Edge Architecture for Dynamic Data Stream Analysis and Manipulation

The exponential growth in IoT and connected devices fea- turing limited computational capabilities requires the delegation of com- putation tasks to cloud compute platforms. Edge compute tasks largely involve sending data from an edge compute device to a central location where data is processed and returned to the edge device as a response. Since most edge network infrastructure is restricted in its ability to dy- namically delegate computation while retaining context, these events are commonly limited to a predefined task that the edge function is modeled to process and respond to. Edge functions traditionally handle isolated events or periodic updates, making them ill-suited for continuous tasks on streaming data. We propose a decentralized, massively scalable archi- tecture of modular edge compute components which dynamically defines computation channels in the network, with emphasis on the ability to efficiently process data streams from a large amount of producers and support a large amount of consumers in real time. We test this archi- tecture on real-world tasks, involving chaining of edge functions, context retention, and machine learning models on the edge, demonstrating its viability.