Edge computing frameworks

Key facts

First appeared

2012

Category

technology

Problem solved

Edge computing frameworks address the challenges of high latency, limited bandwidth, and data privacy/security inherent in solely relying on centralized cloud computing for increasingly distributed and real-time applications. They enable applications to operate closer to data generation, improving response times, reducing data transmission costs, and enhancing data sovereignty for IoT and critical infrastructure.

Platforms

Android (for edge devices), Kubernetes (for orchestrating edge nodes), ARM-based processors, GPUs/TPUs (for AI inference at edge), Embedded Linux, x86-based processors, Real-time Operating Systems (RTOS) like FreeRTOS, Windows IoT Core, Linux (various distributions)

Related technologies

• IoT devices
• Containerization (Docker, Kubernetes)
• Serverless Functions
• Artificial Intelligence (AI) / Machine Learning (ML)
• Cloud Computing (AWS, Azure, GCP)
• 5G networks
• Embedded Systems
• Real-time data streaming (Kafka, MQTT)

Notable users

• Manufacturing companies
• Verizon
• Cisco
• Energy sector
• Siemens
• IBM
• Schneider Electric
• Automotive industry
• Microsoft (Azure)
• AT&T
• Google (GCP)
• Amazon (AWS)