edgefirst

Edgefirst is a term describing a design and deployment approach that places computation and data processing at or near the edge of the network, close to data sources. It emphasizes processing, filtering, and decision-making on edge devices, gateways, or micro data centers before sending data to centralized cloud services.

The approach aims to reduce latency, conserve bandwidth, improve privacy and data sovereignty, and increase resilience by enabling local operation. It often involves lightweight, containerized services, serverless functions, or AI models deployed at the edge, with coordination to the cloud for orchestration, model updates, and long-term storage.

Architectural patterns associated with edgefirst typically include edge devices (sensors, gateways, embedded systems), an edge orchestration layer, local storage, and connections to a cloud backend. Processing tasks can encompass data filtering, aggregation, event triggering, and local inference. Secure communication and updates are commonly emphasized, and federated learning may be used to train models without transmitting raw data to central servers.

Common use cases span the Internet of Things, industrial automation, autonomous vehicles, smart city applications, and real-time analytics scenarios. Edgefirst deployments may be incremental, addressing specific workloads at the edge before expanding to broader edge-centric architectures.

Challenges and considerations include managing heterogeneity of hardware and software, ensuring security across distributed nodes, handling energy and cooling constraints, maintaining updates and observability, and navigating evolving standards. The term is mainly used in industry and marketing to denote prioritizing edge processing, rather than signifying a single formal specification.