diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..35bcc5bd3cc3c8c5c918afff12b428a1293e5b42 Binary files /dev/null and b/.DS_Store differ diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml index b53a906a569c1e5f4d3decfdabe235d8cebd263d..15bbeb8f9094dfd5685bfea5e7ac4f29f2d40936 100644 --- a/.gitlab-ci.yml +++ b/.gitlab-ci.yml @@ -135,6 +135,36 @@ deploy:camel_converter: - docker tag camel_converter:latest $CI_REGISTRY_IMAGE/camel_converter:$CI_COMMIT_BRANCH - docker push $CI_REGISTRY_IMAGE/camel_converter:$CI_COMMIT_BRANCH +deploy:workflow_analyzer: + stage: deploy + image: $DOCKER_DIND_IMAGE + only: + - morphemic-rc3.0 + services: + - $DOCKER_DIND_SERVICE + script: + - cd workflow_analyzer + - docker build -t workflow_analyzer -f ./Dockerfile . + - docker image ls + - echo "$K8S_SECRET_DOCKER_PASSWORD" | docker login $CI_REGISTRY -u $K8S_SECRET_DOCKER_USER --password-stdin + - docker tag workflow_analyzer:latest $CI_REGISTRY_IMAGE/workflow_analyzer:$CI_COMMIT_BRANCH + - docker push $CI_REGISTRY_IMAGE/workflow_analyzer:$CI_COMMIT_BRANCH + +deploy:component_grouping: + stage: deploy + image: $DOCKER_DIND_IMAGE + only: + - morphemic-rc3.0 + services: + - $DOCKER_DIND_SERVICE + script: + - cd grouping + - docker build -t component_grouping -f ./Dockerfile . + - docker image ls + - echo "$K8S_SECRET_DOCKER_PASSWORD" | docker login $CI_REGISTRY -u $K8S_SECRET_DOCKER_USER --password-stdin + - docker tag component_grouping:latest $CI_REGISTRY_IMAGE/component_grouping:$CI_COMMIT_BRANCH + - docker push $CI_REGISTRY_IMAGE/component_grouping:$CI_COMMIT_BRANCH + deploy:polymorphic_solver: stage: deploy image: $DOCKER_DIND_IMAGE diff --git a/camel_converter/.DS_Store b/camel_converter/.DS_Store index 6cf47ff4a0efcd873bae7ff9d2f7ba8a0e90f202..4ff426009321c3f347f2c1c8cfc82d6bf0ebd6b9 100644 Binary files a/camel_converter/.DS_Store and b/camel_converter/.DS_Store differ diff --git a/camel_converter/readme.md b/camel_converter/readme.md new file mode 100644 index 0000000000000000000000000000000000000000..03848d681be6bec0f06f3a5b93221c1100e70a65 --- /dev/null +++ b/camel_converter/readme.md @@ -0,0 +1,42 @@ +## Overview +The camel converter is service with two functionalities: +- import camel model from xmi to the cdo server +- export camel from the cdo server to a xmi file +This component is based on pyjnius library which allow the usage of +Java class in python program. +The API is based on fastapi and listen to the port 7676 + +## cdo server configuration +the cdo server parameters can be found in src/.paasage/eu.paasage.mddb.cdo.client.properties +the default values are the following: +host=localhost +port=2036 +repository=repo1 +logging=on +userName=morphemic +password=morphemic + +For a custom cdo server please mount a volume containing the file eu.paasage.mddb.cdo.client.properties +and use the environment parameters PAASAGE_CONFIG_DIR to specify the folder to the API. + +## API specification + +- Import camel +This endpoint received a file (xmi) and a resource for storing the file in the cdo server +the file is validated against syntaxe before insertion. +the file should be placed in the folders /src/models. You can use docker volume for customization +of this folder, see the docker-compose.yaml example +endpoint : "/import_camel" +method: "POST" +data: {"resource_name":"camel_resource_name", "filename": "file.xmi"} +response: - {"status": true, "message": "camel imported successfully"} + - {"status": false, "message": "error message"} + +- Export camel +This endpoint is used to export an existing camel model using the resource name to a file +endpoing : "/export_camel" +method: "POST" +data: {"resource_name":"resource_name", "filename": "output.xmi", "output":"xmi"} +the output parameter is optional. The default value is xmi. +The current version supports also "json" as value for the field. +the output file will be placed into src/models diff --git a/camel_converter/requirements.txt b/camel_converter/requirements.txt index 83c6c5048f9c7c9a72e4a17512df9330d8ab89ec..47ef5ce282e8cc658140d84d7a91474aa58ba74b 100644 --- a/camel_converter/requirements.txt +++ b/camel_converter/requirements.txt @@ -1,3 +1,4 @@ pyjnius fastapi uvicorn +xmltodict \ No newline at end of file diff --git a/camel_converter/src/.DS_Store b/camel_converter/src/.DS_Store index 0a5eaa8ef9721cac2c519f44c35e0c848f675699..41a3f3e6c95a8ff34bf264853c48240a106c8e6b 100644 Binary files a/camel_converter/src/.DS_Store and b/camel_converter/src/.DS_Store differ diff --git a/camel_converter/src/app.py b/camel_converter/src/app.py index 6e6b86dcae93d368b636373cc451d833b0c356ee..9be0bcdcfae5501aee547fd87ac66b79bc3d8951 100644 --- a/camel_converter/src/app.py +++ b/camel_converter/src/app.py @@ -15,6 +15,13 @@ class ImportRequest(BaseModel): resource_name: str filename: str +class ExportCDO(BaseModel): + xtext: Optional[bool] = False + +class ImportCDO(BaseModel): + filename: str + xtext: bool + app = FastAPI() @app.post("/import_model") @@ -24,7 +31,11 @@ async def import_model(request: ImportRequest): return {"status": False, "message": "File {0} not found".format(filename)} try: client = CDOClientWapper() - status = client.importModel(filename, request.resource_name) + status = False + if request.filename[request.filename.index(".")+1:] == "camel": + status = client.importTextualModel(filename, request.resource_name) + else: + status = client.importModel(filename, request.resource_name) if status: return {"status": True, "message": "camel imported successfully"} else: @@ -32,6 +43,31 @@ async def import_model(request: ImportRequest): except Exception as e: return {"status": False, "message": e} +@app.post("/export_cdo") +async def export_cdo(request: ExportCDO): + try: + client = CDOClientWapper() + status = client.exportCDO(model_folder, request.xtext) + if status: + return {"status": True} + else: + return {"status": False, "message": "Operation failed"} + except Exception as e: + return {"status": False, "message": e} + +@app.post("/import_cdo") +async def import_cdo(request: ImportCDO): + filename = model_folder + request.filename + try: + client = CDOClientWapper() + status = client.importCDO(filename, request.xtext) + if status: + return {"status": True} + else: + return {"status": False, "message": "operation failed"} + except Exception as e: + return {"status": False, "message": e} + @app.post("/export_model") async def export_model(request: ExportRequest): filename = model_folder + request.filename diff --git a/camel_converter/src/loader.py b/camel_converter/src/loader.py index fd0f0f46858e958c19784ba4a0a47536f4739564..e944ea45242129d2e375dbd9c609f896ac509e7e 100644 --- a/camel_converter/src/loader.py +++ b/camel_converter/src/loader.py @@ -22,10 +22,21 @@ class CDOClientWapper(): print("Exporting camel from file: {0}, to the resource name {1}".format(filename, resource_name)) return self.cdo_client.importModel(filename, resource_name, True) #True for validating model before storing + def importTextualModel(self, filename, resource_name): + print("Exporting camel from file: {0}, to the resource name {1}".format(filename, resource_name)) + return self.cdo_client.importTextualModel(filename, resource_name, True) #True for validating model before storing + def exportModel(self, filename, resource_name): print('Exporting camel model from resource name {0} to the file path {1}'.format(resource_name, filename)) return self.cdo_client.exportModel(resource_name, filename) + def exportCDO(self, dir_path, xtext): + print('Exporting the cdo reposition into the folder = {0}'.format(dir_path)) + return self.cdo_client.exportCDOContent(dir_path, xtext) + + def importCDO(self, filepath, xtext): + print("Importing the cdo repository from the file = {0}".format(filepath)) + return self.cdo_client.loadCDOContent(filepath, xtext) if __name__ == "__main__": client = CDOClientWapper() diff --git a/camel_converter/src/morphemic/.DS_Store b/camel_converter/src/morphemic/.DS_Store index c115bc0c484c463fc0209d6199c243b95ff1816d..a7ef982e23f16ddac81adfca90b7ede3ebc8efb5 100644 Binary files a/camel_converter/src/morphemic/.DS_Store and b/camel_converter/src/morphemic/.DS_Store differ diff --git a/polymorphic_solver/src/tmp/maddpg/simple_adversary/.DS_Store b/camel_converter/tmp/.DS_Store similarity index 100% rename from polymorphic_solver/src/tmp/maddpg/simple_adversary/.DS_Store rename to camel_converter/tmp/.DS_Store diff --git a/grouping/.DS_Store b/grouping/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..a4f37fc5c7d3518db8900d6da82f1c3ff4dda234 Binary files /dev/null and b/grouping/.DS_Store differ diff --git a/grouping/Dockerfile b/grouping/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..37424cbcd396216f2698afa0e6ba36d2ca7b6f1a --- /dev/null +++ b/grouping/Dockerfile @@ -0,0 +1,19 @@ +FROM python:3.9 + +#Labels as key value pair +LABEL Maintainer="pepi1989" +RUN pip install --upgrade pip +COPY requirements.txt /requirements.txt +RUN pip install -r /requirements.txt + +RUN mkdir /app +ADD . /app +WORKDIR /app +EXPOSE 7474 +CMD [ "python","-u", "PPO_cg.py"] + + + + + + diff --git a/grouping/PPO_cg.py b/grouping/PPO_cg.py new file mode 100644 index 0000000000000000000000000000000000000000..437f5cfe2eaa29a814b5d59855cda67810c68c68 --- /dev/null +++ b/grouping/PPO_cg.py @@ -0,0 +1,832 @@ + +import tensorflow as tf +from tensorflow import keras +from tensorflow.keras import layers +import scipy.signal +import time +from threading import Thread +import json, os, time,uvicorn, requests, sys, stomp +from fastapi import FastAPI +from test_application import UtilitySimulator +from pydantic import BaseModel +from action_creator import prepareX, makeY_, find_solutions, final_list +from gym import Env +from gym.spaces import Discrete, Box +import numpy as np +import ast +import random +import requests +from random import shuffle +import string +from collections import deque +import tensorflow as tf +from tensorflow.keras import layers +from itertools import combinations +from tensorflow.keras import layers +from tensorflow.keras.models import Sequential +from tensorflow.keras.layers import Dense, Flatten +from tensorflow.keras.optimizers import Adam +from keras.models import load_model +from utility_interactor import UtilityInteractor + +url_polymorphic_solver = os.environ.get("URL_POLYMORPHIC_SOLVER","http://localhost:7879/groups") +config_dir = os.environ.get("CONFIG_DIR","./config_dir") + +app_id = None +uri_notification = None +request_type = None +full_filepath = None + +components_indexes_map = {} + +def get_component_name_from_solutions(solutions): + global components_indexes_map + result = [] + for solution in solutions: + solution_with_name = [] + for component_index in solution: + solution_with_name.append(components_indexes_map[component_index-1]) + result.append(solution_with_name) + return result + +def groupings(file_): + global filename_ + filename_ = file_ + + visitedList = [[]] + + + #env variables that are used inside the program + activemq_hostname = os.environ.get("ACTIVEMQ_HOST", "localhost") + activemq_port = int(os.environ.get("ACTIVEMQ_PORT", "61613")) + activemq_username = os.environ.get("ACTIVEMQ_USERNAME", "morphemic") + activemq_password = os.environ.get("ACTIVEMQ_PASSWORD", "morphemic") + amq_topic_to_send = os.environ.get("AMQ_TOPIC_REQUEST_CG", "/topic/solver_ug_request") + amq_topic_utility_receive = os.environ.get("AMQ_TOPIC_CG_REPLY", "/topic/solver_ug_reply") + + class Listener_(stomp.ConnectionListener): + + def __init__(self): + self.message_list=[] + + def on_error(self, headers, message): + print('received an error "%s"' % message) + def on_message(self, headers, message): + self.message_list.append(message) + + def amq_send_receive(data_): + connected = False + while not connected: + try: + hosts = [(activemq_hostname, activemq_port)] + conn = stomp.Connection(host_and_ports=hosts) + listener_ = Listener_() + conn.set_listener('', listener_) + conn.connect(activemq_username, activemq_password, wait=True) + connected = True + except Exception as e: + print("Connection failed, process will retry in 5 seconds") + time.sleep(5) + # Register a subscriber with ActiveMQ. This tells ActiveMQ to send + # all messages received on the topic 'topic-1' to this listener + try: + conn.subscribe(destination=amq_topic_to_send, ack='auto') + time.sleep(1) + conn.send(body=json.dumps(data_), destination=amq_topic_to_send) + time.sleep(1) + conn.disconnect() + conn.connect(activemq_username, activemq_password, wait=True) + conn.subscribe(destination=amq_topic_utility_receive, ack='auto') + time.sleep(5) + while True: + for element in listener_.message_list: + e_ = json.loads(element) + if e_['sender_id'] == data_['sender_id']: + result = e_["utility"] + conn.disconnect() + break + else: + continue + except: + print("Could not connect to the ACTIVEMQ Server") + result = None + + return result + + #function to assign a unique id to each request in utility generator + def get_random_ug_id(): + # choose from all lowercase letter + characters = string.ascii_letters + string.digits + password = ''.join(random.choice(characters) for i in range(8)) + return password + + #function to retrive the data from the .json file produced by the WF Analyzer + def export_data(): + ''' + This function is used to acquire the data fromcd Pro + the json file + Returns: the data/ the content + ''' + #final_path = os.path.join(os.getcwd(), file_) + with open(filename_, 'r+') as f: + data = json.load(f) + return data + + + #function to create all the possible paths from the application's workflow + visitedList = [[]] + def depthFirst(graph, currentVertex, visited): + visited.append(currentVertex) + for vertex in graph[currentVertex]: + if vertex not in visited: + depthFirst(graph, vertex, visited.copy()) + visitedList.append(visited) + return visitedList + + + #function to split the relative paths to smaller ones + def split_list(alist, wanted_parts=1): + length = len(alist) + return [ alist[i*length // wanted_parts: (i+1)*length // wanted_parts] + for i in range(wanted_parts) ] + + #function for supporting to filter combinations based on the combinations derived from the application's workflow + def intersection(lst1, lst2): + return list(set(lst1) & set(lst2)) + + #function to create all the possible combinations in lists based the nummber of elements in a list + def combination_per_len(components,r): + combinations_ = list(combinations(components, r)) + for idx, element in enumerate(combinations_): + combinations_[idx] = list(element) + return combinations_ + + + + ############################################## + ''' THE ENTIRE FUNCTIONALITY FOR COMMUNICATION BETWEEN CG AND UTILITY GENERATOR + - reads the json file from the WF Analyzer and exploits it + - create the app workflow graph and creates the filter for combinations + - usage of dlx algorithms to find the correct combinations + - creates the resources of each combinations and produces the input for th UG''' + + class Application_model: + def __init__(self): + self.data = export_data() + self.application_components = self.data['Number_of_components'] # i.e. 2 if the App has two Components + self.app_list = [i+1 for i in range(self.application_components)] # [1,2,...,n] + self.app_worfklow = self.data['Application_graph'] #to retrieve the application's flow + self.dict_cpus = {} + self.dict_ram = {} + self.dict_hardware={} + self.dict_variant={} + self.dict_instances={} + self.resources={} + self.final_resources=[] #[{'Component': 1, 'CORES': 1, 'RAM': 8100}, {'Component': 2, 'CORES': 1, 'RAM': 1024}] + self.solutions= [] #[[[1], [2]], [[1, 2]]] all the combinations + self.resource_combination = None + self.str_actions = None #['[[1], [2]]', '[[1, 2]]'] + self.workflow_rules=None + self.no_of_actions = None + + def transform_workflow(self): + global components_indexes_map + new_dict,dict_ = {}, {} + component_name_list,list_with_keys = [], [] + for idx,item in enumerate(self.data['Application_graph']): + new_dict[list(item.keys())[0]] = list(item.values())[0] + + for idx in range(self.data['Number_of_components']): + component_name = self.data['Application_components'][idx]['name'] + component_name_list.append(component_name) + components_indexes_map[idx] = component_name + original_values = list(new_dict.values()) + for element in original_values: + for id_, each_element in enumerate(element): + for idx_, e in enumerate(component_name_list): + if each_element == e: + element[id_] = idx_ + for i in range(0,len(original_values)): + list_with_keys.append(str(i)) + for idx,element in enumerate(component_name_list): + dict_.update({idx: original_values[idx]}) + + return dict_ + + def set_actions(self): + self.get_workflow() + self.get_all_resources() + self.create_combinations() + self.get_combinations() + self.resource_MAP() + self.no_of_actions = len(self.solutions) + + def get_default_conf(self): + return [[i+1] for i in range(self.application_components)] + + def get_resources(self, no_component): + dict_cpus,dict_ram, dict_gpu = {},{},{} + self.dict_variant.update({"variant":[self.data['Application_components'][no_component]['Variants']][0]}) + for element in self.data['General_requirements']: + for idx, element in enumerate(self.data['General_requirements']): + if idx!= self.data['Number_of_components']: + self.dict_instances.update({'instances': int(list(list(self.data['General_requirements'][no_component].values())[0][0].values())[0])}) + + for element in self.data['Application_components'][no_component]['Resources']: + for k, v in element.items(): + if k == "CPU": + dict_cpus['CORES'] = list(v[0].values())[0] + self.dict_cpus.update(dict_cpus) + if k == 'GPU': + dict_gpu['GPU_CORES'] = list(v[0].values())[0] + self.dict_hardware.update(dict_gpu) + + if k == 'RAM': + dict_ram['RAM'] = list(v[0].values())[0] + self.dict_ram.update(dict_ram) + if dict_gpu =={}: + self.dict_hardware.update({'GPU_CORES': 0}) + + + def reset_resources(self): + self.dict_cpus={} + self.dict_ram={} + self.dict_hardware={} + self.dict_variant={} + + def get_all_resources(self): + for i in range(self.application_components): + self.get_resources(i) + self.final_resources.append({'Component': i+1, 'CORES': int(list(self.dict_cpus.values())[0]), 'RAM': int(list(self.dict_ram.values())[0]), 'Hardware': list(self.dict_hardware.values())[0], 'variant': list(self.dict_variant.values())[0], 'instances': list(self.dict_instances.values())[0]}) + self.reset_resources() + + def get_workflow(self): + graph = {'graph': self.transform_workflow()} + graph = {int(k):[int(i) for i in v] for k,v in graph['graph'].items()} + list_=[] + visitedList = depthFirst(graph, 0, []) + for v_ in visitedList: + if len(v_)==None and len(v_)==1: + visitedList.remove(v_) + if len(v_)>2: + for first, second in zip(v_, v_[1:]): + visitedList.append([first, second]) + + for element in visitedList: + if len(element)==0 or len(element)==1: + visitedList.remove(element) + else: + pass + + for element in visitedList: + list_.append(str(element)) + + final_list = list(set(list_)) + visitedList=[] + for element in final_list: + visitedList.append(ast.literal_eval(element)) + + for element in visitedList: + for idx,e_ in enumerate(element): + element[idx] = e_+1 + self.workflow_rules = visitedList + + def combination_list(self): + final_list=[] + self.get_workflow() + final_list.append(self.app_list) + for element in self.app_list: + final_list.append([element]) + + for r in range(2, len(self.app_list)): + combinations_ = combination_per_len(self.app_list, r) + + for element in combinations_: + for rule in self.workflow_rules: + if intersection(element, rule) == rule and len(element) == len(rule): + final_list.append(element) + + return final_list + + + def create_combinations(self): + return self.final_resources + + def get_combinations(self): + + id = [i+1 for i in range(self.application_components)] + combinations = self.combination_list() + X = id + Y = makeY_(final_list(X, combinations)) + X_set = prepareX(X, Y) + self.solutions = find_solutions(X_set, Y) + return self.solutions + + def filter_node_candidates(self): + pass + + def create_single_resources(self, i, resource): + mid_cores_list,mid_mem_list,f_list,list_with_min=[],[],[],[] + for element in self.solutions[i]: + if len(element)>1: + for e in element: + idx= e-1 + mid_cores_list.append(self.final_resources[idx][resource]) + f_list.append(mid_cores_list) + mid_cores_list=[] + else: + idx=element[0]-1 + + mid_cores_list.append(self.final_resources[idx][resource]) + f_list.append(mid_cores_list) + mid_cores_list=[] + for element in f_list: + + min_=0 + if len(element)>1: + min_=min(element) + for i in range(len(element)): + list_with_min.append(min_) + + else: + list_with_min.append(element[0]) + + return list_with_min + + def resource_MAP(self): + resource_list,resource_merge_list=[],[] + str_states=[] + R = 'RAM' + C = 'CORES' + H = 'Hardware' + V = 'variant' + I = 'instances' + for i in range(0,len(self.solutions)): + str_states.append("{}".format(self.solutions[i])) + flat_list = [item for sublist in self.solutions[i] for item in sublist] + cores= self.create_single_resources(i, resource=C) + mem_=self.create_single_resources(i, resource=R) + instances = self.create_single_resources(i, resource=I) + for i in range(len(flat_list)): + #print({'name': flat_list[i], 'properties': [cores[i],mem_[i], 1, "CPU", "VM"]}) + resource_list.append({'name': 'Component_{}'.format(flat_list[i]), 'properties': [cores[i],mem_[i],int(instances[i]), 'CPU', "[]"]}) + resource_merge_list.append(resource_list) + resource_list=[] + + self.resource_combination = resource_merge_list + self.str_actions = str_states + + def create_input_utility(self, id): + hardw_ = [] + input_utility, dict_cores, dict_ram, dict_instances, dict_variant, dict_harware, dict_component_name= {}, {}, {}, {}, {}, {}, {} + resources=[] + input_utility.update({"target": "utility", "sender_id": "gc_{}".format(id), "combination": self.str_actions[id], "variables": self.resource_combination[id]}) + for element in range(len(input_utility['variables'])): + dict_component_name.update({"component": element+1}) + dict_cores.update({"cores": input_utility['variables'][element]['properties'][0]}) + dict_ram.update({"memory": input_utility['variables'][element]['properties'][1]}) + dict_instances.update({"instances": int(list(self.data['General_requirements'][element].values())[0][0]['minInstances'])}) + dict_variant.update({'variant': '[]'}) + dict_harware.update({'Hardware': 'CPU'}) + resources.append({"component": dict_component_name['component'], "_cores": dict_cores['cores'], "_memory": dict_ram['memory'], "_instances": dict_instances['instances'], "_variant": dict_variant['variant'], "_hardware": dict_harware['Hardware']}) + dict_cores, dict_ram,dict_instances, dict_variant, dict_harware= {}, {}, {}, {}, {} + + + return ({"target": "utility",'sender_id': get_random_ug_id(),'combination':input_utility['combination'],'variables':resources}) + + + ''' RL CUSTOM ENVIRONMENT + - ACTIONS: {"0": [[[1,2]], "1": [[1], [2]]]} (i.e. in an app with 2 components + - OBSERVATION SPACE: 1D, TAKES THE UTILITY VALUES FOR EACH COMBINATION + - INITIAL ACTION :0 + - INITIAL UTILITY VALUE: 0 SINCE THE APP HAS NOT STARTED YET''' + + class Application_Env(Env): + def __init__(self): + self.application=Application_model() + self.action_space = Discrete(actions) # number of possible actions + self.observation_space = Box(low = np.array([11]), high = np.array([200]), dtype=np.int64) + self.utility_state = 0 + self.utility_interactor = UtilityInteractor() + self.action_masked = [i for i in range(len(self.application.get_combinations()))] + self.original_actions = [i for i in self.application.get_combinations()] + self.initial_action = self.application.get_default_conf() + self.masked_initial_action = [len(i) for i in self.original_actions].index(max([len(i) for i in self.original_actions])) + self.list_of_inputs= [] + self.metrics_to_predict = None + + def prepare_inputs(self): + self.application.get_workflow() + self.application.get_all_resources() + self.application.create_combinations() + self.application.get_combinations() + self.application.resource_MAP() + self.application.get_combinations() + for i in range(len(self.application.solutions)): + self.list_of_inputs.append(self.application.create_input_utility(i)) + + + def get_performance(self, action): + data = self.list_of_inputs[action] + #return data,random.uniform(0.1, 0.9) + print("Data sent -> ",data) + return self.utility_interactor.getPerformance(data) + """ + while True: + utility_value = amq_topic_utility_receive(data, action) + if utility_value != None: + break + else: + continue + return utility_value""" + + + def step(self, action): + + print('ACTION:{}'.format(self.application.solutions[action])) + input_, utility = self.get_performance(action) + print(input_) + ''' + JD only to check if it is working - in this part the ulitity should work instead of a random or the + simulated application + ''' + #input_, utility = self.get_performance(action) + #util = UtilitySimulator(input_) + #util.generateLoad(lower_bound=11, upper_bound=200) + #load_ = util.load + #util.computePerformance() + #application_performance = util.getPerfomance() + application_performance = random.randint(1,300) + scale_app_performance = (application_performance-0.05)/(300-0.05) + + if scale_app_performance<0: + scale_app_performance = 0 + else: + pass + print('performance based on utility: {}'.format(scale_app_performance)) + if scale_app_performance>=0.7: + reward=1 + done=True + else: + reward = -1 + done=True + info={} + self.state = random.uniform(0.1, 0.9) + ''' + if self.state>200: + self.state=200 + elif self.state<11: + self.state=11 + else: + pass + ''' + return self.state, reward, done, info, self.state + + def reset(self): + self.state= 0 + self.initial_action = self.action_space.sample() + self.initial_mask_action= [len(i) for i in self.original_actions].index(max([len(i) for i in self.original_actions])) + return self.state + + + def discounted_cumulative_sums(x, discount): + # Discounted cumulative sums of vectors for computing rewards-to-go and advantage estimates + return scipy.signal.lfilter([1], [1, float(-discount)], x[::-1], axis=0)[::-1] + + + class Buffer: + # Buffer for storing trajectories + def __init__(self, observation_dimensions, size, gamma=0.99, lam=0.95): + # Buffer initialization + self.observation_buffer = np.zeros( + (size, observation_dimensions), dtype=np.float32 + ) + self.action_buffer = np.zeros(size, dtype=np.int32) + self.advantage_buffer = np.zeros(size, dtype=np.float32) + self.reward_buffer = np.zeros(size, dtype=np.float32) + self.return_buffer = np.zeros(size, dtype=np.float32) + self.value_buffer = np.zeros(size, dtype=np.float32) + self.logprobability_buffer = np.zeros(size, dtype=np.float32) + self.gamma, self.lam = gamma, lam + self.pointer, self.trajectory_start_index = 0, 0 + + def store(self, observation, action, reward, value, logprobability): + # Append one step of agent-environment interaction + self.observation_buffer[self.pointer] = observation + self.action_buffer[self.pointer] = action + self.reward_buffer[self.pointer] = reward + self.value_buffer[self.pointer] = value + self.logprobability_buffer[self.pointer] = logprobability + self.pointer += 1 + + def finish_trajectory(self, last_value=0): + # Finish the trajectory by computing advantage estimates and rewards-to-go + path_slice = slice(self.trajectory_start_index, self.pointer) + rewards = np.append(self.reward_buffer[path_slice], last_value) + values = np.append(self.value_buffer[path_slice], last_value) + + deltas = rewards[:-1] + self.gamma * values[1:] - values[:-1] + + self.advantage_buffer[path_slice] = discounted_cumulative_sums( + deltas, self.gamma * self.lam + ) + self.return_buffer[path_slice] = discounted_cumulative_sums( + rewards, self.gamma + )[:-1] + + self.trajectory_start_index = self.pointer + + def get(self): + # Get all data of the buffer and normalize the advantages + self.pointer, self.trajectory_start_index = 0, 0 + advantage_mean, advantage_std = ( + np.mean(self.advantage_buffer), + np.std(self.advantage_buffer), + ) + self.advantage_buffer = (self.advantage_buffer - advantage_mean) / advantage_std + return ( + self.observation_buffer, + self.action_buffer, + self.advantage_buffer, + self.return_buffer, + self.logprobability_buffer, + ) + + + def mlp(x, sizes, activation=tf.tanh, output_activation=None): + # Build a feedforward neural network + for size in sizes[:-1]: + x = layers.Dense(units=size, activation=activation)(x) + return layers.Dense(units=sizes[-1], activation=output_activation)(x) + + + def logprobabilities(logits, a): + # Compute the log-probabilities of taking actions a by using the logits (i.e. the output of the actor) + logprobabilities_all = tf.nn.log_softmax(logits) + logprobability = tf.reduce_sum( + tf.one_hot(a, num_actions) * logprobabilities_all, axis=1 + ) + return logprobability + + + # Sample action from actor + @tf.function + def sample_action(observation): + logits = actor(observation) + action = tf.squeeze(tf.random.categorical(logits, 1), axis=1) + return logits, action + + + # Train the policy by maxizing the PPO-Clip objective + @tf.function + def train_policy( + observation_buffer, action_buffer, logprobability_buffer, advantage_buffer + ): + + with tf.GradientTape() as tape: # Record operations for automatic differentiation. + ratio = tf.exp( + logprobabilities(actor(observation_buffer), action_buffer) + - logprobability_buffer + ) + min_advantage = tf.where( + advantage_buffer > 0, + (1 + clip_ratio) * advantage_buffer, + (1 - clip_ratio) * advantage_buffer, + ) + + policy_loss = -tf.reduce_mean( + tf.minimum(ratio * advantage_buffer, min_advantage) + ) + policy_grads = tape.gradient(policy_loss, actor.trainable_variables) + policy_optimizer.apply_gradients(zip(policy_grads, actor.trainable_variables)) + + kl = tf.reduce_mean( + logprobability_buffer + - logprobabilities(actor(observation_buffer), action_buffer) + ) + kl = tf.reduce_sum(kl) + return kl + + + # Train the value function by regression on mean-squared error + @tf.function + def train_value_function(observation_buffer, return_buffer): + with tf.GradientTape() as tape: # Record operations for automatic differentiation. + value_loss = tf.reduce_mean((return_buffer - critic(observation_buffer)) ** 2) + value_grads = tape.gradient(value_loss, critic.trainable_variables) + value_optimizer.apply_gradients(zip(value_grads, critic.trainable_variables)) + + + # Hyperparameters of the PPO algorithm + steps_per_epoch = 30 + epoch = 20 + gamma = 0.99 + clip_ratio = 0.2 + policy_learning_rate = 3e-4 + value_function_learning_rate = 1e-3 + train_policy_iterations = 80 + train_value_iterations = 80 + lam = 0.97 + target_kl = 0.01 + hidden_sizes = (64, 64) + + # True if you want to render the environment + render = False + + + # Initialize the environment and get the dimensionality of the + # observation space and the number of possible actions + #filename_ = 'wf.json' + filename_ = file_ + app= Application_model() + app.set_actions() + x = app.solutions + global actions + actions = app.no_of_actions + print(actions) + print('Initializing actions......') + time.sleep(2) + print('No of possible grouping combinations: {}'.format(actions)) + time.sleep(1) + print('Combinations are: {}'.format(app.solutions)) + env = Application_Env() + env.prepare_inputs() + + observation_dimensions = env.observation_space.shape[0] + num_actions = env.action_space.n + list_of_scores=[] + list_of_groupings=[] + + + + # Initialize the buffer + buffer = Buffer(observation_dimensions, steps_per_epoch) + + # Initialize the actor and the critic as keras models + observation_input = keras.Input(shape=(observation_dimensions,), dtype=tf.float32) + logits = mlp(observation_input, list(hidden_sizes) + [num_actions], tf.tanh, None) + actor = keras.Model(inputs=observation_input, outputs=logits) + value = tf.squeeze( + mlp(observation_input, list(hidden_sizes) + [1], tf.tanh, None), axis=1 + ) + critic = keras.Model(inputs=observation_input, outputs=value) + + # Initialize the policy and the value function optimizers + policy_optimizer = tf.keras.optimizers.Adam(learning_rate=policy_learning_rate) + value_optimizer = tf.keras.optimizers.Adam(learning_rate=value_function_learning_rate) + + # Initialize the observation, episode return and episode length + observation, episode_return, episode_length,observation = env.reset(), 0, 0, env.reset() + + + list_sum_legth=[] + # Iterate over the number of epochs + for e in range(0,10): + #print('EPOCH: {}'.format(epoch)) + #print('epoch {}'.format(epoch)) + # Initialize the sum of the returns, lengths and number of episodes for each epoch + sum_return = 0 + sum_length = 0 + num_episodes = 0 + list_episodes=[] + + + # Iterate over the steps of each epoch + for t in range(steps_per_epoch): + #print('step per epoch {}/{}'.format(t, steps_per_epoch)) + #print(t) + if render: + env.render() + + + # Get the logits, action, and take one step in the environment + observation = np.array([observation]) + observation=observation.reshape(1, -1) + logits, action = sample_action(observation) + observation_new, reward, done, _,observation_new = env.step(action[0].numpy()) + episode_return += reward + episode_length += 1 + + # Get the value and log-probability of the action + value_t = critic(observation) + logprobability_t = logprobabilities(logits, action) + + # Store obs, act, rew, v_t, logp_pi_t + buffer.store(observation, action, reward, value_t, logprobability_t) + + + # Update the observation + observation = observation_new + + # Finish trajectory if reached to a terminal state + terminal = done + if terminal or (t == steps_per_epoch - 1): + list_of_scores.append((app.solutions[action.numpy()[0]],observation)) + last_value = 0 if done else critic(np.array([[observation]])) + buffer.finish_trajectory(last_value) + sum_return += episode_return + sum_length += episode_length + num_episodes += 1 + observation, episode_return, episode_length = env.reset(), 0, 0 + + # Get values from the buffer + ( + observation_buffer, + action_buffer, + advantage_buffer, + return_buffer, + logprobability_buffer, + ) = buffer.get() + + # Update the policy and implement early stopping using KL divergence + for _ in range(train_policy_iterations): + kl = train_policy( + observation_buffer, action_buffer, logprobability_buffer, advantage_buffer + ) + if kl > 1.5 * target_kl: + # Early Stopping + break + + # Update the value function + for _ in range(train_value_iterations): + train_value_function(observation_buffer, return_buffer) + + # Print mean return and length for each epoch + + print( + f" Epoch: {e + 1}. Mean Return: {sum_return / num_episodes}. Mean Length: {sum_length / num_episodes}" + ) + list_sum_legth.append(sum_length / num_episodes) + list_of_groupings.append(list_of_scores) + list_of_scores=[] + + global full_filepath, app_id, uri_notification, request_type + #print(list_sum_legth) + actions=[] + + performance_m = [] + for element in list_of_groupings: + actions.append(element[0]) + performance_m.append(element[0]) + + max_ = max(performance_m) + solutions_ = (max_) + print(solutions_) + print(components_indexes_map) + data_to_send_to_ps = {"utility": solutions_[1], "application_id": app_id,"uri_notification": uri_notification, "request_type":request_type, "result": get_component_name_from_solutions(solutions_[0]), "json_path": full_filepath} + headers = {"Content-Type":"application/json"} + try: + response = requests.post(url=url_polymorphic_solver,data=json.dumps(data_to_send_to_ps), headers=headers) + print(response) + except Exception as e: + print("Could not reach the polymorphic solver") + + +app = FastAPI() + +class RequestWA(BaseModel): + applicationId: str + filepath: str + request_type: str + uri_notification: str + +@app.get("/") +async def home(): + return {"message": "Welcome"} + +@app.post("/groups") +async def grouping(request:RequestWA): + print("Data received") + global full_filepath, app_id, uri_notification, request_type + app_id = request.applicationId + full_filepath = config_dir +"/"+ request.filepath + request_type = request.request_type + uri_notification = request.uri_notification + t = Thread(target=groupings, args=(full_filepath,)) + t.start() + #solutions_ = groupings(file_=full_filepath) + """ + data_to_send_to_ps = {"utility": solutions_[1], "application_id": app_id,"uri_notification": uri_notification, "request_type":request_type, "result": solutions_[0], "json_path": full_filepath} + headers = {"Content-Type":"application/json"} + try: + response = requests.post(url=url_polymorphic_solver,data=json.dumps(data_to_send_to_ps), headers=headers) + print(response) + except Exception as e: + print("Could not reach the polymorphic solver") """ + return {"status": True} + +if __name__ == "__main__": + uvicorn.run(app,host="0.0.0.0", port=7474) + +''' +actions_=[] +performances=[] + + +print(list_of_groupings) +for element in list_of_groupings: + actions_.append(element[0]) + performances.append(element[1]) + +print(max(performances)) +''' \ No newline at end of file diff --git a/grouping/README.md b/grouping/README.md new file mode 100644 index 0000000000000000000000000000000000000000..3dab2c634c99c9c865f71eb6be9b3b2f31ba7e87 --- /dev/null +++ b/grouping/README.md @@ -0,0 +1,16 @@ +# Component grouping (CG) + +Component grouping has been designed in order to find the optimal groups of components of an Application such that to maximize the application's hollistic performance + +## - The CG exposes an API endpoint, and expects as data in the below form: + #### {"applicationId": "id from camunda process", "filepath": "directory that the workflow analyzer json file is located","request_type": "request type", "uri_notification": "uri_notification"} + +In case that the cg will be tested standalone a POST request must me implemented and an example request follows: +curl -X POST -H "Content-Type: application/json" -d '{"applicationId": "x", "filepath": "directory that the workflow analyzer json file is located", "request_type": "type of request", "uri_notification": "uri_notification"}' + +## Prerequisites + +The component grouping is connected with the Utility generator thus, in case that an activemq connection has not been established then it will fail + +The result of component grouping is a json-oriented message like the below example: +### {"utility":1.0,"application_id":"id_1","request_type":"optimization","result":[[1,2,3]],"json_path":"x"} \ No newline at end of file diff --git a/grouping/action_creator.py b/grouping/action_creator.py new file mode 100644 index 0000000000000000000000000000000000000000..994fdda8d521993a715ad1842ecf61d3e00bb1e8 --- /dev/null +++ b/grouping/action_creator.py @@ -0,0 +1,195 @@ +from itertools import combinations, permutations +from re import L +import ast +import modifier +import time + +# ============= SCRIPT SHORT DESCRIPTION==================== +#THIS SCRIPT READS THE NUMBER OF COMPONENTS AS A LIST +#DERIVED FROM THE camel_reader.py FILE +#THEN BY USING THE DLX ALGORITHM, IT SEEKS TO FIND THE EXACT COVER FROM THE NUMBER OF APP COMPONENTS +# FINALLY RETURNS ALL THE POSSIBLE COMPONENT COMBINATIONS AS ACTIONS FOR RL AGENT +# FOR EXAMPLE IF AN APPLICATION HAS 3 COMPONENTS, THEN THE RESULT WILL BE: +# ACTIONS: {0: [[1], [2],[3]] -> [[1,2],[3]], 1: [[1,3],[2]] -> [[2,3],[1]], 2: [[2,3],[1]] -> [[1,2,3]] } + +#STATES: [[1],[2],[3]], [[1,2], [3]], [[1,3], [2]], [[1,2,3]], [[2,3],[1]] + + +#========== DLX ALGORITHM FOR COMPONENT COMBINATION=============# +def solve(X, Y, solution=[]): + if not X: + yield list(solution) + else: + c = min(X, key=lambda c: len(X[c])) + for r in list(X[c]): + solution.append(r) + cols = select(X, Y, r) + for s in solve(X, Y, solution): + yield s + deselect(X, Y, r, cols) + solution.pop() + +def select(X, Y, r): + cols = [] + for j in Y[r]: + for i in X[j]: + for k in Y[i]: + if k != j: + X[k].remove(i) + cols.append(X.pop(j)) + return cols + +def deselect(X, Y, r, cols): + for j in reversed(Y[r]): + X[j] = cols.pop() + for i in X[j]: + for k in Y[i]: + if k != j: + X[k].add(i) + +#========= PREPARATION OF THE PROPER SETS AND SUBSETS========= + +def create_components_list(pruned_comb): + combinations=[] + length_ = max(len(x) for x in pruned_comb) + for element in range(1, length_+1): + combinations.append(element) + return combinations + +def makeY(X): + result = [] + _dict = {} + for i in range(len(X)+1): + cmbs = list(combinations(X,i)) + for cmb in cmbs: + if len(cmb) > 0: + result.append(list(cmb)) + for i in range(len(result)): + _dict[str(i)] = result[i] + return _dict + + +def makeY_(X): + result = [] + _dict = {} + for cmb in X: + if len(cmb) > 0: + result.append(list(cmb)) + for i in range(len(result)): + _dict[str(i)] = result[i] + return _dict + +def final_list(X, pruned_combinations): + list_ = [i for i in makeY(X).values()] + for idx, element in enumerate(list_): + list_[idx] = str(element) + for idx, element in enumerate(pruned_combinations): + pruned_combinations[idx] = str(element) + + final = list(set(list_)- set(pruned_combinations)) + + for element in final: + list_.remove(element) + + for idx,element in enumerate(list_): + list_[idx] = ast.literal_eval(element) + + return list_ + +def prepareX(X, Y): + X = {j: set() for j in X} + for i in Y: + for j in Y[i]: + X[j].add(i) + return X + +#===== THE FUNCTION THAT FINDS THE SOLUTIONS============ +def find_solutions(X_set, Y): + solved = list(solve(X_set, Y)) + solutions = [] + for sol in solved: + solution = [] + for index in sol: + solution.append(Y[index]) + solutions.append(solution) + return solutions + +def get_actions(solutions): + length_=2 + actions=[] + keys=[] + for subset in permutations(solutions, length_): + actions.append(list(subset)) + for i in range(0,len(actions)): + keys.append(i) + + dict_=dict(zip(keys,actions)) + #print(dict_) + + return actions, dict_ + +def output(solution, pruned_resources, pruned_combinations): + + list_with_dicts=[] + i=0 + + for idx, element in enumerate(pruned_combinations): + pruned_combinations[idx] = ast.literal_eval(element) + for element in solution: + list_with_combinations = [] + list_with_resources=[] + dict_={} + + for element_ in element: + list_with_combinations.append(pruned_combinations.index(element_)) + + for element in list_with_combinations: + list_with_resources.append(pruned_resources[element]) + + row_comb= {'combination_{}'.format(i): solution[i]} + row_resource={"resources": list_with_resources} + dict_.update(row_comb) + dict_.update(row_resource) + list_with_dicts.append(dict_) + i+=1 + + return list_with_dicts + +def recreate_actions(list_): + for element in list_: + for e_ in element: + if len(e_) ==3: + e_ = e_.sort(key=max) + return list_ + +def final(): + #description, X, category = camel_reader.main() + pruned_combinations, pruned_resources = modifier.final() + print(pruned_combinations) + X = create_components_list(pruned_combinations) + Y = makeY_(final_list(X, pruned_combinations)) + X_set = prepareX(X, Y) + + sol = find_solutions(X_set, Y) + + actions, action_dict = get_actions(sol) + + + #print('len:', len(actions)) + #print(sol) + + list_of_output = output(sol, pruned_resources, pruned_combinations) + + actions_ = recreate_actions(actions) + sol_ = list(map(sorted, sol)) + #print(sol_) + #print("*********************************") + #print(list_of_output) + return actions_, sol_, list_of_output + +if __name__ == "__main__": + #====== RETURNS ALL THE COMBINATIONS===========# + #FINALLY THIS RESULT IS PASSING TO THE SCRIPT WITH THE RL ENVIRONMENT + + final() + diff --git a/grouping/amq_client/Event.py b/grouping/amq_client/Event.py new file mode 100644 index 0000000000000000000000000000000000000000..52dbc842ef3c729386bed8f8e7cd4b92bc9420c6 --- /dev/null +++ b/grouping/amq_client/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/grouping/amq_client/MorphemicConnection.py b/grouping/amq_client/MorphemicConnection.py new file mode 100644 index 0000000000000000000000000000000000000000..9fd61b18c35ed6f26bd3197c53d5209aaa77ef41 --- /dev/null +++ b/grouping/amq_client/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, 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/grouping/amq_client/MorphemicListener.py b/grouping/amq_client/MorphemicListener.py new file mode 100644 index 0000000000000000000000000000000000000000..35e2eeeb41cf864f2ca4d47dded4ee75e3c51721 --- /dev/null +++ b/grouping/amq_client/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/grouping/amq_client/Payloads.py b/grouping/amq_client/Payloads.py new file mode 100644 index 0000000000000000000000000000000000000000..5de1adc844e289db185d74f7c7d76bca0045d686 --- /dev/null +++ b/grouping/amq_client/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/grouping/amq_client/__init__.py b/grouping/amq_client/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..45fe25b1772cc225e9ec364aa83620bd07f4e3a7 --- /dev/null +++ b/grouping/amq_client/__init__.py @@ -0,0 +1,5 @@ + +from . import MorphemicConnection as morphemic +from . import MorphemicListener as listener +from . import Event as events +from . import Payloads as payloads \ No newline at end of file diff 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0000000000000000000000000000000000000000..c5097cdefe2bc6d86743efb6fc4c4c77c1d7f9e5 Binary files /dev/null and b/grouping/amq_client/__pycache__/adapter.cpython-39.pyc differ diff --git a/grouping/amq_client/adapter.py b/grouping/amq_client/adapter.py new file mode 100644 index 0000000000000000000000000000000000000000..66740125b48a76d31750dd480936907cb8bd1852 --- /dev/null +++ b/grouping/amq_client/adapter.py @@ -0,0 +1,113 @@ +import logging, time, os +from threading import Thread + +from importlib_metadata import metadata +from amq_client.MorphemicConnection import Connection + +activemq_port = int(os.environ.get("ACTIVEMQ_PORT", "61613")) +activemq_hostname = os.environ.get("ACTIVEMQ_HOST", "localhost") +activemq_topic = os.environ.get("ACTIVEMQ_TOPIC", "static-topic-1") +activemq_subs_key = os.environ.get("ACTIVEMQ_SUBS_KEY", "subs-1") +activemq_username = os.environ.get("ACTIVEMQ_USERNAME", "aaa") +activemq_password = os.environ.get("ACTIVEMQ_PASSWORD", "111") + +logname = "./log/cg.log" +logging.basicConfig(filename=logname,filemode='a',format='%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s',datefmt='%H:%M:%S',level=logging.DEBUG) + +class Listener(object): + def __init__(self, conn, handler, metadata): + self.conn = conn + self.count = 0 + self.metadata = metadata + self.handler = handler + self.start = time.time() + + def on_error(self, frame): + print("received an error %s" % frame.body) + + def on_message(self, frame): + self.handler(frame.body, self.metadata) + +class Consumer(Thread): + def __init__(self, handler, queue, consumer_id = int(time.time()), metadata = {}): + self.handler = handler + self.queue = queue + self.consumer_id = consumer_id + self.metadata = metadata + self.conn = None + self.stop_consumer = False + super(Consumer,self).__init__() + + def stop(self): + self.stop_consumer = True + if self.conn: + self.conn.disconnect() + + def run(self): + connected = False + while not connected: + if self.stop_consumer: + break + try: + print('Subscribe to the topic {0}'.format(self.queue)) + #logging.info('Subscribe to the topic {0}'.format(self.queue)) + self.conn = Connection(username=activemq_username, password=activemq_password, host=activemq_hostname,port=activemq_port, debug=False) + self.conn.connect() + self.conn.set_listener('', Listener(self.conn, self.handler, self.metadata)) + self.conn.subscribe(destination=self.queue, id=self.consumer_id, ack='auto') + connected = True + except Exception as e: + print("Could not subscribe") + logging.error("Could not subscribe to the topic {0}".format(self.queue)) + print(e) + connected = False + +class Publisher(Thread): + def __init__(self): + self.message = None + self.destination = None + self.client = None + super(Publisher, self).__init__() + + def setParameters(self, message, queue): + self.message = message + self.queue = queue + + 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') + #logging.info('The publisher tries to connect to ActiveMQ broker') + self.client = Connection(username=activemq_username, password=activemq_password, host=activemq_hostname,port=activemq_port, debug=False) + self.client.connect() + print("connection established") + logging.info("connection established !!!") + return True + except: + print("Could not connect the publisher") + logging.error("Could not connect the publisher") + + def send(self): + if self.message == None or self.queue == None: + print("Message or queue is None") + return False + if not self.client: + time.sleep(5) + self.send() + 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) + return True + except Exception as e: + print(e) + self.client.disconnect() + print("Reconnection in 10s ...") + logging.info("Reconnection in 10s ...") + time.sleep(10) + self.connect() + self.send() \ No newline at end of file diff --git a/grouping/camel_reader.py b/grouping/camel_reader.py new file mode 100644 index 0000000000000000000000000000000000000000..63bb2119d81aab98f5205c02c0bf355520d4f41d --- /dev/null +++ b/grouping/camel_reader.py @@ -0,0 +1,102 @@ + +import random +import json +import os +import time + +def URL_files(directory): + """ + This function takes as a input the directory + and returns the relevant file + """ + os.chdir(directory) + for file_ in os.listdir(): + if file_.endswith("final_ComponentGroupin.json"): + result = file_ + break + else: + result = 'file not exists' + return result + +def export_data(file_): + ''' + This function is used to acquire the data from + the json file + Returns: the data/ the content + ''' + final_path = os.path.join(os.getcwd(), file_) + with open(final_path) as f: + data = json.load(f) + return data + +class JSON_EXTRACTOR: + def __init__(self, data): + self.data = data + #self.no_of_components = self.get_Application_Components() + #self.descr_components=self.get_all_components() + + def prettyprint(self): + return json.dumps(self.data, indent=4) + + def get_no_Components(self): + components_list=[] + no_of_components = self.data['Applications_components'][0]['Number_of_components'] + for iteration in range(no_of_components): + components_list.append(iteration+1) + + return components_list + + def get_ram(self): + ram = int(self.data['Description'][0]['Component_First'][6]['Requirements'][1][1][1][0]['minRamApp'])/1000 + return ram + + def get_cores(self): + return self.data['Description'][0]['Component_First'][6]['Requirements'][0][1][1][0]['minCoresApp'] + + def create_the_json(self): + components=[] + for element in self.get_no_Components(): + print(element) + dict_ = {'Component': element, "CORES": self.get_cores(), 'RAM': self.get_ram()} + components.append(dict_) + return components + + +def main(): + directory_ = input("Enter the path of your file: ") + isFile = os.path.isdir(directory_) + if isFile == True: + file_ = URL_files(directory_) + data = export_data(file_) + #list_of_components=[] + APP = JSON_EXTRACTOR(data) + #print(APP.data['Description'][1]) + #description = APP.get_all_components() + #list_of_components = APP.get_no_components() + #category = APP.get_variants() + #print(data['Applications_components'][0]['Number_of_components']) + #print(APP.get_no_Components()) + #print(APP.get_cores()) + print(APP.create_the_json()) + return data + #print(description, list_of_components, category) + #return description, list_of_components, category + else: + + response = "Sorry, directory is not exists" + #print(response) + + return response + + +if __name__ == "__main__": + main() + + + + + + + + + diff --git a/grouping/config_dir/.DS_Store b/grouping/config_dir/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 Binary files /dev/null and b/grouping/config_dir/.DS_Store differ diff --git a/morphemic-performance-model/images/.gitkeep b/grouping/log/cg.log similarity index 100% rename from morphemic-performance-model/images/.gitkeep rename to grouping/log/cg.log diff --git a/grouping/modifier.py b/grouping/modifier.py new file mode 100644 index 0000000000000000000000000000000000000000..2cab8b717b7c19f2b4d6a626cb4cc36367c64867 --- /dev/null +++ b/grouping/modifier.py @@ -0,0 +1,172 @@ +from itertools import combinations +import camel_reader #script camel_reader.py +from itertools import chain, groupby +from operator import itemgetter +from collections import defaultdict + +import ast + +#necessary variables-lists +removed_dict=[] +final_list=[] +final_list_resources=[] +max_dict_resources=[] +solo_dict_resources=[] +combination_of_components=[] + +PORT = 8000 +URL = "http://127.0.0.1:{}/getInformation".format(PORT) + +def combination_list(components): + final_list=[] + #creates all the combination of components based on itertools.combinations + for element in range(1, len(components)+1): + a = list(combinations(components, element)) + for element_ in a: + final_list.append(list(element_)) + #returns: [1], [2], [3], [1,2], [1,3] + return (final_list) + +def removekey(d): + #removes the unnecessary keys from camel description + keys=['Component', 'status'] + del d[keys[0]] + del d[keys[1]] + #returns new dicts without the unecessary components + return d + +def comb(dict_tuple): + #appends dictionaries as much as we want + dd = defaultdict(list) + for d in dict_tuple: # you can list as many input dicts as you want here + for key, value in d.items(): + dd[key].append(value) + + return(dd) + +def convert_to_str(list_): + #converts a list-element to string in order to create a dictionary + new_list=[] + for element in list_: + new_list.append(str(element)) + return new_list + + +def reversed_type(list_): + #converts str to dicts + LIST_DICTS=[] + for element in range(len(list_)): + LIST_DICTS.append(ast.literal_eval(list_[element])) + return (LIST_DICTS) + +class INFO_TABLE: + def __init__(self, dictionary:dict): + self.dictionary_ = dictionary + self.combinations = [key for key in self.dictionary_.keys()] + self.resources = [ast.literal_eval(element) for element in self.dictionary_.values()] + self.original_combinations=[] + self.original_combinations = self.modify_combinations() + self.idx, self.cores, self.ram=[],[],[] + self.cores, self.ram = self.CORES_RAM_implementation() + self.idx=self.create_the_table() + self.eliminate=[] + self.vcpu,self.memory=[],[] + self.vcpu,self.memory= self.get_modified_candidates() + self.node_resources,self.components_resources,self.elimination, self.keep=[],[],[], [] + self.final_combinations, self.final_resources=[], [] + + def CORES_RAM_implementation(self): + for element in self.resources: + for k,v in element.items(): + if k == 'CORES': + self.cores.append(v) + else: + self.ram.append(v) + + return self.cores, self.ram + + def modify_combinations(self): + for element in self.combinations: + self.original_combinations.append(ast.literal_eval(element)) + + return self.original_combinations + + def create_the_table(self): + for element in range(1, len(self.combinations)+1): + self.idx.append(element) + return self.idx + + def get_modified_candidates(self): + for element in self.candidates: + for k,v in element.items(): + if k=='memory': + self.memory.append((int(v[:-3]))) + elif k=='vcpu': + self.vcpu.append(int(v)) + + return self.vcpu, self.memory + + + def eliminations(self): + for element in range(len(self.cores)): + self.components_resources.append([self.cores[element], self.ram[element]]) + for element in range(len(self.vcpu)): + self.node_resources.append([self.vcpu[element], self.memory[element]]) + for element in self.components_resources: + self.eliminate=[] + for ele in self.node_resources: + if element[0]<=ele[0] and element[1]<=ele[1]: #checks if node resources are capable to support the combination and sets the value 1: + self.eliminate.append(1) + else: + self.eliminate(0) + self.elimination.append((max(self.eliminate))) #otherwise, sets the value 0 + + for idx, element in enumerate(self.elimination): + if element==1: + self.keep.append(idx) + + for element in self.keep: + self.final_combinations.append(self.original_combinations[element]) + for element in self.keep: + self.final_resources.append(self.resources[element]) + + return self.final_combinations, self.final_resources + +def final(): + resources, components, category = camel_reader.main() + for element in resources: + removed_dict.append(removekey(element)) + for element in combination_list(removed_dict): + if len(tuple(element))>1: + max_dict_resources.append(dict(comb(tuple(element)))) + else: + solo_dict_resources.extend(element) + for element in max_dict_resources: + for k, v in element.items(): + element[k] = max(v) + + combination_list_final = convert_to_str(combination_list(components)) #keys + all_properties = convert_to_str(solo_dict_resources+max_dict_resources) #values + + result = dict(zip(combination_list_final, all_properties)) #created dict from the above keys and values + + + #print(result,candidates) + + map_ = INFO_TABLE(result) + pruned_combinations, pruned_resources = map_.eliminations() + #print(pruned_combinations, pruned_resources) + return pruned_combinations, pruned_resources + +if __name__ == "__main__": + final() + + + + + + + + + + \ No newline at end of file diff --git a/grouping/requirements.txt b/grouping/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..c7e0b59db7faecd869ab619255e81b348011045a --- /dev/null +++ b/grouping/requirements.txt @@ -0,0 +1,11 @@ +tensorflow==2.6.0 +keras==2.6.0 +scipy==1.7.2 +uvicorn==0.17.4 +fastapi==0.73.0 +pydantic==1.9.0 +stomp.py +slugify +gym==0.17.3 +numpy>=1.19.2 +requests==2.26.0 \ No newline at end of file diff --git a/grouping/test_application.py b/grouping/test_application.py new file mode 100644 index 0000000000000000000000000000000000000000..a7c9c2aa10f473df9cfb9af83fb0f4506ec03f58 --- /dev/null +++ b/grouping/test_application.py @@ -0,0 +1,166 @@ + +import random +from threading import Thread +#computing power + +import numpy as np + +class Database(): + def __init__(self): + self.state = None + self.response_time = 0 + self.throughput = 0 + self.properties = None + + def setProperties(self, properties): + self.properties = properties + def getState(self): + return self.state + def getProperties(self): + return self.state.properties + def getResponseTime(self): + return self.response_time + def getThroughput(self): + return self.throughput + def setState(self, state): + self.state = state + self.computeKPI() + def computeKPI(self): + if self.properties[1] > 0: + self.response_time = 2000 / (computing_power['CPU'] * self.properties[1] * self.properties[2] * self.properties[0]) + self.throughput = computing_power['CPU'] * self.properties[1] + if self.properties[3] == 'GPU': + self.response_time = 2000 / (computing_power['GPU'] * self.properties[2] * self.properties[0]) + self.throughput = 100 + if self.properties[3] == 'FPGA': + self.response_time = 2000 / (computing_power['FPGA'] * self.properties[2] * self.properties[0]) + self.throughput = 125 + if self.properties[4] == 'DOCKER': + self.response_time *= 1.2 + if self.properties[4] == 'SERVERLESS': + self.response_time *= 1.5 + + +class WebService(): + def __init__(self): + self.state = None + self.response_time = 0 + self.number_request = 0 + self.properties = None + self.load = None + def setLoad(self, load): + self.number_request = load * 100 + self.computeKPI() + def setProperties(self, properties): + self.properties = properties + def getState(self): + return self.state + def getProperties(self): + return self.state.properties + def getResponseTime(self): + return self.response_time + def getNumberRequest(self): + return self.number_request + + def computeKPI(self): + if self.number_request < 10: + self.response_time = 1 #ms + else: + if self.properties[1] > 0: + self.response_time = (self.number_request)*100 / (computing_power['CPU'] * self.properties[1] * self.properties[2] * self.properties[0]) + if self.properties[3] == 'GPU': + self.response_time = (self.number_request)*100 / (computing_power['GPU'] * self.properties[2] * self.properties[0]) + if self.properties[3] == 'FPGA': + self.response_time = (self.number_request)*100 / (computing_power['FPGA'] * self.properties[2] * self.properties[0]) + if self.properties[4] == 'DOCKER': + self.response_time *= 1.2 + if self.properties[4] == 'SERVERLESS': + self.response_time *= 1.5 +input_ = 0 + +class UtilitySimulator(): + def __init__(self, input_): + #self.consumer = Consumer(self.handleData, utility_generator_send_topic) + #self.publisher = Publisher() + self.index = 0 + self.db_response_time = 0 + self.ws_response_time = 0 + self.db_throughput = 0 + self.ws1_object = WebService() + self.ws2_object = WebService() + self.db_object = Database() + self.ws1_resource = set_resources(input_)[0] + self.ws2_resource = set_resources(input_)[2] + self.db_resource = set_resources(input_)[1] + self.performance = 0 + self.latency = 0 + self.load = 0 + self.range_performance=[] + super(UtilitySimulator, self).__init__() + + def makeState(self, payload): + pass + + def generateLoad(self, lower_bound, upper_bound): + self.load = random.randint(lower_bound,upper_bound) + + def computePerformance(self): + #print("current db res", self.db_resource) + #print("current ws res", self.ws1_resource) + #print("current ws res", self.ws2_resource) + self.ws1_object.setProperties(self.ws1_resource) + self.ws2_object.setProperties(self.ws2_resource) + self.db_object.setProperties(self.db_resource) + self.db_object.computeKPI() + self.ws1_object.setLoad(self.load) + self.ws2_object.setLoad(self.load) + + self.db_response_time = self.db_object.getResponseTime() + self.ws1_response_time = self.ws1_object.getResponseTime() + s_ws1_rt = self.scaleValue(self.ws1_response_time,0,1000) + self.ws2_response_time = self.ws2_object.getResponseTime() + s_ws2_rt = self.scaleValue(self.ws2_response_time,0,1000) + self.db_throughput = self.db_object.getThroughput() + s_db_rt = self.scaleValue(self.db_response_time,0,200) + self.performance = s_db_rt * 100 / (s_ws1_rt * s_ws2_rt * s_db_rt) + #print("Load = " , self.load,"Performance =>", self.performance) + + + def getPerfomance(self): + return self.performance + + def scaleValue(self, value, _min, _max): + return (value - _min) / (_max - _min) + + +if __name__ == "__main__": + act_0 = {'target': 'utility', 'sender_id': 'qFfkeNtN', 'combination': '[[1, 2], [3]]', 'variables': [{'component': 1, '_cores': 8100, '_memory': 1, '_instances': 1, '_variant': 'VM', '_hardware': 'CPU'}, {'component': 2, '_cores': 8100, '_memory': 1, '_instances': 1, '_variant': 'VM', '_hardware': 'CPU'}, {'component': 3, '_cores': 4100, '_memory': 2, '_instances': 1, '_variant': 'VM', '_hardware': 'CPU'}]} + act_1 = {'target': 'utility', 'sender_id': 'WU7uamf6', 'combination': '[[1], [2, 3]]', 'variables': [{'component': 1, '_cores': 8100, '_memory': 1, '_instances': 1, '_variant': 'VM', '_hardware': 'CPU'}, {'component': 2, '_cores': 4100, '_memory': 2, '_instances': 1, '_variant': 'VM', '_hardware': 'CPU'}, {'component': 3, '_cores': 4100, '_memory': 2, '_instances': 1, '_variant': 'VM', '_hardware': 'CPU'}]} + act_2 = {'target': 'utility', 'sender_id': 'loGekdb6', 'combination': '[[1, 2, 3]]', 'variables': [{'component': 1, '_cores': 8100, '_memory': 2, '_instances': 1, '_variant': 'VM', '_hardware': 'CPU'}, {'component': 2, '_cores': 8100, '_memory': 2, '_instances': 1, '_variant': 'VM', '_hardware': 'CPU'}, {'component': 3, '_cores': 8100, '_memory': 2, '_instances': 1, '_variant': 'VM', '_hardware': 'CPU'}]} + act_3 = {'target': 'utility', 'sender_id': 'K1oXNw0J', 'combination': '[[1], [3], [2]]', 'variables': [{'component': 1, '_cores': 8100, '_memory': 1, '_instances': 1, '_variant': 'VM', '_hardware': 'CPU'}, {'component': 2, '_cores': 4100, '_memory': 2, '_instances': 1, '_variant': 'VM', '_hardware': 'CPU'}, {'component': 3, '_cores': 1024, '_memory': 1, '_instances': 1, '_variant': 'VM', '_hardware': 'CPU'}]} + act_4 = {'target': 'utility', 'sender_id': '0D3AoHuF', 'combination': '[[1, 3], [2]]', 'variables': [{'component': 1, '_cores': 8100, '_memory': 2, '_instances': 1, '_variant': 'VM', '_hardware': 'CPU'}, {'component': 2, '_cores': 8100, '_memory': 2, '_instances': 1, '_variant': 'VM', '_hardware': 'CPU'}, {'component': 3, '_cores': 1024, '_memory': 1, '_instances': 1, '_variant': 'VM', '_hardware': 'CPU'}]} + # act_4 = {'target': 'utility', 'sender_id': 'n8Th3Zu9', 'combination': '[[2, 3, 4], [1]]', 'variables': [{'component': 1, '_cores': 4100, '_memory': 2, '_instances': 1, '_variant': 'VM', '_hardware': 'CPU'}, {'component': 2, '_cores': 4100, '_memory': 2, '_instances': 1, '_variant': 'VM', '_hardware': 'CPU'}, {'component': 3, '_cores': 4100, '_memory': 2, '_instances': 1, '_variant': 'VM', '_hardware': 'CPU'}, {'component': 4, '_cores': 8100, '_memory': 1, '_instances': 1, '_variant': 'VM', '_hardware': 'CPU'}]} +''' +load_=[] +for i in range(0,5): + if i == 0: + input_= act_0 + elif i==1: + input_=act_1 + elif i==2: + input_= act_2 + elif i==3: + input_=act_3 + else: + input_= act_4 + util = UtilitySimulator(input_) + for i in range(20): + util.generateLoad(lower_bound=151, upper_bound=200) + util.computePerformance() + application_performance = util.getPerfomance() + load_.append(util.performance) + +print("load 101-150") +print(min(load_)) +print(max(load_)) +''' \ No newline at end of file diff --git a/grouping/utility_interactor.py b/grouping/utility_interactor.py new file mode 100644 index 0000000000000000000000000000000000000000..e9ecfaa6358a57eefeabf6a3aef42ca15d759e04 --- /dev/null +++ b/grouping/utility_interactor.py @@ -0,0 +1,78 @@ +import json, time, os +from amq_client.adapter import Publisher, Consumer + +utility_generator_feedback_topic = os.environ.get("UTILITY_GENERATOR_FEEDBACK_TOPIC","solver_ug_reply") +utility_generator_send_topic = os.environ.get("UTILITY_GENERATOR_SENDER_TOPIC","solver_ug_request") +timeout_ug_waiting = int(os.environ.get("TIMEOUT_UG_WAITING","20")) +prefix_predicted_metric = os.environ.get("PREFIX_PREDICTED_METRIC","Predictions") + + +class ActiveMQUGCollector(): + def __init__(self): + self.collection = {} + self.max_list = 5000 + + def add(self, sender_id): + if len(list(self.collection.keys())) == self.max_list: + k = list(self.collection.keys())[0] + del self.collection[k] + self.collection[sender_id] = {"time": time.time(), "utility": None} + + def setUtility(self, sender_id, utility): + if sender_id in self.collection: + self.collection[sender_id]["utility"] = utility + self.collection[sender_id]["time"] = time.time() - self.collection[sender_id]["time"] + + def get(self, sender_id): + if sender_id in self.collection: + utility = self.collection[sender_id]["utility"] + duration = self.collection[sender_id]["time"] + return utility, duration + return None, None + + def remove(self, sender_id): + if sender_id in self.collection: + del self.collection[sender_id] + return True + return False + +class UtilityInteractor(): + def __init__(self): + self.sender_index = 0 + self.ug_collector = None + + self.consumer = Consumer(self.consumerHandler, "/topic/"+utility_generator_feedback_topic) + self.consumer.start() + self.publisher = Publisher() + self.publisher.start() + self.ug_collector = ActiveMQUGCollector() + + def consumerHandler(self, data, metadata): + _json = None + try: + _json = json.loads(data) + except Exception as e: + print("Cannot decode json content") + print(e) + self.ug_collector.setUtility(_json['sender_id'], _json['utility']) + + def getPerformance(self, state): + data = {"states": state['variables']} + sender_id = "CMPGP-{0}".format(self.sender_index) + data_to_send = {"sender_id": sender_id,"request": "get_performance", "variables": state['variables']} + self.publisher.setParameters(data_to_send, utility_generator_send_topic) + self.publisher.send() + _send_time = int(time.time()) + self.ug_collector.add(sender_id) + self.sender_index +=1 + utility, duration = self.ug_collector.get(sender_id) + if utility: + return state, utility + while utility == None: + if time.time() - _send_time >= timeout_ug_waiting: + return None, None + utility, duration = self.ug_collector.get(sender_id) + if utility != None: + self.ug_collector.remove(sender_id) + return state, utility + time.sleep(0.1) \ No newline at end of file diff --git a/morphemic-performance-model/.DS_Store b/morphemic-performance-model/.DS_Store index 2f9d9c1dd5794c72e36466d9c07b9c6834dde627..2b02149897ee35fa6f24bf33c9cfda42029f5ad0 100644 Binary files a/morphemic-performance-model/.DS_Store and b/morphemic-performance-model/.DS_Store differ diff --git a/morphemic-performance-model/data/README.md b/morphemic-performance-model/data/README.md deleted file mode 100644 index 17f01f9b0e64a7c7a53ae01cf91701f6d7c56229..0000000000000000000000000000000000000000 --- a/morphemic-performance-model/data/README.md +++ /dev/null @@ -1,12 +0,0 @@ -## 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/morphemic-performance-model/data/Time series prediction/connected_consumer/result_0.csv b/morphemic-performance-model/data/Time series prediction/connected_consumer/result_0.csv deleted file mode 100644 index 47851406e8219c9d78761f5c2b333dead9258527..0000000000000000000000000000000000000000 --- a/morphemic-performance-model/data/Time series prediction/connected_consumer/result_0.csv +++ /dev/null @@ -1,8642 +0,0 @@ -time,served_request,request_rate,response_time,performance,cpu_usage,memory -1602538627.766,2110,426,673.574009325832,0.6265087342404618,31.6,71798784 -1602538637.766,1845,314,132.2850770097438,2.774311421181451,25.8,65302528 -1602538647.766,2284,461,3.297176336031244,139.8166045783581,36.1,65859584 -1602538657.766,2354,469,3.2620622429082022,143.7740806508572,36.9,65826816 -1602538667.766,2301,463,3.245652939432966,142.652344116894,36.9,65777664 -1602538677.766,2297,461,3.401607962655253,135.52414183560091,37.2,65974272 -1602538687.766,2342,463,3.4215814735419112,135.31754353366816,36.4,65810432 -1602538697.766,2349,463,3.530493286022383,130.85990046464883,36.8,65851392 -1602538707.766,2285,462,3.319184263634212,139.19082621045902,36.9,65941504 -1602538717.766,2329,467,3.389207934964722,137.7903064554406,37.1,65703936 -1602538727.766,2165,483,31.02494574584256,13.956511110354587,36.3,69308416 -1602538737.766,2162,431,665.1086605444316,0.6495179293656798,31.8,71315456 -1602538747.766,2123,425,678.4113507982812,0.6249895428504882,31.5,71344128 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