Edge Architecture for Dynamic Data Stream Analysis and Manipulation.

The exponential growth in IoT and connected devices featuring limited computational capabilities requires the delegation of computation 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 dynamically 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 architecture 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 architecture on real-world tasks, involving chaining of edge functions, context retention, and machine learning models on the edge, demonstrating its viability .