Edge Computing Architecture

Table of Contents

1. Executive Summary
2. Introduction
3. Architecture Overview
4. Core Components
5. Distributed Variable Management
6. Real-Time Communications
7. Security Model
8. Scalability & Performance
9. Deployment Strategies
10. Best Practices

Executive Summary

Modern edge computing environments require sophisticated systems to manage distributed variables, coordinate real-time communications, and maintain security across heterogeneous devices. This whitepaper presents the ByteHive Framework, a comprehensive architecture for building scalable, reliable edge computing platforms on embedded systems.

The ByteHive Framework provides:

• Centralized Variable Management - Real-time, distributed variable access with type-safe operations
• Device Registry - Automatic device discovery, registration, and health monitoring
• Web API - RESTful FastCGI interface for remote management and monitoring
• Authentication & Authorization - Role-based access control with session management
• Real-Time Notifications - Publish-subscribe system for data-driven events
• Cross-Platform Support - Native compilation for x86_64, ARM64, and embedded systems

Introduction

Edge computing represents a fundamental shift in how we architect IoT and embedded systems. Rather than centralizing all intelligence and data processing in cloud environments, edge computing pushes computation closer to data sources, reducing latency, improving reliability, and enabling real-time decision making.

However, edge environments present unique challenges:

• Heterogeneity - Multiple device types, architectures, and protocols coexist
• Resource Constraints - Limited memory, storage, and processing power
• Real-Time Requirements - Deterministic behavior and predictable latency are critical
• Distributed Coordination - Systems must synchronize state across multiple devices
• Security & Isolation - Untrusted networks and devices require strong authentication

The ByteHive Framework addresses these challenges through a modular, efficient architecture designed specifically for embedded systems and edge computing deployments.

Architecture Overview

The ByteHive Framework follows a multi-tier architecture with clear separation of concerns:

Component Architecture Diagram

Core Components

ByteHive Core Server (bytehive_core)

The central variable server that manages all shared state across the edge system. The Core Server:

• Maintains an in-memory hash table of typed variables
• Supports multi-instance operation with isolated variable namespaces
• Provides thread-safe read/write operations with minimal locking
• Generates real-time notifications on variable changes
• Listens on Unix socket for client connections (/tmp/bytehive_core.sock)

Device Server (bytehive_devserver)

Manages device registration, discovery, and status tracking:

• Accepts device registration requests with metadata
• Tracks device status (online/offline/error)
• Maintains device datamodel configurations
• Generates device-level notifications
• Listens on separate Unix socket (/tmp/bytehive_devserver.sock)

FastCGI Web API (bytehive_fcgi)

Provides RESTful HTTP interface for remote management:

• Session-based authentication with cookie support
• Three access levels: Admin, User, Viewer
• Full CRUD operations on variables and devices
• System health and status endpoints
• Runs as FastCGI application (port 8080 by default)

Client Libraries

Two native C libraries for client development:

• libbytehive_client - Variable client for accessing shared state
• libbytehive_devclient - Device client for device registration and management

Utility Programs

Command-line tools for system administration:

• bhvars - Query and list variables
• bhsetvar - Set variable values
• bhwatch - Monitor real-time variable changes
• bhvarcreate - Bulk import variables from JSON
• bhdevlist - List registered devices

Distributed Variable Management

Variable Model

Each variable in the ByteHive system is defined by:

• Name - Unique identifier (max 128 characters)
• Type - int16, int32, int64, uint16, uint32, uint64, float, double, string
• Value - Current data stored in the variable
• Instance ID - Multi-instance support for isolation
• Max Size - For string variables, maximum character limit

Type Safety

All type checking occurs at variable creation time. Variables are strictly typed, preventing accidental type mismatches or data corruption. The system supports:

• Signed integers (16, 32, 64-bit)
• Unsigned integers (16, 32, 64-bit)
• Floating-point (32-bit float, 64-bit double)
• Strings with configurable max size

Multi-Instance Architecture

The framework supports multiple independent instances, enabling:

• Isolated variable namespaces per instance
• Multiple autonomous processes to coexist
• Per-instance memory isolation
• Scalable variable deployment across edge networks

Performance Characteristics

Variable operations are optimized for embedded systems:

• O(1) hash table lookup for get/set operations
• Minimal memory overhead per variable
• No dynamic allocation after variable creation
• Thread-safe operations with minimal lock contention
• Sub-millisecond latency for typical operations

Real-Time Communications

Notification System

ByteHive implements a publish-subscribe notification system for real-time data propagation:

• Clients subscribe to variables via notification requests
• When a variable changes, subscribed clients receive immediate notifications
• Supports multiple subscribers per variable
• Notifications include variable name, type, and new value

Message Protocol

All communications use a lightweight binary protocol:

• Message ID (1 byte)
• Message Type Identifier
• Variable metadata (name, type, size)
• Data payload (variable value)

Unix Socket Communication

The framework uses Unix domain sockets for local IPC:

• Lower overhead than TCP/IP
• File system based permissions model
• No network routing overhead
• Typical latency: 1-5 microseconds

Broadcast Capability

Variable changes are broadcast to all interested subscribers:

• Single write triggers multiple notifications
• Asynchronous delivery to subscribers
• No subscriber blocking on writes
• Scalable to hundreds of subscribers

Security Model

Authentication

The FastCGI layer implements user authentication:

• Username and password based login
• SHA-256 password hashing (no plaintext storage)
• Persistent password database (/var/auth/bhpw)
• Default Admin account with required password change on first login

Authorization Levels

Session Management

• Session-based authentication with 32-character hex session IDs
• Secure cookies with HttpOnly and SameSite=Strict flags
• 1-hour default timeout on inactivity
• Maximum 100 concurrent sessions
• In-memory session storage (cleared on restart)

Unix Socket Permissions

Local IPC sockets are protected by the filesystem permission model:

• Socket files owned by application user
• Permissions set to 0666 for shared access
• Only accessible to local processes
• No network exposure of core services

Best Practices for Deployment

• Change default Admin password immediately
• Use HTTPS reverse proxy for web API
• Restrict FCGI port to trusted networks
• Implement firewall rules for network access
• Regularly audit user access logs
• Use strong passwords (minimum 12 characters)

Scalability & Performance

Memory Efficiency

The ByteHive Framework is designed for resource-constrained environments:

• Minimal memory footprint (~2-5 MB for core server)
• Hash table implementation with configurable size
• No dynamic memory allocation after variable creation
• String variables limited by user-defined max_size
• Suitable for embedded systems with 64 MB - 512 MB RAM

Performance Metrics

• Variable Lookup - O(1) hash table, ~100 nanoseconds
• Variable Write - ~500 nanoseconds + notification broadcast
• Notification Delivery - ~1-5 microseconds per subscriber
• API Request - ~10-50 milliseconds (includes FCGI overhead)
• Socket Communication - ~1-5 microseconds

Scalability Limits

• Maximum Variables - Depends on available memory, typically 100K+
• Maximum Subscribers - Configurable, tested to 1000+
• Maximum Devices - 10K+ devices per registry
• Concurrent Connections - Thousands via FCGI

Optimization Techniques

• Hash table tuning for locality of reference
• Minimal lock contention through per-variable locking
• Batch operations for bulk variable creation
• Connection pooling in client libraries
• Lazy notification delivery for high-frequency changes

Deployment Strategies

Single-Machine Deployment

Ideal for localized edge sites (single factory floor, building, battery energy storage system, etc.):

• All ByteHive services on single embedded device
• Local devices connect via Unix sockets or HTTP API
• Minimal network requirements
• Suitable for Raspberry Pi, Intel NUC, or similar

Real-World Example: 7.2MWh BESS Deployment

• 192 cell voltages per BESS unit (bess_cell_voltage_001 - bess_cell_voltage_192)
• Critical float metrics: SOC percentage, pack voltage/current, temperatures
• String identifiers with max_size constraints (bess_system_id: 256 bytes)
• Multi-unit coordination (BESS-001-Primary, BESS-002-Secondary)
• uint32 cycle counters, uint16 status flags, uint64 timestamps
• Real-time balancing algorithms responding within 100 microseconds
• Site-level optimization coordinating power flows across units

Multi-Machine Deployment

For larger edge networks spanning multiple locations:

• Central management server running core services
• Edge gateways handling local device communication
• HTTP API for inter-site communication
• Replication and eventual consistency

Container Deployment

ByteHive runs in containerized environments:

• Lightweight Docker images (~50 MB)
• Support for ARM64 architecture
• Kubernetes-ready for orchestration
• Health check endpoints for liveness probes

Embedded Linux Deployment

Prebuilt filesystem images available:

• ext4 images for direct boot on embedded systems
• x86_64 and ARM64 architectures supported
• All ByteHive components included
• Ready for Raspberry Pi 5 and similar devices

Best Practices

Variable Design

• Use descriptive, hierarchical naming (e.g., "thermostat_01_temperature")
• Choose appropriate types to minimize memory usage
• Set realistic max_size limits for string variables
• Document variable purposes and expected value ranges
• Use bulk import (bhvarcreate) for large variable sets

BESS Variable Naming Example:

{
  "bess_system_id": {"type": "string", "value": "BESS-001-Primary", "max_size": 256},
  "bess_soc_percent": {"type": "float", "value": 75.5},
  "bess_power_active": {"type": "float", "value": -12160.0},
  "bess_voltage_pack": {"type": "float", "value": 480.5},
  "bess_system_status": {"type": "uint16", "value": 1},
  "bess_cycles_total": {"type": "uint32", "value": 1250},
  "bess_timestamp_last_update": {"type": "uint64", "value": 1706427600},
  "bess_cell_voltage_001": {"type": "float", "value": 2.502}
}
            

Security Practices

• Never use default passwords in production
• Implement HTTPS for all API communications
• Use role-based access control (restrict to User level where possible)
• Audit authentication logs regularly
• Isolate ByteHive services behind a firewall
• Use strong, unique passwords (minimum 12 characters)

Performance Optimization

• Batch variable updates to reduce notification overhead
• Use multi-instance support for variable partitioning
• Monitor memory usage and adjust variable limits accordingly
• Implement client-side caching for frequently read variables
• Use subscriptions instead of polling for real-time data

Operational Best Practices

• Implement automated backups of configuration files
• Use monitoring tools to track system health
• Plan for regular maintenance windows
• Document device datamodels and configurations
• Implement automated testing for critical variables
• Use containerization for easy deployment and scaling

Conclusion

The ByteHive Framework provides a robust, efficient foundation for building edge computing systems on embedded platforms. By combining centralized variable management, real-time communications, and comprehensive security, ByteHive enables organizations to deploy scalable IoT and embedded systems with confidence.

Key advantages of the framework include:

• Lightweight design suitable for resource-constrained environments
• Real-time, distributed variable management
• Strong security with authentication and role-based access control
• Easy integration through native C libraries and HTTP APIs
• Multi-platform support (x86_64, ARM64, and more)
• Comprehensive documentation and utilities

For organizations looking to modernize their edge computing infrastructure, ByteHive offers a proven, production-ready solution.