Harnessing AI for Web Hosting: Automating Server Management and Optimization

sudo apt install ansible docker.io -y
sudo systemctl start docker
sudo systemctl enable docker

sudo apt install prometheus grafana -y
sudo systemctl start prometheus
sudo systemctl enable prometheus
sudo systemctl start grafana-server
sudo systemctl enable grafana-server

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor

# Load data
data = pd.read_csv('server_metrics.csv')
X = data.drop('cpu_usage', axis=1)
y = data['cpu_usage']

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train model
model = RandomForestRegressor()
model.fit(X_train, y_train)

# Predict
predictions = model.predict(X_test)

crontab -e
# Add the following line to run the script every hour
0 * * * * /usr/bin/python3 /path/to/predictive_maintenance.py

sudo snap install kubectl --classic
sudo snap install microk8s --classic
sudo microk8s.start
sudo microk8s.enable dns dashboard

apiVersion: apps/v1kind: Deployment
metadata:
name: ai-model-deployment
spec:
replicas: 2
selector:
matchLabels:
app: ai-model
template:
metadata:
labels:
app: ai-model
spec:
containers:
- name: ai-model
image: your_dockerhub_username/ai-model:latest
ports:
- containerPort: 80

kubectl autoscale deployment ai-model-deployment --cpu-percent=50 --min=1 --max=10

global: resolve_timeout: 5m
route:
receiver: 'slack-notifications'
receivers:
- name: 'slack-notifications'
slack_configs:
- api_url: 'https://hooks.slack.com/services/T00000000/B00000000/XXXXXXXXXXXXXXXXXXXXXXXX'
channel: '#alerts'
    send_resolved: true