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datacreation.py: This file is helpful to generate sample dataset (in csv) for CPU utlilization with other parameters of Cloud Computing Nodes. Parameters are "Timestamp", "Node_ID", "Application", "CPU_Utilization", "Memory_Usage", "Workload_Intensity", "Network_Traffic", "Storage_Utilization", "Number_of_Requests", "CPU_Temperature", "Power_Consumption", "Clock_Speed", "Cache_Size", "Number_of_Cores" Output will be file named generated_csv.csv
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noderesponsibilityforfailure.py
This program select some features ('CPU_Utilization', 'Memory_Usage', 'Power_Consumption','CPU_Temperature', 'Workload_Intensity', 'Network_Traffic', 'Number_of_Requests', 'Cache_Size') with some threshold values ('CPU_Utilization': 80, 'Memory_Usage': 90, 'Power_Consumption': 300, 'CPU_Temperature': 75, 'Workload_Intensity': 'High', # For categorical feature, 'Network_Traffic': '200 Mbps', # For categorical feature 'Number_of_Requests': 1500, 'Cache_Size': 10,) and save nodes data which are liable for failure in csv file named failed_nodes_data.csv. It also plot the data for each monitoring feature.
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failednodechecking.py This program Standardize the monitoring features by calculating z-scores, calculate overall score for each node based on z-score, and rank them.
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metrices.py This program use Metrics that are more relevant for evaluating unsupervised anomaly detection techniques which include:
a. Silhouette Score: Measures how similar an object is to its own cluster (cohesion) compared to other clusters (separation). A higher silhouette score indicates better separation and cohesive clusters. b. Davies-Bouldin Index: Measures the average similarity between each cluster and its most similar cluster. Lower values indicate better clustering.
These are used for KMeans, Hierarchical and for DBSCAN
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isolationforest.py This program uses isolation forest for detecting outliers
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allsteps.py This program is extension of isolation forest and saving outliers responsible for max cpu utilization is same named csv file.
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completecode.py it is the basic program for visulalizing the data with dimensiionality reduction