Segment Routing Machine Learning Pipeline

Segment Routing

Segment Routing SR เทคโนโลยี Routing ใช้ Segments Labels กำหนดเส้นทาง SR-MPLS MPLS Labels SRv6 IPv6 Extension Headers Traffic Engineering ลดความซับซ้อน

Machine Learning Pipeline ระบบอัตโนมัติ ML Models Data Collection Feature Engineering Training Deployment Monitoring MLflow Kubeflow Scale

Segment Routing Architecture

# segment_routing.py — Segment Routing Architecture
from dataclasses import dataclass, field
from typing import List, Dict, Tuple

@dataclass
class Segment:
    sid: int  # Segment Identifier
    seg_type: str  # node, adjacency, service
    description: str
    node: str = ""

@dataclass
class SRPath:
    name: str
    segments: List[Segment]
    bandwidth: str
    latency: str
    policy: str

class SegmentRoutingNetwork:
    """Segment Routing Network"""

    def __init__(self):
        self.nodes: Dict[str, List[Segment]] = {}
        self.paths: List[SRPath] = []

    def add_node(self, name: str, segments: List[Segment]):
        self.nodes[name] = segments

    def add_path(self, path: SRPath):
        self.paths.append(path)

    def show_topology(self):
        print(f"\n{'='*55}")
        print(f"Segment Routing Network")
        print(f"{'='*55}")

        for node, segments in self.nodes.items():
            print(f"\n  Node: {node}")
            for seg in segments:
                print(f"    SID {seg.sid}: [{seg.seg_type}] {seg.description}")

    def show_paths(self):
        print(f"\n  SR Paths:")
        for path in self.paths:
            sids = " -> ".join([str(s.sid) for s in path.segments])
            print(f"\n    [{path.name}]")
            print(f"      Segments: {sids}")
            print(f"      BW: {path.bandwidth} | Latency: {path.latency}")
            print(f"      Policy: {path.policy}")

network = SegmentRoutingNetwork()

# Nodes and Segments
nodes = {
    "Router-A (Ingress)": [
        Segment(16001, "node", "Router-A Node SID", "Router-A"),
        Segment(24001, "adjacency", "Link to Router-B", "Router-A"),
    ],
    "Router-B (Transit)": [
        Segment(16002, "node", "Router-B Node SID", "Router-B"),
        Segment(24002, "adjacency", "Link to Router-C", "Router-B"),
        Segment(24003, "adjacency", "Link to Router-D", "Router-B"),
    ],
    "Router-C (Transit)": [
        Segment(16003, "node", "Router-C Node SID", "Router-C"),
    ],
    "Router-D (Egress)": [
        Segment(16004, "node", "Router-D Node SID", "Router-D"),
    ],
}

for node, segs in nodes.items():
    network.add_node(node, segs)

paths = [
    SRPath("Low-Latency Path", 
           [Segment(16001, "node", "A"), Segment(16002, "node", "B"), Segment(16004, "node", "D")],
           "1 Gbps", "5ms", "latency-optimized"),
    SRPath("High-BW Path",
           [Segment(16001, "node", "A"), Segment(16003, "node", "C"), Segment(16004, "node", "D")],
           "10 Gbps", "15ms", "bandwidth-optimized"),
]

for path in paths:
    network.add_path(path)

network.show_topology()
network.show_paths()

# SR-MPLS vs SRv6
comparison = {
    "Segment ID": {"SR-MPLS": "20-bit MPLS Label", "SRv6": "128-bit IPv6 Address"},
    "Infrastructure": {"SR-MPLS": "MPLS Network", "SRv6": "IPv6 Network"},
    "Overhead": {"SR-MPLS": "4 bytes/label", "SRv6": "16 bytes/SID"},
    "Flexibility": {"SR-MPLS": "ปานกลาง", "SRv6": "สูง (Network Programming)"},
    "Adoption": {"SR-MPLS": "แพร่หลาย", "SRv6": "เติบโตเร็ว"},
}

print(f"\n\nSR-MPLS vs SRv6:")
for feature, values in comparison.items():
    print(f"  {feature:<15} SR-MPLS: {values['SR-MPLS']:<25} SRv6: {values['SRv6']}")

ML-driven Network Optimization

# ml_network.py — ML Pipeline for Network Optimization
import numpy as np
from dataclasses import dataclass
from typing import List, Dict

@dataclass
class TrafficSample:
    timestamp: str
    src: str
    dst: str
    bandwidth_mbps: float
    latency_ms: float
    packet_loss: float
    path: str

class NetworkMLPipeline:
    """ML Pipeline สำหรับ Network Optimization"""

    def __init__(self):
        self.data: List[TrafficSample] = []

    def collect_data(self, samples: List[TrafficSample]):
        """1. Data Collection"""
        self.data.extend(samples)
        print(f"  Collected {len(samples)} samples (Total: {len(self.data)})")

    def feature_engineering(self) -> np.ndarray:
        """2. Feature Engineering"""
        features = []
        for sample in self.data:
            features.append([
                sample.bandwidth_mbps,
                sample.latency_ms,
                sample.packet_loss,
                1 if "low-latency" in sample.path else 0,
            ])
        X = np.array(features)
        print(f"  Features: {X.shape}")
        return X

    def train_model(self, X: np.ndarray):
        """3. Model Training"""
        # Production: sklearn RandomForest, XGBoost, or Neural Network
        print(f"  Training model on {X.shape[0]} samples...")
        print(f"  Model: RandomForest (Traffic Prediction)")
        print(f"  Features: bandwidth, latency, packet_loss, path_type")
        print(f"  Target: optimal_path (classification)")

    def predict_congestion(self, current_traffic: Dict) -> dict:
        """4. Predict Congestion"""
        # Simulated prediction
        risk = "HIGH" if current_traffic.get("utilization", 0) > 80 else "LOW"
        return {
            "congestion_risk": risk,
            "recommended_action": "reroute" if risk == "HIGH" else "none",
            "suggested_path": "high-bw-path" if risk == "HIGH" else "current",
            "confidence": 0.92,
        }

    def optimize_routing(self, predictions: dict) -> dict:
        """5. Optimize SR Policies"""
        if predictions["recommended_action"] == "reroute":
            return {
                "action": "UPDATE_SR_POLICY",
                "old_path": "16001 -> 16002 -> 16004",
                "new_path": "16001 -> 16003 -> 16004",
                "reason": f"Congestion risk: {predictions['congestion_risk']}",
            }
        return {"action": "NO_CHANGE"}

# ตัวอย่าง
pipeline = NetworkMLPipeline()

samples = [
    TrafficSample("2024-01-15 10:00", "A", "D", 800, 5, 0.01, "low-latency"),
    TrafficSample("2024-01-15 10:05", "A", "D", 950, 8, 0.02, "low-latency"),
    TrafficSample("2024-01-15 10:10", "A", "D", 1200, 15, 0.05, "high-bw"),
]

print("ML Network Pipeline:")
pipeline.collect_data(samples)
X = pipeline.feature_engineering()
pipeline.train_model(X)

prediction = pipeline.predict_congestion({"utilization": 85})
print(f"\n  Prediction: {prediction}")

action = pipeline.optimize_routing(prediction)
print(f"  Action: {action}")

MLOps Pipeline

# mlops_pipeline.py — MLOps for Network ML
mlops_stages = {
    "1. Data Collection": {
        "sources": "NetFlow, sFlow, SNMP, Telemetry (gNMI)",
        "storage": "Time-series DB (InfluxDB, Prometheus)",
        "frequency": "ทุก 5 วินาที",
    },
    "2. Feature Engineering": {
        "features": "Bandwidth Util, Latency, Jitter, Packet Loss, Time-of-day",
        "tools": "Pandas, Spark, dbt",
        "output": "Feature Store (Feast, Redis)",
    },
    "3. Model Training": {
        "algorithms": "RandomForest, XGBoost, LSTM, Transformer",
        "tools": "MLflow, Kubeflow, Dagster",
        "schedule": "Retrain ทุกสัปดาห์",
    },
    "4. Model Serving": {
        "serving": "Triton, TensorFlow Serving, FastAPI",
        "latency": "< 10ms (Real-time Decision)",
        "deployment": "Canary Release, A/B Testing",
    },
    "5. Monitoring": {
        "metrics": "Model Accuracy, Drift Detection, Prediction Latency",
        "tools": "Evidently AI, Grafana, Prometheus",
        "alerts": "Slack, PagerDuty เมื่อ Accuracy ลดลง",
    },
}

print("MLOps Pipeline for Network Optimization:")
for stage, info in mlops_stages.items():
    print(f"\n  [{stage}]")
    for key, value in info.items():
        print(f"    {key}: {value}")

# Use Cases
use_cases = {
    "Congestion Prediction": "ทำนาย Congestion 15 นาทีล่วงหน้า เปลี่ยนเส้นทางก่อน",
    "Anomaly Detection": "ตรวจจับ Traffic ผิดปกติ DDoS, Loop, Misconfiguration",
    "Bandwidth Optimization": "จัดสรร Bandwidth ตาม Demand ที่ ML ทำนาย",
    "Path Selection": "เลือก SR Path ที่ดีที่สุดตาม Real-time Conditions",
    "Failure Prediction": "ทำนาย Link Failure จาก Telemetry Data",
    "SLA Assurance": "รับประกัน SLA ด้วย Proactive Rerouting",
}

print(f"\n\nML + SR Use Cases:")
for case, desc in use_cases.items():
    print(f"  {case}: {desc}")

Best Practices

• SRv6: ใช้ SRv6 สำหรับ Network ใหม่ ยืดหยุ่นกว่า SR-MPLS
• Telemetry: ใช้ gNMI Streaming Telemetry เก็บข้อมูล Real-time
• Feature Store: ใช้ Feature Store เก็บ Features ใช้ซ้ำได้
• Low Latency: Model Serving ต้อง < 10ms สำหรับ Real-time Routing
• Canary: Deploy ML Model แบบ Canary ทดสอบก่อนใช้จริง
• Monitoring: ติดตาม Model Drift เพื่อ Retrain ทันเวลา

การนำไปใช้งานจริงในองค์กร

สำหรับองค์กรขนาดกลางถึงใหญ่ แนะนำให้ใช้หลัก Three-Tier Architecture คือ Core Layer ที่เป็นแกนกลางของระบบ Distribution Layer ที่ทำหน้าที่กระจาย Traffic และ Access Layer ที่เชื่อมต่อกับผู้ใช้โดยตรง การแบ่ง Layer ชัดเจนช่วยให้การ Troubleshoot ง่ายขึ้นและสามารถ Scale ระบบได้ตามความต้องการ

เรื่อง Network Security ก็สำคัญไม่แพ้กัน ควรติดตั้ง Next-Generation Firewall ที่สามารถ Deep Packet Inspection ได้ ใช้ Network Segmentation แยก VLAN สำหรับแต่ละแผนก ติดตั้ง IDS/IPS เพื่อตรวจจับการโจมตี และทำ Regular Security Audit อย่างน้อยปีละ 2 ครั้ง

สรุป

Segment Routing SR Segments Labels SR-MPLS SRv6 Traffic Engineering ML Pipeline วิเคราะห์ Traffic Predict Congestion Anomaly Detection Bandwidth Optimization gNMI Telemetry MLflow Kubeflow Model Serving Real-time