Edge Computing

Overview

Edge computing distributes computational workloads from centralized data centers to locations closer to data sources. This architectural pattern emerged from the need to process massive volumes of data generated by IoT devices, mobile applications, and distributed sensors without overwhelming network bandwidth or introducing unacceptable latency.

Traditional cloud computing routes all data to centralized servers for processing. A temperature sensor in a factory sends readings to a distant data center, which analyzes the data and returns instructions. This round trip introduces latency measured in hundreds of milliseconds. Edge computing places processing capacity at the factory itself, reducing response time to single-digit milliseconds.

The edge refers to any computing resource between data sources and cloud data centers. This includes on-premises servers, cellular base stations, routers with computing capability, and specialized edge devices. The edge layer forms a distributed processing fabric that handles time-sensitive operations locally while forwarding aggregated results to the cloud.

Edge computing addresses three fundamental limitations of centralized architectures:

Latency: Physical distance creates unavoidable delays. A request traveling from San Francisco to a Virginia data center and back requires at minimum 70 milliseconds due to the speed of light. Applications requiring sub-10ms response times must process data locally.

Bandwidth: Network connections have finite capacity. Streaming high-definition video from thousands of security cameras to the cloud consumes enormous bandwidth. Processing video at the edge to detect events and transmitting only alerts reduces bandwidth requirements by orders of magnitude.

Resilience: Centralized systems create single points of failure. Edge computing enables applications to continue operating during network outages by processing critical functions locally. A manufacturing line can maintain operation even when disconnected from the cloud.

# Edge processing decision based on latency requirements
class EdgeDecisionEngine
  LATENCY_THRESHOLDS = {
    critical: 10,      # milliseconds
    important: 50,
    standard: 200
  }
  
  def process_location(data_size_mb, priority)
    estimated_latency = calculate_roundtrip_latency(data_size_mb)
    threshold = LATENCY_THRESHOLDS[priority]
    
    if estimated_latency > threshold
      :edge
    else
      :cloud
    end
  end
  
  private
  
  def calculate_roundtrip_latency(data_size_mb)
    # Simplified calculation: network latency + transfer time
    network_latency = 50  # ms base latency
    bandwidth_mbps = 100
    transfer_time = (data_size_mb * 8 / bandwidth_mbps) * 1000
    network_latency + transfer_time
  end
end

engine = EdgeDecisionEngine.new
engine.process_location(0.1, :critical)  # => :edge
engine.process_location(0.1, :standard)  # => :cloud

Key Principles

Edge computing operates on the principle of distributed data processing across a hierarchical architecture. The architecture typically consists of three tiers: device tier, edge tier, and cloud tier. Each tier handles specific processing responsibilities based on latency requirements, computational capacity, and data sensitivity.

Device Tier contains sensors, actuators, and embedded systems that generate or consume data. These devices often have limited processing power and focus on data collection and basic preprocessing. A temperature sensor might perform analog-to-digital conversion and basic filtering before transmitting readings.

Edge Tier provides localized computing resources with greater capacity than devices but less than cloud data centers. Edge nodes process time-sensitive data, aggregate information from multiple devices, and filter data before cloud transmission. An edge server in a retail store might process video feeds from cameras, detect customer movements, and generate heatmaps without sending raw video to the cloud.

Cloud Tier offers massive computational resources for batch processing, machine learning model training, and long-term storage. The cloud receives aggregated data from edge nodes, performs complex analytics, and distributes updated models or configurations back to the edge.

Data flow in edge architectures follows a bidirectional pattern. Real-time data flows from devices to edge nodes for immediate processing. Edge nodes send aggregated results and alerts to the cloud. The cloud pushes configuration updates, machine learning models, and firmware updates down to edge nodes and devices.

Processing Distribution: Edge computing requires careful allocation of workloads across tiers. Time-critical operations execute at the edge, while resource-intensive analytics run in the cloud. An autonomous vehicle processes sensor fusion and collision detection at the edge, but uploads driving data to the cloud for improving machine learning models.

Data Locality: Processing data near its source reduces transmission requirements and improves privacy. Personal health data from wearable devices can be analyzed on a user's smartphone without sending raw data to the cloud. Only aggregated health metrics and alerts require cloud transmission.

Eventual Consistency: Edge systems operate with relaxed consistency guarantees. Edge nodes may process data using slightly outdated models or configurations. Synchronization with the cloud occurs periodically rather than continuously. A content delivery network edge cache may serve slightly stale content until the next sync interval.

Autonomy: Edge nodes must function independently during network partitions. Local decision-making capabilities allow operations to continue when cloud connectivity is unavailable. Manufacturing equipment processes quality control checks using locally cached models even when disconnected from the cloud.

# Edge-cloud data synchronization pattern
class EdgeCloudSync
  def initialize(edge_storage, cloud_client)
    @edge_storage = edge_storage
    @cloud_client = cloud_client
    @sync_interval = 300  # seconds
    @last_sync = Time.now
  end
  
  def process_data(data)
    # Process locally first
    result = local_process(data)
    @edge_storage.save(result)
    
    # Queue for cloud sync if needed
    if should_sync?
      sync_to_cloud
    end
    
    result
  end
  
  private
  
  def local_process(data)
    # Edge processing using cached model
    model = @edge_storage.get_model
    model.predict(data)
  end
  
  def should_sync?
    Time.now - @last_sync > @sync_interval ||
      @edge_storage.pending_count > 100
  end
  
  def sync_to_cloud
    pending = @edge_storage.get_pending
    @cloud_client.batch_upload(pending)
    @edge_storage.clear_pending
    @last_sync = Time.now
  rescue NetworkError => e
    # Continue operating with stale data
    @edge_storage.log_sync_failure(e)
  end
end

Implementation Approaches

Edge computing implementations vary based on deployment model, processing distribution, and infrastructure requirements. Organizations select approaches based on latency needs, data volumes, security requirements, and operational complexity.

Cloudlet Model deploys small-scale data centers at the network edge. Cloudlets function as intermediate processing layers between devices and cloud data centers. This approach works well for applications requiring moderate computing resources with reduced latency. A cloudlet at a shopping mall processes data from hundreds of stores, providing faster response than distant cloud servers while consolidating infrastructure for cost efficiency.

Infrastructure management in the cloudlet model resembles traditional data center operations but at smaller scale. Organizations provision virtual machines or containers on edge servers, deploy applications through standard orchestration tools, and maintain separate management plane for edge resources.

Mobile Edge Computing (MEC) integrates computing resources into cellular network infrastructure. Processing occurs at or near cellular base stations, enabling ultra-low latency for mobile applications. MEC deployments support augmented reality applications, real-time gaming, and vehicle-to-everything communication where milliseconds matter.

Cellular operators control MEC infrastructure, creating dependency on telecom partnerships. Applications must adapt to heterogeneous edge environments as users move between cell towers. Connection handoff and state migration become critical considerations.

Fog Computing extends cloud infrastructure down to the network edge through hierarchical layers of processing. Fog computing emphasizes the continuum from cloud to edge rather than discrete tiers. Data processing occurs at multiple levels based on requirements, with each layer having specific responsibilities.

A fog deployment might include plant-level servers, factory-level data centers, regional processing centers, and centralized cloud resources. Data flows through this hierarchy, with processing occurring at the lowest level capable of handling each workload.

On-Premises Edge deploys dedicated edge infrastructure at facility locations. Organizations maintain complete control over hardware, software, and data. This approach suits scenarios with strict data residency requirements or unreliable cloud connectivity. Healthcare facilities process patient data on-premises to meet regulatory requirements while backing up to the cloud.

# Edge deployment configuration for different models
class EdgeDeploymentConfig
  def self.cloudlet_config
    {
      tier: :cloudlet,
      resources: {
        cpu_cores: 32,
        memory_gb: 128,
        storage_gb: 1000
      },
      services: [:compute, :storage, :messaging],
      sync_interval: 60,
      failure_mode: :cache_and_continue
    }
  end
  
  def self.on_premises_config
    {
      tier: :on_premises,
      resources: {
        cpu_cores: 64,
        memory_gb: 256,
        storage_gb: 5000
      },
      services: [:compute, :storage, :messaging, :database],
      sync_interval: 300,
      failure_mode: :full_autonomy
    }
  end
  
  def self.mobile_edge_config
    {
      tier: :mobile_edge,
      resources: {
        cpu_cores: 16,
        memory_gb: 64,
        storage_gb: 500
      },
      services: [:compute, :cache],
      sync_interval: 30,
      failure_mode: :fallback_to_cloud
    }
  end
end

# Workload placement decision
class WorkloadPlacement
  def place_workload(workload)
    case workload[:latency_requirement]
    when 0..10
      deploy_to_device(workload)
    when 11..50
      deploy_to_edge(workload)
    when 51..200
      deploy_to_cloudlet(workload)
    else
      deploy_to_cloud(workload)
    end
  end
  
  private
  
  def deploy_to_edge(workload)
    edge_nodes = discover_edge_nodes(workload[:location])
    selected_node = edge_nodes.min_by { |node| node.current_load }
    selected_node.deploy(workload)
  end
end

Container-Based Edge deploys applications as containers orchestrated across edge nodes. Kubernetes and similar platforms manage container lifecycle, networking, and scaling. This approach provides consistency between cloud and edge deployments, simplifying operations. Container-based edge deployments face challenges with resource-constrained environments and unreliable connectivity.

Serverless Edge executes functions at edge locations in response to events. Developers write stateless functions that process data without managing infrastructure. Serverless platforms handle scaling, deployment, and resource allocation. Content delivery networks increasingly offer serverless execution at edge points of presence.

Design Considerations

Edge computing introduces architectural complexity that requires careful evaluation of trade-offs. The decision to implement edge computing depends on application requirements, operational capabilities, and cost constraints.

Latency Requirements drive edge computing adoption. Applications with hard real-time constraints require edge processing. Industrial control systems, autonomous vehicles, and augmented reality applications cannot tolerate cloud round-trip latency. Conversely, batch processing, analytics, and applications with latency tolerance of seconds gain minimal benefit from edge deployment.

Measure actual latency requirements rather than assuming edge computing is necessary. A retail analytics application might perceive 100ms response time as instantaneous while adding significant infrastructure complexity for edge deployment. Profile application behavior under various latency conditions to determine true requirements.

Data Volume and Bandwidth considerations affect edge architecture decisions. Applications generating massive data volumes that require minimal processing should filter at the edge. Video surveillance systems generating multiple gigabits per second cannot practically transmit all data to the cloud. Edge processing extracts meaningful events from video streams, reducing cloud transmission to kilobits per second.

Calculate total bandwidth requirements including peak loads. A factory with 10,000 sensors each transmitting 1KB per second generates 10MB/s baseline, but sensor bursts during equipment failures might spike to 100MB/s. Edge aggregation reduces baseline transmission while handling bursts locally.

Operational Complexity increases with edge deployments. Each edge location requires provisioning, monitoring, updating, and troubleshooting. Cloud-centric architectures benefit from consolidated operations, while edge computing distributes operational burden across potentially thousands of locations. Organizations must evaluate whether operational teams can support distributed infrastructure.

Edge deployments create version skew challenges. Edge nodes may run different software versions during rolling updates. Applications must handle version compatibility between edge and cloud components. Synchronizing configuration changes across distributed edge infrastructure requires careful orchestration.

Cost Analysis compares edge infrastructure costs against cloud alternatives. Edge computing requires upfront capital investment in hardware and ongoing maintenance costs. Cloud computing shifts costs to operational expenses with pay-per-use pricing. Calculate total cost of ownership including hardware depreciation, power consumption, cooling, network connectivity, and operational labor.

Edge computing may reduce cloud costs by processing data locally. Transmitting raw data to the cloud for processing incurs bandwidth and compute charges. Edge processing reduces both, potentially offsetting edge infrastructure costs. Model costs under various scenarios considering growth projections.

Security and Privacy considerations influence edge architecture. Processing sensitive data at the edge reduces exposure during transmission and limits cloud storage of private information. Medical devices process patient data locally, transmitting only anonymized aggregates. Edge processing supports regulatory compliance for data residency requirements.

Edge security introduces challenges. Distributed edge nodes increase attack surface compared to centralized cloud infrastructure. Physical security of edge devices may be limited. Edge nodes require secure boot, encrypted storage, and regular security updates despite unreliable connectivity.

# Cost comparison model for edge vs cloud
class EdgeCloudCostModel
  def initialize(data_volume_gb_per_day, processing_hours_per_day)
    @data_volume = data_volume_gb_per_day
    @processing_hours = processing_hours_per_day
  end
  
  def cloud_monthly_cost
    bandwidth_cost + cloud_compute_cost + cloud_storage_cost
  end
  
  def edge_monthly_cost
    edge_hardware_amortized + power_and_cooling + 
      maintenance + minimal_cloud_cost
  end
  
  private
  
  def bandwidth_cost
    # $0.09 per GB
    @data_volume * 30 * 0.09
  end
  
  def cloud_compute_cost
    # $0.05 per hour for processing instance
    @processing_hours * 30 * 0.05
  end
  
  def cloud_storage_cost
    # $0.023 per GB per month
    @data_volume * 30 * 0.023
  end
  
  def edge_hardware_amortized
    # $5000 edge server / 36 months
    5000.0 / 36
  end
  
  def power_and_cooling
    # 500W server, $0.12 per kWh
    0.5 * 24 * 30 * 0.12
  end
  
  def maintenance
    100  # monthly maintenance estimate
  end
  
  def minimal_cloud_cost
    # Only aggregated data goes to cloud
    (@data_volume * 0.01) * 30 * 0.09
  end
  
  def breakeven_data_volume
    # Calculate where edge becomes cost-effective
    (edge_monthly_cost - minimal_cloud_cost) / (0.09 + 0.023)
  end
end

model = EdgeCloudCostModel.new(100, 24)
puts model.cloud_monthly_cost  # => ~$416
puts model.edge_monthly_cost   # => ~$286

Failure Modes differ between edge and cloud architectures. Cloud systems fail by becoming unavailable, while edge systems may operate in degraded mode with stale data. Design edge applications to function autonomously during cloud disconnection. Cache machine learning models, configuration data, and reference information locally. Implement conflict resolution for data modified during network partitions.

Development and Testing complexity increases with edge deployments. Developers must test applications under various network conditions including intermittent connectivity, high latency, and bandwidth limitations. Simulating edge environments requires specialized tooling. Testing distributed synchronization logic and conflict resolution adds to development time.

Ruby Implementation

Ruby applications run at edge locations through various deployment approaches. While Ruby may not match compiled languages for performance, its developer productivity and rich ecosystem make it viable for many edge computing scenarios.

Edge Web Services deploy Ruby applications as HTTP APIs at edge locations. Sinatra or Roda provide lightweight frameworks suitable for resource-constrained edge environments. An edge API processes local requests while synchronizing state with cloud services.

# Lightweight edge API using Roda
require 'roda'
require 'json'

class EdgeAPI < Roda
  plugin :json
  
  route do |r|
    @edge_storage = EdgeStorage.instance
    
    r.on 'api' do
      r.on 'process' do
        r.post do
          data = JSON.parse(r.body.read)
          
          # Process at edge
          result = process_locally(data)
          @edge_storage.cache(result)
          
          # Queue for cloud sync
          SyncQueue.enqueue(result)
          
          { status: 'processed', result: result }
        end
      end
      
      r.on 'status' do
        r.get do
          {
            edge_id: ENV['EDGE_NODE_ID'],
            last_sync: @edge_storage.last_sync_time,
            pending_items: SyncQueue.size,
            health: system_health
          }
        end
      end
    end
  end
  
  def process_locally(data)
    model = @edge_storage.get_model
    model.predict(data['features'])
  end
  
  def system_health
    memory_available = `free -m | grep Mem | awk '{print $7}'`.to_i
    disk_available = `df -m / | tail -1 | awk '{print $4}'`.to_i
    
    {
      memory_mb: memory_available,
      disk_mb: disk_available,
      uptime: `uptime -p`.strip
    }
  end
end

Message Processing handles event streams from IoT devices. Ruby processes incoming sensor data, applies filtering rules, and forwards significant events to cloud services. The ruby-mqtt gem enables MQTT protocol support for IoT communication.

require 'mqtt'
require 'json'

class EdgeMessageProcessor
  def initialize(broker_host, edge_id)
    @broker_host = broker_host
    @edge_id = edge_id
    @client = MQTT::Client.connect(broker_host)
    @cloud_sync = CloudSyncClient.new
  end
  
  def start
    @client.subscribe('sensors/#')
    
    @client.get do |topic, message|
      process_message(topic, message)
    end
  end
  
  private
  
  def process_message(topic, message)
    data = JSON.parse(message)
    sensor_id = topic.split('/').last
    
    # Edge processing: anomaly detection
    if anomaly_detected?(data)
      alert = create_alert(sensor_id, data)
      
      # Immediate local action
      trigger_local_action(alert)
      
      # Send to cloud
      @cloud_sync.send_alert(alert)
    end
    
    # Aggregate for periodic cloud sync
    AggregationBuffer.add(sensor_id, data)
    
    # Sync aggregated data every 5 minutes
    if AggregationBuffer.should_flush?
      @cloud_sync.send_batch(AggregationBuffer.flush)
    end
  rescue JSON::ParserError => e
    log_error("Invalid message format: #{e.message}")
  end
  
  def anomaly_detected?(data)
    reading = data['value']
    threshold = ConfigCache.get_threshold(data['sensor_type'])
    reading > threshold
  end
  
  def trigger_local_action(alert)
    # Execute immediate response without cloud dependency
    case alert[:severity]
    when :critical
      LocalActuator.emergency_stop
    when :warning
      LocalActuator.adjust_parameters(alert[:recommended_action])
    end
  end
end

# Start edge processor
processor = EdgeMessageProcessor.new('localhost', ENV['EDGE_ID'])
processor.start

Background Jobs process queued tasks at edge locations. Sidekiq or similar job processing systems handle asynchronous workloads. Edge nodes queue work items for processing during network outages, then synchronize results when connectivity restores.

require 'sidekiq'

class EdgeDataProcessor
  include Sidekiq::Worker
  sidekiq_options queue: :edge, retry: 5
  
  def perform(data_id)
    data = EdgeStorage.fetch(data_id)
    
    # Process locally
    result = analyze_data(data)
    EdgeStorage.save_result(data_id, result)
    
    # Attempt cloud sync
    begin
      CloudAPI.upload_result(data_id, result)
      EdgeStorage.mark_synced(data_id)
    rescue NetworkError
      # Retry handled by Sidekiq
      raise
    end
  end
  
  private
  
  def analyze_data(data)
    # CPU-intensive analysis runs at edge
    features = extract_features(data)
    model = ModelCache.current_model
    prediction = model.predict(features)
    
    {
      timestamp: Time.now.to_i,
      prediction: prediction,
      confidence: calculate_confidence(features, prediction)
    }
  end
end

State Management requires careful handling in edge applications. Ruby applications cache frequently accessed data locally while maintaining synchronization with cloud state. Redis or SQLite provide local persistence at edge nodes.

require 'redis'
require 'sqlite3'

class EdgeStateManager
  def initialize
    @redis = Redis.new
    @db = SQLite3::Database.new('edge_state.db')
    setup_database
  end
  
  def get(key)
    # Try Redis cache first
    cached = @redis.get(key)
    return JSON.parse(cached) if cached
    
    # Fallback to local database
    row = @db.get_first_row('SELECT value FROM state WHERE key = ?', key)
    return nil unless row
    
    value = JSON.parse(row[0])
    @redis.setex(key, 3600, row[0])  # Cache for 1 hour
    value
  end
  
  def set(key, value)
    json_value = JSON.generate(value)
    
    # Update cache
    @redis.setex(key, 3600, json_value)
    
    # Persist to database
    @db.execute(
      'INSERT OR REPLACE INTO state (key, value, updated_at) VALUES (?, ?, ?)',
      [key, json_value, Time.now.to_i]
    )
    
    # Queue for cloud sync
    SyncQueue.enqueue({ action: 'update', key: key, value: value })
  end
  
  def sync_with_cloud
    # Pull updates from cloud
    cloud_updates = CloudAPI.get_updates_since(@last_sync)
    cloud_updates.each do |update|
      set(update['key'], update['value'])
    end
    
    # Push local changes to cloud
    local_changes = get_unsynced_changes
    CloudAPI.push_updates(local_changes)
    
    @last_sync = Time.now.to_i
  rescue NetworkError => e
    log_error("Sync failed: #{e.message}")
    # Continue with cached data
  end
  
  private
  
  def setup_database
    @db.execute <<-SQL
      CREATE TABLE IF NOT EXISTS state (
        key TEXT PRIMARY KEY,
        value TEXT NOT NULL,
        updated_at INTEGER NOT NULL
      )
    SQL
  end
end

Model Deployment distributes machine learning models to edge locations. Ruby applications load serialized models and execute inference locally. Models require periodic updates from cloud training pipelines.

require 'onnxruntime'

class EdgeMLModel
  def initialize(model_path)
    @model = OnnxRuntime::Model.new(model_path)
    @version = extract_version(model_path)
  end
  
  def predict(features)
    input = { 'input' => [features] }
    output = @model.predict(input)
    output['output'][0]
  end
  
  def self.update_model(new_model_url)
    # Download new model
    new_model_path = download_model(new_model_url)
    
    # Validate model
    test_model = new(new_model_path)
    raise 'Model validation failed' unless validate_model(test_model)
    
    # Atomic swap
    File.rename(new_model_path, current_model_path)
    
    # Reload in-memory model
    @@current_model = new(current_model_path)
  end
  
  def self.current_model
    @@current_model ||= new(current_model_path)
  end
  
  private
  
  def extract_version(path)
    File.basename(path).match(/v(\d+)/)[1].to_i
  end
  
  def self.current_model_path
    'models/current_model.onnx'
  end
end

Tools & Ecosystem

Edge computing platforms provide infrastructure for deploying and managing distributed applications. These platforms handle provisioning, orchestration, monitoring, and synchronization across edge nodes.

AWS IoT Greengrass extends AWS services to edge devices. Greengrass Core software runs on edge hardware, enabling local execution of Lambda functions, machine learning inference, and message routing. Applications developed for AWS Lambda can run at the edge with minimal modifications. Greengrass synchronizes state between edge and cloud, manages deployment of updated functions, and provides local messaging between devices.

Ruby applications integrate with Greengrass through Lambda functions or long-lived components. The aws-sdk gem enables interaction with Greengrass services and local shadow state.

Azure IoT Edge deploys containerized workloads to edge devices. Edge modules run as Docker containers orchestrated by the IoT Edge runtime. Azure provides modules for stream analytics, machine learning, and custom code. Ruby applications package as containers and deploy through Azure IoT Hub. The runtime handles module lifecycle, networking, and cloud synchronization.

Google Distributed Cloud Edge extends Google Cloud to edge locations. Applications deploy using Kubernetes on edge infrastructure. Anthos manages clusters across cloud and edge locations with consistent tooling. Ruby applications package as containers and deploy through standard Kubernetes manifests.

EdgeX Foundry provides an open-source framework for edge computing. EdgeX defines microservice architecture for device connectivity, data processing, and application logic. The framework supports multiple languages including Ruby for custom services. EdgeX handles device abstraction, data transformation, and rule execution.

K3s offers a lightweight Kubernetes distribution optimized for edge deployments. K3s reduces memory footprint and binary size while maintaining Kubernetes API compatibility. Edge deployments run K3s on resource-constrained hardware, deploying applications as containers. Ruby applications deploy to K3s clusters using standard Kubernetes tooling.

# Deploying Ruby application to K3s edge cluster
# kubernetes_deploy.rb

require 'kubeclient'

class EdgeDeployment
  def initialize(kubeconfig_path)
    config = Kubeclient::Config.read(kubeconfig_path)
    @client = Kubeclient::Client.new(
      config.context.api_endpoint,
      'v1',
      ssl_options: config.context.ssl_options,
      auth_options: config.context.auth_options
    )
  end
  
  def deploy_edge_service(name, image, replicas: 1)
    deployment = {
      metadata: {
        name: name,
        namespace: 'edge-services'
      },
      spec: {
        replicas: replicas,
        selector: {
          matchLabels: { app: name }
        },
        template: {
          metadata: {
            labels: { app: name }
          },
          spec: {
            containers: [{
              name: name,
              image: image,
              ports: [{ containerPort: 8080 }],
              env: [
                { name: 'EDGE_NODE_ID', valueFrom: { fieldRef: { fieldPath: 'spec.nodeName' } } }
              ],
              resources: {
                limits: { memory: '256Mi', cpu: '500m' },
                requests: { memory: '128Mi', cpu: '250m' }
              }
            }]
          }
        }
      }
    }
    
    @client.create_deployment(deployment)
  end
end

MQTT Brokers handle messaging between edge devices and applications. Eclipse Mosquitto and HiveMQ provide MQTT broker implementations for edge environments. Ruby applications use ruby-mqtt gem to publish and subscribe to topics. Edge deployments often run local MQTT brokers to enable device communication during cloud disconnection.

Time Series Databases store sensor data at edge locations. InfluxDB and TimescaleDB handle high-volume time series writes with efficient storage. Ruby applications query time series data for local analytics and dashboards.

Container Registries distribute application images to edge nodes. Harbor and Docker Registry host container images accessible from edge locations. Edge deployments cache frequently used images locally to reduce deployment time and bandwidth consumption.

Real-World Applications

Edge computing enables specific use cases where centralized cloud processing fails to meet requirements. Production deployments demonstrate edge computing value across industries.

Manufacturing Quality Control processes sensor data from production lines at edge locations. Vision systems capture product images at high frame rates. Edge processing runs computer vision models to detect defects in real-time. Only defective products and summary statistics transmit to cloud storage. This approach reduces bandwidth from multiple gigabits per second to kilobits per second while enabling immediate rejection of defective products.

A beverage bottling facility deploys edge servers at production lines. Cameras capture images of bottles at 120 fps. Edge servers run neural networks to detect underfilled bottles, damaged labels, and foreign objects. The system processes 50,000 bottles per hour, generating alerts within 50ms of defect detection. Cloud systems receive hourly quality metrics and defect images for long-term analysis.

Retail Analytics analyzes customer behavior in physical stores using edge computing. Cameras throughout stores feed video to edge servers. Computer vision models detect customer paths, dwell times, and product interactions. Processing video at the edge protects customer privacy by transmitting only anonymized analytics rather than raw video.

A retail chain deploys edge servers in 500 stores. Each server processes video from 20 cameras, generating heat maps of customer movement. Edge processing identifies high-traffic areas and low-performing displays. Store managers access real-time analytics through local dashboards. Cloud systems aggregate data across stores for inventory optimization.

Autonomous Vehicles require edge computing for safety-critical decisions. Vehicles process sensor fusion, object detection, and path planning locally. Latency requirements prevent reliance on cloud processing for driving decisions. Cloud connectivity provides map updates, traffic information, and over-the-air updates.

Self-driving systems combine inputs from cameras, lidar, radar, and GPS. Edge computing hardware in vehicles processes sensor data at 30-60 fps. Neural networks detect pedestrians, vehicles, and obstacles within 30ms. Driving decisions execute in under 100ms to maintain safety margins. Vehicles upload sensor logs and edge cases to cloud storage for improving machine learning models.

Smart Cities deploy sensors throughout urban environments. Edge computing processes data from traffic cameras, environmental sensors, and infrastructure monitoring systems. Local processing enables real-time traffic management and emergency response.

A city deploys edge nodes at traffic intersections. Cameras feed traffic flow data to edge processors. Systems detect congestion and adjust signal timing dynamically. Emergency vehicle detection preempts signals to clear paths. Environmental sensors trigger alerts for air quality issues. Edge processing reduces cloud transmission by 99% while improving response time from minutes to seconds.

Healthcare Monitoring processes patient data at edge devices and local servers. Wearable devices and medical sensors generate continuous data streams. Edge processing detects anomalies and generates alerts for healthcare providers. Processing data locally addresses privacy requirements and enables operation during network outages.

Hospital rooms deploy edge gateways that aggregate data from multiple medical devices. Edge processing monitors vital signs, detects deteriorating conditions, and generates immediate alerts. Nurse stations display real-time patient status from local edge processing. Cloud systems provide long-term storage and analytics while maintaining HIPAA compliance through edge filtering of personally identifiable information.

Content Delivery caches static content at edge locations to reduce latency for end users. Edge servers respond to requests for images, videos, and web pages without accessing origin servers. Dynamic content generation at the edge personalizes responses based on user location and context.

Content delivery networks operate thousands of edge points of presence globally. Edge servers cache popular content and execute serverless functions for dynamic content. Users receive responses from nearby edge locations with sub-50ms latency. Origin servers handle only cache misses and content updates.

Reference

Edge Computing Tiers

Deployment Models

Processing Distribution Guidelines

Network Considerations

Synchronization Patterns

State Management Approaches

Ruby Edge Computing Gems

Edge Security Checklist

Failure Mode Strategies