env and remove unnecessary files
Change-Id: I678feeec5e0c0481633fe93d7666d3ad7aae3647
This commit is contained in:
Gregory Koronakos
parent
c504f94705
commit
ac6e1c1300
9
cfsb-backend/.env.prod
Normal file
9
cfsb-backend/.env.prod
Normal file
@ -0,0 +1,9 @@
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NEBULOUS_BROKER_URL=158.37.63.86
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NEBULOUS_BROKER_PORT=31609
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NEBULOUS_BROKER_USERNAME=admin
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NEBULOUS_BROKER_PASSWORD=admin
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POSTGRES_DB_HOST=localhost
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POSTGRES_DB_NAME=fog_broker
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POSTGRES_DB_PORT=5432
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POSTGRES_DB_USER=dbuser
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POSTGRES_DB_PASS=pass123
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@ -124,7 +124,7 @@ def perform_evaluation(data_table, relative_wr_data, immediate_wr_data, node_nam
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num_of_dmus = len(next(iter(data_table.values())))
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Cols_No = len(criteria_list)
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DEA_Scores = []
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epsilon = 0.00001 # Lower bound of the variables
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epsilon = 0.00000 # Lower bound of the variables
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# Iterating over each DMU to Perform DEA
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for dmu_index in range(num_of_dmus):
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@ -340,145 +340,67 @@ def create_data_table(selected_criteria, extracted_data, field_mapping):
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return data_table
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# Used to Append "Score" and "Rank" for each node in SAL's response JSON
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# def append_evaluation_results(SALs_JSON_filename, raw_evaluation_results):
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# # Load the JSON content from the file
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# with open(SALs_JSON_filename, 'r') as file:
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# SALs_JSON = json.load(file)
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#
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# # Check if raw_evaluation_results is a string and parse it, otherwise use it directly
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# if isinstance(raw_evaluation_results, str):
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# try:
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# evaluation_results = json.loads(raw_evaluation_results)
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# except json.JSONDecodeError as e:
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# print(f"An error occurred while decoding the JSON data: {e}")
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# return
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# else:
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# evaluation_results = raw_evaluation_results
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#
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# eval_results_dict = {result['Id']: (result['DEA Score'], result['Rank']) for result in evaluation_results}
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#
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# for node in SALs_JSON:
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# node_id = node.get("id")
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# if node_id in eval_results_dict:
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# score, rank = eval_results_dict[node_id]
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# node["Score"] = score
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# node["Rank"] = rank
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#
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# return SALs_JSON
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# # # Write the updated SALs_JSON to a new JSON file
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# # with open('updated_SALs_JSON.json', 'w') as file:
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# # json.dump(SALs_JSON, file, indent=4)
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# def append_evaluation_results(sal_reply_body, scores_and_ranks):
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# # Create a dictionary mapping Ids to scores and ranks
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# eval_results_dict = {result['Id']: (result['DEA Score'], result['Rank'])
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# for result in scores_and_ranks}
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#
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# # Iterate over each node in sal_reply_body and append Score and Rank
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# for node in sal_reply_body:
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# node_id = node.get('id') # Assuming the ID is directly under the node
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# if node_id in eval_results_dict:
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# score, rank = eval_results_dict[node_id]
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# node["Score"] = score
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# node["Rank"] = rank
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#
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# return sal_reply_body
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import random
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# def append_evaluation_results(sal_reply_body, scores_and_ranks):
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# # Check if sal_reply_body is a string and convert it to a Python object
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# if isinstance(sal_reply_body, str):
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# sal_reply_body = json.loads(sal_reply_body)
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#
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# # Create a dictionary mapping Ids to scores and ranks
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# eval_results_dict = {result['Id']: (result['DEA Score'], result['Rank'])
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# for result in scores_and_ranks}
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# if scores_and_ranks:
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# # Create a dictionary mapping Ids to scores and ranks
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# eval_results_dict = {result['Id']: (result['DEA Score'], result['Rank'])
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# for result in scores_and_ranks}
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#
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# # Iterate over each node in sal_reply_body and append Score and Rank
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# for node in sal_reply_body:
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# node_id = node.get('id') # Assuming the ID is directly under the node
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# if node_id in eval_results_dict:
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# score, rank = eval_results_dict[node_id]
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# node["score"] = score
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# # Iterate over each node in sal_reply_body and append Score and Rank
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# for node in sal_reply_body:
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# node_id = node.get('id') # Assuming the ID is directly under the node
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# if node_id in eval_results_dict:
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# score, rank = eval_results_dict[node_id]
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# node["score"] = score
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# node["rank"] = rank
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# else:
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# # If scores_and_ranks is empty
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# for index, node in enumerate(sal_reply_body):
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# if index == 0:
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# # First node gets a score of 1 and rank of 1
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# node["score"] = 1
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# node["rank"] = 1
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# else:
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# # Assign random scores between 0.33 and 0.93 to the rest
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# node["score"] = random.uniform(0.33, 0.93)
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#
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# # Sort nodes by score in descending order to calculate ranks
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# sorted_nodes = sorted(sal_reply_body[1:], key=lambda x: x["score"], reverse=True)
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#
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# # Assign ranks based on sorted order, starting from 2 since the first node is ranked 1
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# for rank, node in enumerate(sorted_nodes, start=2):
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# node["rank"] = rank
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#
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# # Combine the first node with the rest
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# sal_reply_body = [sal_reply_body[0]] + sorted_nodes
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#
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# return sal_reply_body
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# Used to parse Patini's JSON
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def parse_device_info_from_file(file_path):
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with open(file_path, 'r') as file:
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json_data = json.load(file)
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device_names = []
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device_info = {
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'id': json_data['_id'],
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'name': json_data['name'], # Save the device name
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'deviceInfo': json_data['deviceInfo'],
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'creationDate': json_data['creationDate'],
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'lastUpdateDate': json_data['lastUpdateDate'],
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'status': json_data['status'],
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'metrics': {
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'cpu': json_data['metrics']['metrics']['cpu'],
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'uptime': json_data['metrics']['metrics']['uptime'],
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'disk': json_data['metrics']['metrics']['disk'],
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'ram': json_data['metrics']['metrics']['ram']
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}
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}
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# Example of converting and handling ISODate strings, adjust accordingly
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device_info['creationDate'] = datetime.fromisoformat(device_info['creationDate'].replace("ISODate('", "").replace("')", ""))
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device_info['lastUpdateDate'] = datetime.fromisoformat(device_info['lastUpdateDate'].replace("ISODate('", "").replace("')", ""))
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device_info['creationDate'] = device_info['creationDate'].isoformat()
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device_info['lastUpdateDate'] = device_info['lastUpdateDate'].isoformat()
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# Update the global device_names list
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device_names.append({'id': device_info['id'], 'name': device_info['name']})
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return device_names, device_info
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import json
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import random
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def append_evaluation_results(sal_reply_body, scores_and_ranks):
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# Check if sal_reply_body is a string and convert it to a Python object
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if isinstance(sal_reply_body, str):
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sal_reply_body = json.loads(sal_reply_body)
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if scores_and_ranks:
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# Create a dictionary mapping Ids to scores and ranks
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eval_results_dict = {result['Id']: (result['DEA Score'], result['Rank'])
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for result in scores_and_ranks}
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# Create a dictionary mapping Ids to scores and ranks
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eval_results_dict = {result['Id']: (result['DEA Score'], result['Rank'])
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for result in scores_and_ranks}
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# Iterate over each node in sal_reply_body and append Score and Rank
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for node in sal_reply_body:
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node_id = node.get('id') # Assuming the ID is directly under the node
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if node_id in eval_results_dict:
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score, rank = eval_results_dict[node_id]
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node["score"] = score
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node["rank"] = rank
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else:
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# If scores_and_ranks is empty
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for index, node in enumerate(sal_reply_body):
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if index == 0:
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# First node gets a score of 1 and rank of 1
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node["score"] = 1
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node["rank"] = 1
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else:
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# Assign random scores between 0.33 and 0.93 to the rest
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node["score"] = random.uniform(0.33, 0.93)
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# Sort nodes by score in descending order to calculate ranks
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sorted_nodes = sorted(sal_reply_body[1:], key=lambda x: x["score"], reverse=True)
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# Assign ranks based on sorted order, starting from 2 since the first node is ranked 1
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for rank, node in enumerate(sorted_nodes, start=2):
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# Iterate over each node in sal_reply_body and append Score and Rank
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for node in sal_reply_body:
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node_id = node.get('id') # Assuming the ID is directly under the node
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if node_id in eval_results_dict:
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score, rank = eval_results_dict[node_id]
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node["score"] = score
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node["rank"] = rank
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# Combine the first node with the rest
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sal_reply_body = [sal_reply_body[0]] + sorted_nodes
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return sal_reply_body
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@ -495,7 +417,6 @@ def append_evaluation_results(sal_reply_body, scores_and_ranks):
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# Example usage
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# extracted_data, NUMBER_OF_FOG_NODES, node_names = extract_node_candidate_data('dummy_data_node_candidates.json')
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# print(NUMBER_OF_FOG_NODES)
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@ -36,8 +36,9 @@ class SyncedHandler(Handler):
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# Save the correlation_id (We do not have it from the app_side)
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uuid.uuid4().hex.encode("utf-8") # for Correlation id
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# Optimizer_correlation_id = '88334290cad34ad9b21eb468a9f8ff11' # dummy correlation_id
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correlation_id_optimizer = message.correlation_id
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if not correlation_id_optimizer:
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correlation_id_optimizer = '88334290cad34ad9b21eb468a9f8ff11' # dummy correlation_id
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# logging.info(f"Optimizer_correlation_id {message.correlation_id}")
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print("Optimizer Correlation Id: ", correlation_id_optimizer)
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@ -89,85 +90,6 @@ class SyncedHandler(Handler):
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# }
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# ]
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# "jobIdForByon": "null",
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# "jobIdForEDGE": "null"
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# body_sent_from_optimizer =[
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# {
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# "type": "NodeTypeRequirement",
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# "nodeTypes": ["IAAS"]
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# },
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# {
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# "type": "AttributeRequirement",
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# "requirementClass": "image",
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# "requirementAttribute": "operatingSystem.family",
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# "requirementOperator": "IN","value":"UBUNTU"},
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# {
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# "type":"AttributeRequirement",
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# "requirementClass":"hardware",
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# "requirementAttribute":"ram",
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# "requirementOperator":"GEQ",
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# "value":"4096"
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# },
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# {"type":"AttributeRequirement","requirementClass":"hardware","requirementAttribute":"cores",
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# "requirementOperator":"GEQ","value":"4"}
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# ]
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# body_sent_from_optimizer = [
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# {
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# "type": "NodeTypeRequirement",
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# "nodeTypes": ["IAAS"]
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# }
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# ]
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# "nodeTypes": ["EDGE"]
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# "nodeTypes": ["IAAS", "PAAS", "FAAS", "BYON", "EDGE", "SIMULATION"]
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# "jobIdForEDGE": "FCRnewLight0"
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# "jobIdForByon":"dummy-app-id",
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# "jobIdForEDGE":"dummy-app-id"
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# body_sent_from_optimizer = [
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# {
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# "type": "NodeTypeRequirement",
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# "nodeTypes": ["IAAS"]
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# },
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# {
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# "type": "AttributeRequirement",
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# "requirementClass": "hardware",
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# "requirementAttribute": "cores",
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# "requirementOperator": "EQ",
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# "value": "2"
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# },
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# {
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# "type": "AttributeRequirement",
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# "requirementClass": "hardware",
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# "requirementAttribute": "ram",
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# "requirementOperator": "EQ",
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# "value": "4096"
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# }
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# ]
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# body_sent_from_optimizer =[
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# {
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# "type": "NodeTypeRequirement",
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# "nodeTypes": ["IAAS"],
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# "jobIdForByon": "dummy-app-id",
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# "jobIdForEDGE": "dummy-app-id"
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# },
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# {
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# "type": "AttributeRequirement",
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# "requirementClass": "hardware",
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# "requirementAttribute": "cores",
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# "requirementOperator": "EQ",
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# "value": "2"
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# },
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# {
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# "type": "AttributeRequirement",
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# "requirementClass": "hardware",
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# "requirementAttribute": "ram",
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# "requirementOperator": "EQ",
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# "value": "4096"
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# }
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# ]
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# logging.info(body_sent_from_optimizer)
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# print("Extracted body from Optimizer Message:", body_sent_from_optimizer)
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@ -216,17 +138,16 @@ class SyncedHandler(Handler):
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# sal_reply_body = json.load(file)
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# print("SAL's Reply from JSON File:", sal_reply_body)
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try:
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# Parse the JSON string to a Python object
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nodes_data = json.loads(sal_body)
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total_nodes = len(nodes_data) # Get the total number of nodes
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# Check if more than 58 nodes exist
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if total_nodes > 58:
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if total_nodes > 400:
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print("More than 58 nodes exist. Only the first 51 nodes will be processed.")
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# Filter to only include the first 51 nodes and convert back to JSON string
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sal_reply_body = json.dumps(nodes_data[:15])
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sal_reply_body = json.dumps(nodes_data[:400])
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else:
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print(f"Total {total_nodes} nodes found. Processing all nodes.")
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# Keep sal_reply_body as is since it's already a JSON string
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@ -341,7 +262,7 @@ def start_exn_connector_in_background():
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addressOPTtriggering = 'eu.nebulouscloud.cfsb.get_node_candidates'
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addressSendToOPT = 'eu.nebulouscloud.cfsb.get_node_candidates.reply'
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connector = EXN('ui', url="localhost", port=5672, username="admin", password="admin",
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connector = EXN('ui', url=os.getenv('NEBULOUS_BROKER_URL'), port=os.getenv('NEBULOUS_BROKER_PORT'), username=os.getenv('NEBULOUS_BROKER_USERNAME'), password=os.getenv('NEBULOUS_BROKER_PASSWORD'),
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handler=Bootstrap(),
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publishers=[
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SyncedPublisher('SAL-GET', addressSAL_GET, True, True),
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@ -1,8 +1,11 @@
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from app_factory import create_app
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from dotenv import load_dotenv
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from activemq import start_exn_connector_in_background
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from activemqOLD import start_exn_connector_in_background1
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from app_factory import create_app # Import your Flask app factory
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load_dotenv()
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app = create_app()
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# Start the EXN connector in the background
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start_exn_connector_in_background()
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@ -1,88 +0,0 @@
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from flask import Flask, request, jsonify, render_template, redirect, url_for
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from flask_cors import CORS, cross_origin
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import json
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app = Flask(__name__, template_folder='templates')
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#app = Flask(__name__)
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CORS(app)
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#CORS(app, resources={r"/get_hierarchical_category_list": {"origins": "http://localhost:8080"}})
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hierarchy_data = hierarchical_list = [
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{
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"name": "Level 1 - Item 1",
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"children": [
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{
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"name": "Level 2 - Item 1.1",
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"children": [
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{"name": "Level 3 - Item 1.1.1"},
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{"name": "Level 3 - Item 1.1.2"},
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],
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},
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{
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"name": "Level 2 - Item 1.2",
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"children": [
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{"name": "Level 3 - Item 1.2.1"},
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{"name": "Level 3 - Item 1.2.2"},
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],
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},
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],
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},
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{
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"name": "Level 1 - Item 2",
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"children": [
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{
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"name": "Level 2 - Item 2.1",
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"children": [
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{"name": "Level 3 - Item 2.1.1"},
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{"name": "Level 3 - Item 2.1.2"},
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],
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},
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{
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"name": "Level 2 - Item 2.2",
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"children": [
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{"name": "Level 3 - Item 2.2.1"},
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{"name": "Level 3 - Item 2.2.2"},
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],
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},
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],
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},
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# Add more items as needed
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]
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# print(json.dumps(hierarchical_list, indent=2))
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'''
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def traverse_hierarchy(node, selected_items, required_count):
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if node['name'] in selected_items:
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required_count -= 1
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for child in node.get('children', []):
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required_count = traverse_hierarchy(child, selected_items, required_count)
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return required_count
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'''
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@app.route('/get_hierarchical_category_list')
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def get_hierarchical_category_list():
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return jsonify(hierarchy_data)
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@app.route('/process_selected_items', methods=['POST'])
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def process_selected_items():
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try:
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data = request.get_json()
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selected_items = data.get('selectedItems', [])
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# Print selected items for debugging
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print("Selected Items:", selected_items)
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# Continue processing the selected items
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# For example, you can print or process the selected items here
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# Redirect to the show_selected_items route
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return redirect(url_for('show_selected_items', items=','.join(selected_items)))
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except Exception as e:
|
||||
return jsonify({'success': False, 'error': str(e)}), 500
|
||||
|
||||
@app.route('/show_selected_items/<items>')
|
||||
@cross_origin()
|
||||
def show_selected_items(items):
|
||||
return render_template('selected_items.html', items=items.split(','))
|
||||
|
||||
if __name__ == '__main__':
|
||||
app.run(debug=True)
|
||||
@ -1,9 +1,11 @@
|
||||
import os
|
||||
|
||||
import psycopg2
|
||||
|
||||
|
||||
def db_open():
|
||||
# Connect to the database
|
||||
conn = psycopg2.connect(database="fog_broker", user="dbuser", password="pass123", host="localhost", port="5432")
|
||||
conn = psycopg2.connect(database=os.getenv('POSTGRES_DB_NAME'), user=os.getenv('POSTGRES_DB_USER'), password=os.getenv('POSTGRES_DB_PASS'), host=os.getenv('POSTGRES_DB_HOST'), port=os.getenv('POSTGRES_DB_PORT'))
|
||||
# create a cursor
|
||||
cur = conn.cursor()
|
||||
return conn, cur
|
||||
|
||||
@ -6,6 +6,8 @@ services:
|
||||
dockerfile: Dockerfile
|
||||
ports:
|
||||
- "8001:8001"
|
||||
env_file:
|
||||
- .env.prod
|
||||
depends_on:
|
||||
- db
|
||||
db:
|
||||
|
||||
@ -1,80 +0,0 @@
|
||||
{
|
||||
"_id": "b4ce322c-698a-43b9-a889-bf0da2a4dcb9",
|
||||
"os": "LINUX",
|
||||
"name": "Test VM #0001",
|
||||
"owner": "admin",
|
||||
"ipAddress": "10.10.0.6",
|
||||
"location": {
|
||||
"name": "laptop",
|
||||
"latitude": 12.345,
|
||||
"longitude": 56.789
|
||||
},
|
||||
"username": "ubuntu",
|
||||
"password": [
|
||||
"u",
|
||||
"b",
|
||||
"u",
|
||||
"n",
|
||||
"t",
|
||||
"u"
|
||||
],
|
||||
"publicKey": [],
|
||||
"deviceInfo": {
|
||||
"CPU_SOCKETS": "1",
|
||||
"CPU_CORES": "10",
|
||||
"CPU_PROCESSORS": "20",
|
||||
"RAM_TOTAL_KB": "16218480",
|
||||
"RAM_AVAILABLE_KB": "13366788",
|
||||
"RAM_FREE_KB": "10943372",
|
||||
"RAM_USED_KB": "5275108",
|
||||
"RAM_UTILIZATION": "32.5253",
|
||||
"DISK_TOTAL_KB": "1055762868",
|
||||
"DISK_FREE_KB": "976527612",
|
||||
"DISK_USED_KB": "79235256",
|
||||
"DISK_UTILIZATION": "7.50502",
|
||||
"OS_ARCHITECTURE": "x86_64",
|
||||
"OS_KERNEL": "Linux",
|
||||
"OS_KERNEL_RELEASE": "5.15.133.1-microsoft-standard-WSL2"
|
||||
},
|
||||
"requestId": "eb6441fc-613a-482e-ba94-b16db57ecd36",
|
||||
"creationDate": "2024-01-15T13:23:40.602Z",
|
||||
"lastUpdateDate": "2024-01-15T14:32:43.485Z",
|
||||
"status": "HEALTHY",
|
||||
"nodeReference": "40ed1989-49ba-4496-a5c5-3d8ca1a18972",
|
||||
"messages": [],
|
||||
"statusUpdate": {
|
||||
"ipAddress": "10.10.0.6",
|
||||
"clientId": "VM-LINUX-TEST-VM-0001-Test VM #0001-DEFAULT-10.10.0.6-_",
|
||||
"state": "REGISTERED",
|
||||
"stateLastUpdate": "2024-01-15T13:23:47.463Z",
|
||||
"reference": "40ed1989-49ba-4496-a5c5-3d8ca1a18972",
|
||||
"errors": []
|
||||
},
|
||||
"metrics": {
|
||||
"ipAddress": "10.10.0.6",
|
||||
"clientId": "VM-LINUX-TEST-VM-0001-Test VM",
|
||||
"timestamp": "2024-01-15T14:32:33.467Z",
|
||||
"metrics": {
|
||||
"count-total-events-failures": 0,
|
||||
"count-total-events-text": 0,
|
||||
"tx": 0,
|
||||
"count-total-events-other": 0,
|
||||
"count-event-forwards-success": 0,
|
||||
"count-event-forwards-failure": 0,
|
||||
"rx": 0,
|
||||
"count-total-events": 0,
|
||||
"cpu": 0.6,
|
||||
"uptime": 10742,
|
||||
"count-event-local-publish-failure": 0,
|
||||
"count-total-events-object": 0,
|
||||
"disk": 2.48262,
|
||||
"count-event-local-publish-success": 0,
|
||||
"updatetime": 1705318391,
|
||||
"currdatetime": 1705329133,
|
||||
"ram": 23.7719
|
||||
},
|
||||
"latestEvents": []
|
||||
},
|
||||
"retries": 0,
|
||||
"_class": "eu.nebulous.resource.discovery.monitor.model.Device"
|
||||
}
|
||||
@ -1,44 +0,0 @@
|
||||
https://158.39.75.54/projects/nebulous-collaboration-hub/wiki/accessing-the-demo-activemq-broker
|
||||
https://openproject.nebulouscloud.eu/projects/nebulous-collaboration-hub/wiki/accessing-the-nebulous-dev-k8s
|
||||
|
||||
activemq
|
||||
terminal στο Documents/projects/koronakos/nebulous_certs
|
||||
export KUBECONFIG=kubeconfig.yaml
|
||||
kubectl -n nebulous port-forward service/nebulous-activemq 5672:5672 8161:8161 61616:61616
|
||||
|
||||
OPTIMIZER waits for a reply on eu.nebulouscloud.cfsb.get_node_candidates.reply,
|
||||
the reply body will be the list of node candidates as you got it from SAL, but augmented with ranking information.
|
||||
The optimiser will use the highest-ranked node candidates.
|
||||
|
||||
το app name from message.subject
|
||||
το παραπάνω αντί για το "application_id": "app_id" στη γραμμη 74
|
||||
|
||||
passwords
|
||||
gregkoron
|
||||
Olympi@kos789
|
||||
|
||||
-------------
|
||||
SAL
|
||||
SAL POSTMAN: https://158.39.75.54/projects/nebulous-collaboration-hub/wiki/deployment-manager-sal-1
|
||||
|
||||
Port Forwarding to enable POSTMAN for SAL:
|
||||
kubectl port-forward -n nebulous service/sal 8080:8080
|
||||
|
||||
Use this in POSTMAN Request: https://localhost:8080/sal/pagateway/connect
|
||||
---
|
||||
kubectl port-forward -n nebulous service/sal1 8080:8080
|
||||
------If proplems It works with this-----
|
||||
kubectl -n nebulous get pod | findstr sal
|
||||
kubectl -n nebulous delete pod sal-all-6b8bbc54cf-hxsj6
|
||||
-----------
|
||||
|
||||
----------FLUX--------------
|
||||
kubectl scale -n flux-system --replicas=0 deploy/sal-all-flux
|
||||
|
||||
kubectl scale -n flux-system --replicas=1 deploy/sal-all-flux
|
||||
|
||||
kubectl -n flux-system port-forward service/sal-flux <YOUR_FAV_PORT>:8080
|
||||
|
||||
kubectl scale -n flux-system --replicas=0 deploy/sal-all-flux
|
||||
|
||||
kubectl scale -n flux-system --replicas=1 deploy/sal-all-flux
|
||||
File diff suppressed because it is too large
Load Diff
Loading…
x
Reference in New Issue
Block a user