Azure Private Multi-Access Edge Compute (MEC)

Azure Private Multi-Access Edge Compute (MEC) brings Azure services to the network edge, enabling ultra-low latency applications for enterprises. Combined with private 5G, it creates powerful edge computing solutions.

Understanding Private MEC

Architecture Overview

# Private MEC deployment architecture
components:
  azure_stack_edge:
    - Compute platform for edge workloads
    - Kubernetes for container orchestration
    - GPU support for AI/ML inference

  private_5g_core:
    - Local packet core processing
    - SIM management and authentication
    - Quality of Service enforcement

  azure_services:
    - IoT Hub for device management
    - Azure Monitor for observability
    - Azure Arc for management plane

Deploying Private MEC

using Azure.ResourceManager;
using Azure.ResourceManager.Resources;

public class PrivateMECDeploymentService
{
    private readonly ArmClient _armClient;

    public async Task DeployPrivateMECAsync(MECDeploymentConfig config)
    {
        // Step 1: Deploy Azure Stack Edge
        var stackEdge = await DeployStackEdgeAsync(config);

        // Step 2: Configure Kubernetes on Stack Edge
        await ConfigureKubernetesAsync(stackEdge, config);

        // Step 3: Deploy Private 5G Core
        await DeployPrivate5GCoreAsync(config);

        // Step 4: Deploy edge workloads
        await DeployEdgeWorkloadsAsync(config);

        // Step 5: Configure monitoring
        await ConfigureMonitoringAsync(config);
    }

    private async Task<AzureStackEdgeDevice> DeployStackEdgeAsync(MECDeploymentConfig config)
    {
        var deviceData = new DataBoxEdgeDeviceData(config.Location)
        {
            Sku = new SkuInfo("EdgeP_High"),
            Tags =
            {
                ["Environment"] = config.Environment,
                ["Site"] = config.SiteName
            }
        };

        var subscription = await _armClient.GetDefaultSubscriptionAsync();
        var rg = await subscription.GetResourceGroupAsync(config.ResourceGroup);

        var result = await rg.Value.GetDataBoxEdgeDevices()
            .CreateOrUpdateAsync(WaitUntil.Completed, config.DeviceName, deviceData);

        return result.Value;
    }

    private async Task DeployEdgeWorkloadsAsync(MECDeploymentConfig config)
    {
        // Deploy containerized workloads
        var workloads = new List<EdgeWorkload>
        {
            new EdgeWorkload
            {
                Name = "video-analytics",
                Image = "mcr.microsoft.com/azure-video-analyzer:latest",
                Resources = new ResourceRequirements
                {
                    Cpu = "2",
                    Memory = "4Gi",
                    Gpu = "1"
                }
            },
            new EdgeWorkload
            {
                Name = "data-processor",
                Image = config.DataProcessorImage,
                Resources = new ResourceRequirements
                {
                    Cpu = "1",
                    Memory = "2Gi"
                }
            }
        };

        foreach (var workload in workloads)
        {
            await DeployWorkloadAsync(config, workload);
        }
    }
}

public class MECDeploymentConfig
{
    public string ResourceGroup { get; set; }
    public string Location { get; set; }
    public string DeviceName { get; set; }
    public string SiteName { get; set; }
    public string Environment { get; set; }
    public string DataProcessorImage { get; set; }
}

Edge Application Example

# Real-time video analytics at the edge
import cv2
import numpy as np
from azure.iot.device import IoTHubModuleClient
import onnxruntime as ort
import json
import asyncio

class EdgeVideoAnalytics:
    def __init__(self):
        self.client = IoTHubModuleClient.create_from_edge_environment()
        self.model = ort.InferenceSession("/models/object-detection.onnx")
        self.confidence_threshold = 0.7

    async def process_video_stream(self, stream_url):
        cap = cv2.VideoCapture(stream_url)

        while True:
            ret, frame = cap.read()
            if not ret:
                break

            # Preprocess frame
            input_tensor = self.preprocess(frame)

            # Run inference
            detections = self.detect_objects(input_tensor)

            # Process detections
            for detection in detections:
                if detection['confidence'] > self.confidence_threshold:
                    await self.handle_detection(detection, frame)

            # Limit processing rate
            await asyncio.sleep(0.033)  # ~30 FPS

        cap.release()

    def preprocess(self, frame):
        # Resize and normalize
        resized = cv2.resize(frame, (640, 480))
        normalized = resized.astype(np.float32) / 255.0
        return np.expand_dims(normalized.transpose(2, 0, 1), 0)

    def detect_objects(self, input_tensor):
        input_name = self.model.get_inputs()[0].name
        outputs = self.model.run(None, {input_name: input_tensor})

        detections = []
        for i, output in enumerate(outputs[0]):
            if output[4] > self.confidence_threshold:
                detections.append({
                    'class_id': int(output[5]),
                    'confidence': float(output[4]),
                    'bbox': output[:4].tolist()
                })

        return detections

    async def handle_detection(self, detection, frame):
        # Send high-priority events to cloud
        if detection['class_id'] in [0, 1, 2]:  # Person, vehicle, etc.
            event = {
                'type': 'detection',
                'class_id': detection['class_id'],
                'confidence': detection['confidence'],
                'timestamp': datetime.utcnow().isoformat()
            }

            await self.client.send_message_to_output(
                json.dumps(event),
                "detectionOutput"
            )

async def main():
    analytics = EdgeVideoAnalytics()
    await analytics.client.connect()

    # Process multiple video streams
    streams = [
        "rtsp://camera1.local/stream",
        "rtsp://camera2.local/stream"
    ]

    tasks = [analytics.process_video_stream(url) for url in streams]
    await asyncio.gather(*tasks)

if __name__ == "__main__":
    asyncio.run(main())

Low-Latency API Gateway

// Edge API gateway for local processing
public class EdgeApiGateway
{
    private readonly IEdgeCache _cache;
    private readonly IEdgeProcessor _processor;
    private readonly ICloudForwarder _forwarder;

    public async Task<ApiResponse> HandleRequestAsync(ApiRequest request)
    {
        // Check local cache first
        var cachedResponse = await _cache.GetAsync(request.CacheKey);
        if (cachedResponse != null)
        {
            return cachedResponse;
        }

        // Process locally if possible
        if (CanProcessLocally(request))
        {
            var response = await _processor.ProcessAsync(request);
            await _cache.SetAsync(request.CacheKey, response, TimeSpan.FromMinutes(5));
            return response;
        }

        // Forward to cloud
        var cloudResponse = await _forwarder.ForwardAsync(request);
        return cloudResponse;
    }

    private bool CanProcessLocally(ApiRequest request)
    {
        // Determine if request can be handled at edge
        return request.Type switch
        {
            RequestType.RealTimeData => true,
            RequestType.LocalCompute => true,
            RequestType.CachedQuery => true,
            RequestType.CloudOnly => false,
            _ => false
        };
    }
}

Monitoring and Observability

// MEC monitoring queries
// Edge device health
EdgeDeviceMetrics
| where TimeGenerated > ago(1h)
| summarize
    AvgCPU = avg(CPUUtilization),
    AvgMemory = avg(MemoryUtilization),
    AvgGPU = avg(GPUUtilization)
    by DeviceId, bin(TimeGenerated, 5m)
| render timechart

// Application latency at edge
EdgeApplicationMetrics
| where TimeGenerated > ago(1h)
| where MetricName == "ProcessingLatency"
| summarize
    P50 = percentile(MetricValue, 50),
    P95 = percentile(MetricValue, 95),
    P99 = percentile(MetricValue, 99)
    by ApplicationName, bin(TimeGenerated, 5m)
| render timechart

// Data flow between edge and cloud
EdgeDataFlow
| where TimeGenerated > ago(24h)
| summarize
    DataSentToCloud = sum(BytesSent),
    DataProcessedLocally = sum(BytesProcessed)
    by bin(TimeGenerated, 1h)
| extend LocalProcessingRatio = DataProcessedLocally * 100.0 / (DataSentToCloud + DataProcessedLocally)
| render timechart

Use Cases

1. Manufacturing - Real-time quality inspection
2. Retail - In-store analytics and inventory
3. Healthcare - Medical imaging at point of care
4. Sports venues - Fan engagement applications
5. Smart cities - Traffic and safety monitoring

Azure Private MEC delivers cloud capabilities at the edge with enterprise-grade security and management.