.fields['results'].string_value = json.dumps(response) @@ -70,27 +71,26 @@ class PredictRPC(service_pb2_grpc.PredictServicer): reply.status = False reply.message = str(e) reply.application = request.application + reply.sender_id = request.sender_id return reply - try: - _json = json.loads(MessageToJson(request.features)) - features = list(_json["strings"]) - data = {"url_file": request.url_file, "target": request.target, "application": request.application, "features": features, "variant": request.variant} - _file = open(train_folder + '/train.data', 'w') - _file.write(json.dumps(data)) - _file.close() - command = ['python','-u','train.py'] - p = subprocess.Popen(command, start_new_session=True) + _json = json.loads(MessageToJson(request.features)) + features = list(_json["strings"]) + #data = {"url_file": request.url_file, "target": request.target, "application": request.application, "features": features, "variant": request.variant,"sender_id": request.sender_id} + + trainer = Trainer(request.url_file,request.target,request.application,features,request.variant,request.sender_id,request.hardware) + train_data = trainer.train() + #_file = open(train_folder + '/train.data', 'w') + #_file.write(json.dumps(data)) + #_file.close() + + #command = ['python','-u','train.py'] + #p = subprocess.Popen(command, start_new_session=True) - reply = service_pb2.TrainReply() - reply.status = True - reply.message = "Training started" - reply.application = request.application - return reply - except Exception as e: - reply = service_pb2.TrainReply() - reply.status = False - reply.message = str(e) - reply.application = request.application - return reply + reply = service_pb2.TrainReply() + reply.status = train_data["status"] + reply.message = train_data["message"] + reply.application = request.application + reply.sender_id = request.sender_id + return reply \ No newline at end of file diff --git a/morphemic-performance-model/ml_code/src/protos/facade.proto b/morphemic-performance-model/ml_code/src/protos/facade.proto new file mode 100644 index 0000000000000000000000000000000000000000..1b76cbd1fda10a2abbcd015d2e9bebb4b8060a72 --- /dev/null +++ b/morphemic-performance-model/ml_code/src/protos/facade.proto @@ -0,0 +1,36 @@ +syntax = "proto3"; +package eu.melodic.upperware.utilitygenerator.facade.test; + +option java_package = "eu.melodic.upperware.utilitygenerator.facade.test"; +option optimize_for = SPEED; + +option java_multiple_files = true; + +message CallPmPrediction { + int32 dispatcherCores = 1; + int32 dispatcherRam = 2; + string dispatcherLocation = 3; + int32 workerCores = 4; + int32 workerRam = 5; + string workerLocation = 6; + int32 workerMulti = 7; + string senderID = 8; // facade sender ID +}; + +message Config { + int32 cores = 1; + int32 ram = 2; + string location = 3; +} + +message CallPmPrediction2 { + Config dispatcher = 1; + Config worker = 2; + int32 workerMulti = 3; + string senderID = 4; // facade sender ID +} + +message CallPmPredictionResponse { + double utilityValue = 1; + string senderID = 2; // facade sender ID +}; \ No newline at end of file diff --git a/morphemic-performance-model/ml_code/src/protos/service.proto b/morphemic-performance-model/ml_code/src/protos/service.proto index bcbf72daf8366eaedf622f903e47ccebce55ecd9..0e48f927d73ae2c04bdcb9f06f2dfe644542683d 100644 --- a/morphemic-performance-model/ml_code/src/protos/service.proto +++ b/morphemic-performance-model/ml_code/src/protos/service.proto @@ -33,6 +33,8 @@ message PredictRequest { required Dictionary features = 2; required string target = 3; required string variant = 4; + required int32 sender_id = 5; + required string hardware = 6; } @@ -41,11 +43,14 @@ message PredictReply { required Dictionary results = 2; required Dictionary ml = 3; required string message = 4; + required int32 sender_id = 5; } message ModelRequest { required string application = 1; required string target = 2; + required string variant = 3; + required string hardware = 4; } message ModelReply { @@ -66,10 +71,13 @@ message TrainRequest { required ListOfStrings features = 3; required string target = 4; required string variant = 5; + required int32 sender_id = 6; + required string hardware = 7; } message TrainReply { required bool status = 1; required string message = 2; required string application = 3; + required int32 sender_id = 4; } \ No newline at end of file diff --git a/morphemic-performance-model/ml_code/src/requirements.txt b/morphemic-performance-model/ml_code/src/requirements.txt index a6e1aba16e43dbde5329a64e65bfe4ed05e2a540..cdc8caba17a9c94fe48c12d1ea255b43ab245a56 100644 --- a/morphemic-performance-model/ml_code/src/requirements.txt +++ b/morphemic-performance-model/ml_code/src/requirements.txt @@ -26,5 +26,6 @@ requests grpcio protobuf stomp.py +python-slugify fastapi uvicorn \ No newline at end of file diff --git a/morphemic-performance-model/ml_code/src/service.py b/morphemic-performance-model/ml_code/src/service.py index e3bd6f0251d115059467e42b7eebc4468df3d0b8..4297e42debc84903780f54a0ed07e6748f96b971 100644 --- a/morphemic-performance-model/ml_code/src/service.py +++ b/morphemic-performance-model/ml_code/src/service.py @@ -1,7 +1,8 @@ import os, time, json, requests, stomp, train from threading import Thread #from flask import Flask, Response, request -from initial_ML_module import Trainer, Predictor, MLModelManager +#from initial_ML_module import Predictor, MLModelManager +from ml_module import Trainer, Predictor, ApplicationModelsManager from concurrent import futures import grpc, service_pb2_grpc, sqlite3 from predictgrpc import PredictRPC @@ -41,16 +42,22 @@ class TrainRequest(BaseModel): features: list target: str variant: str + sender_id: int + hw: str class Model(BaseModel): application: str target: str + variant: str + hw: str class PredictRequest(BaseModel): application: str features: dict target: str variant: str + sender_id: int + hw: str url_output_api = os.environ.get("URL_OUTPUT_API","http://localhost:8767/api/v1/make") #app = Flask(__name__) @@ -67,12 +74,14 @@ def home(): @app.post('/api/v1/model/train') async def train_model(req: TrainRequest): - url_file, application, features, target, variant = None, None, None, None , None + url_file, application, features, target, variant, sender_id, hw = None, None, None, None , None, None url_file = req.url_file application = req.application features = req.features target = req.target variant = req.variant + sender_id = req.sender_id + hw = req.sender_id if url_file == None or url_file == "": try: @@ -90,45 +99,45 @@ async def train_model(req: TrainRequest): #features = response['features'] except Exception as e: print(e) - return {'status': False, 'message':'Could not create dataset','application': application} + return {'status': False, 'message':'Could not create dataset','application': application,"sender_id": sender_id} try: - data = {"url_file": url_file, "target": target, "application": application, "features": features, "variant": variant} - _file = open(train_folder + '/train.data', 'w') - _file.write(json.dumps(data)) - _file.close() + #data = {"url_file": url_file, "target": target, "application": application, "features": features, "variant": variant, "sender_id": sender_id} + trainer = Trainer(url_file,target,application,features,variant,sender_id,hw) + train_result = trainer.train() + #_file = open(train_folder + '/train.data', 'w') + #_file.write(json.dumps(data)) + #_file.close() #trainer = Trainer(url_file,target,application,features) #response = {"data": trainer.train_separated_thread()} - command = ['python','-u','train.py'] + #command = ['python','-u','train.py'] #os.spawnlp(os.P_DETACH,*command) - p = subprocess.Popen(command, start_new_session=True) - return {"status": True, "message": "Training started", "application": application } + #p = subprocess.Popen(command, start_new_session=True) + return train_result except Exception as e: print(e) - return {'status': False, 'message':'An error occured while training the model','application': application} + return {'status': False, 'message':'An error occured while training the model','application': application,"sender_id": sender_id} @app.post('/api/v1/model') async def get_model(req: Model): application = req.application target = req.target + variant = req.variant + hw = req.hw - model_manager = MLModelManager() - return {"data": model_manager.getModelTrainingData(application, target)} + model_manager = ApplicationModelsManager() + return {"data": model_manager.getModel(application,variant,hw,target) } @app.post('/api/v1/model/predict') def predict_model(req: PredictRequest): - application = req.application features = req.features target = req.target variant = req.variant - - try: - predictor = Predictor(application, features, target, variant) - return json.loads(predictor.predict()) - except Exception as e: - print(e) - return {'status': False, 'message':'An error occured while training the model','application': application} + sender_id = req.sender_id + hw = req.hw + predictor = Predictor(application,features,target,variant,sender_id,hw) + return json.loads(predictor.predict()) if __name__ == "__main__": uvicorn.run(app, host="0.0.0.0", port=8766) diff --git a/morphemic-performance-model/ml_code/src/start_activemq_listener.sh b/morphemic-performance-model/ml_code/src/start_activemq_listener.sh new file mode 100644 index 0000000000000000000000000000000000000000..9f0349e840b4617c2ed42e4401ef8e23fe7e839d --- /dev/null +++ b/morphemic-performance-model/ml_code/src/start_activemq_listener.sh @@ -0,0 +1,4 @@ +#!/bin/bash + +#Performance model service (Manager) +python -u activemq_logic.py \ No newline at end of file diff --git a/morphemic-performance-model/ml_code/src/train.py b/morphemic-performance-model/ml_code/src/train.py index 6ac633df14a12c95852c42dbc1ee9601c855b456..22614d25920dc3b4a61c2b2d794f386636206edd 100644 --- a/morphemic-performance-model/ml_code/src/train.py +++ b/morphemic-performance-model/ml_code/src/train.py @@ -30,7 +30,7 @@ def start(): logger.info(e) return False try: - trainer = Trainer(_json["url_file"],_json["target"],_json["application"],_json["features"], _json['variant']) + trainer = Trainer(_json["url_file"],_json["target"],_json["application"],_json["features"], _json['variant'], _json['sender_id']) trainer.train() except Exception as e: print(e)