Edge Computing

Six Pillars of Digital Transformation

Enables data processing and decision-making closer to the source to support low-latency, resilient, and mission-critical operations.

  • Eliminates latency "round-trips" to centralized clouds for real-time reactions.
  • Ensures mission continuity in disconnected or contested environments (DDIL).
  • Essential for IoT, Autonomous Systems, and Tactical Infrastructure.
Back to Framework Explore ODXA
Mission outcomes delivered through integrated digital capabilities Mission Solutions & Capabilities Architectural integration aligns tradeoffs, design decisions, and cross-pillar dependencies Architectural Integration Tradeoffs • Alignment • Design Decisions Cloud, DevOps, emerging compute, and decentralized platforms enabling portable execution everywhere Ubiquitous Computing Edge Computing enables data processing and decision-making closer to where data is generated to support low-latency, resilient, and mission-critical operations. Edge Computing Artificial Intelligence enables systems to learn, reason, and assist decision-making through data-driven models embedded across digital and operational workflows. Artificial Intelligence Cybersecurity protects systems, data, and missions through Zero Trust principles, resilience, and continuous risk management across all domains. Cyber Security Data Management governs how data is collected, integrated, secured, and used to drive insights and decisions across the enterprise. Data Management Advanced Communications provides secure, resilient connectivity enabling data, systems, and people to operate as an integrated whole. Advanced Comms Strategic Domain Organizational Domain Process Domain Digital Domain Physical Domain

Core Capability

Placing compute, analytics, and control functions near sensors and users to support mission-critical operations where latency or connectivity constraints limit cloud reliance.

Definition

Short Definition:

Edge Computing enables data processing and decision-making closer to where data is generated to support low-latency, resilient, and mission-critical operations.

Long Definition:

Edge Computing extends digital capabilities beyond centralized environments by placing compute, analytics, and control functions near sensors, users, and operational systems. This pillar is essential for environments where latency, connectivity, security, or autonomy constraints limit reliance on centralized cloud platforms. Within the ODXA framework, edge computing requires coordinated architectural decisions across all domains to ensure decentralized assets remain manageable, secure, and aligned with mission strategy.

This Pillar Is

  • Processing at the Source: Turning raw data into insights before transport.
  • Tactical Autonomy: Ensuring systems function without a constant cloud link.
  • Latency-Aware: Architecture designed for sub-millisecond response.

This Pillar Is Not

  • Just "Small Servers": It's about the location and function, not just form factor.
  • A Cloud Mirror: You cannot simply copy-paste central cloud stacks to the edge.
  • Independent Silos: Edge nodes must remain part of a unified framework.
“Edge Computing bridges the Physical and Digital domains by processing sensor data and driving real-time controllers locally.”
CENTRAL CORE / CLOUD Policy Governance • Global Model Training ORCHESTRATION FABRIC Workload Distribution • Resource Optimization • Interoperability OPERATIONAL EDGE Site-wide Coordination • Regional Analytics TACTICAL EDGE Point-of-Action • Real-time Inference SENSOR ACTION SENSOR ACTION END-TO-END EDGE ARCHITECTURE • SENSOR-TO-ACTION

In the ODXA framework, Edge Computing creates a Sensor-to-Action lifecycle. The architecture allows data to be processed and acted upon locally at the Tactical Edge, coordinated site-wide at the Operational Edge, and strategically augmented by the Central Core.

How Edge Computing Maps Across ODXA

Strategic Domain

  • Identify "Mission-Critical" workflows that cannot tolerate cloud latency or outages.
  • Define the balance between decentralized autonomy and centralized oversight.
  • Establish data sovereignty policies for data captured and stored at the physical edge.
  • Align investment with tactical outcomes (e.g., safety, real-time response) vs. purely cost-saving.

Organizational Domain

  • Define ownership of distributed hardware assets across IT and Operational Technology (OT).
  • Upskill "Field Technicians" to manage software-defined infrastructure at remote sites.
  • Establish decentralized accountability models for local site resilience.
  • Bridge the gap between central dev teams and remote field operators.

Process Domain

  • Implement "Zero-Touch Provisioning" to deploy nodes without on-site IT expertise.
  • Standardize "Store and Forward" data processes for intermittent connectivity.
  • Automate remote patching and security updates across thousands of nodes.
  • Establish edge-to-cloud lifecycle processes for model retraining and deployment.

Physical Domain

  • Environmental Hardening: Address constraints of temperature, vibration, and dust.
  • Power & Weight: Optimize for constrained power budgets and physical footprint.
  • Physical Tampering: Ensure nodes are physically secured in public or contested sites.
  • Sensor Integration: Manage the physical cabling and protocol conversion for local data sources.

Digital Domain

  • Deploy lightweight orchestration (e.g., K3s, WASM) suited for small-footprint hardware.
  • Leverage Edge-IAM to ensure security persists even when the node is offline.
  • Optimize containerized microservices for "Limited Resource" environments.
  • Implement API-first interfaces for local sensor and machine-to-machine communication.

Use Cases and Failed Modes

Common Use Cases

  • Real-time Telemetry: Processing drone or vehicle data locally for collision avoidance.
  • Smart Manufacturing: Local AI inference for millisecond-speed quality control on production lines.
  • Tactical DDIL: Enabling military or emergency systems to operate while disconnected.
  • Remote Healthcare: Localized patient monitoring and data filtering in clinics with limited bandwidth.

Common Failure Modes

  • Cloud-Mirroring: Attempting to run heavy cloud management stacks on resource-limited hardware.
  • Ignoring the "Sneakernet": Building systems that require physical access for every minor software update.
  • Connectivity Assumptions: Designing edge nodes that "brick" when the central cloud is unreachable.

System-of-Systems Context

Enabling AI

Acts as the host for Inference—allowing AI models to act on data in real-time at the source while the Cloud handles heavy training.

Enabling Data Management

Provides "Data Reduction" at the edge, filtering massive streams of raw sensor data so only high-value insights are sent over costly communications links.

Dependency on Ubiquitous Computing

Relies on the portability layer to ensure that code developed in the central lab can actually execute on diverse edge hardware.

Dependency on Cybersecurity

Requires decentralized "Zero Trust" because edge nodes are physically accessible and more vulnerable to compromise than a locked data center.

When to Start Here

Prioritize Edge Computing if your mission outcomes are suffering from "Latency Lag" or if your operations are crippled every time the wide-area network connection drops.

Frequently Asked Questions

Is Edge Computing just another name for IoT?

No. IoT (Internet of Things) focuses on the *connection* of devices. Edge Computing focuses on the *processing and control* capabilities provided to those devices at the point of action.

How does 5G impact the Edge?

5G provides the "highway" (Advanced Communications) that allows edge nodes to communicate with high bandwidth and low latency, making massive Edge deployments possible.

Is it more expensive than Cloud?

The initial hardware cost can be higher due to site distribution, but the **Operational ROI** is found in reduced bandwidth costs and the prevention of high-stakes mission failures during outages.

Learn More

The Six Pillars

Framework Overview

The ODXA Domains

Learn ODXA Structure

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