using System.Net.Quic;
using System.Text.Json;
using PubSub.Models;
public class SubscriberService {
private readonly string _serverAddress = "127.0.0.1";
private readonly int _port = 4433;
public async Task FetchMessages() {
using var connection = await QuicConnection.ConnectAsync(
new IPEndPoint(System.Net.IPAddress.Parse(_serverAddress), _port),
new QuicConnectionOptions());
var stream = await connection.OpenOutboundStreamAsync();
await stream.WriteAsync(System.Text.Encoding.UTF8.GetBytes("Fetch Pending Messages"));
var buffer = new byte[1024];
var result = await stream.ReadAsync(buffer);
var messageJson = System.Text.Encoding.UTF8.GetString(buffer, 0, result);
var message = JsonSerializer.Deserialize(messageJson);
Console.WriteLine($"Retrieved Message: {message.Header.MessageId}");
}
}
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
data = pd.read_csv("pubsub_metrics.csv")
X = data[["cpu_usage", "memory_usage", "network_bandwidth",
"message_throughput", "subscriber_count"]]
y = data["scale_up_needed"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = RandomForestRegressor(n_estimators=100)
model.fit(X_train, y_train)
future_load = model.predict([[85, 8, 400, 2000, 1500]])
print("Scaling Decision:", "Scale Up" if future_load[0] > 0.7 else "Stable")
public class CarbonMonitor {
private const double CARBON_INTENSITY = 0.4; // kg CO2 per kWh
public async Task CalculateCarbonFootprint(double cpuPower,
double memoryPower,
double networkPower) {
double totalPower = cpuPower + memoryPower + (networkPower / 1000); // Convert bandwidth to kWh
double carbonEmissions = totalPower * CARBON_INTENSITY;
Console.WriteLine($"Real-Time Carbon Emissions: {carbonEmissions} kg COâ‚‚");
return carbonEmissions;
}
}