expDDn

expDdn, short for Experimental Dynamic Data Delivery Network, is a conceptual framework and prototype platform for scalable data distribution across edge and fog computing environments. It aims to provide low-latency delivery, adaptive routing, and robust performance in heterogeneous networks by combining programmable control with distributed data planes.

The concept emerged in the early 2020s within academic and research settings as an approach to explore dynamic data placement and delivery in edge networks. Early prototypes focused on overlay routing, cache coordination, and versioned data objects. Open-source reference implementations and simulation toolchains were released to enable reproducible experiments.

expDdn separates control and data planes. The control plane orchestrates routing decisions, cache placement, and data versioning policies, while the data plane handles object delivery across a mesh of edge nodes and regional servers. Key components include a metadata service for object versions and locality hints, a programmable routing layer, per-object liveliness and expiration policies, and multi-tier caching. The system supports proximity-aware routing, dynamic replication, and resilience through replication and fast failover.

Objects are identified by globally unique identifiers with version vectors. The network continuously adapts to changes in connectivity and demand, rebalancing caches and migrating data as needed. Security considerations include authentication, integrity, and access control. Observability features such as telemetry and traceability are typically integrated.

While not a standardized protocol, expDdn has been used in simulations and pilot deployments to study content distribution, real-time sensor data streams, and software updates in edge ecosystems. It is also used as a testbed for evaluating new routing and caching strategies.

Related topics include content delivery networks, edge computing, mesh networks, and data versioning systems.