Edge-Native Request Routing with Causal Dependencies
US 11,847,392 B2
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US 11,847,392 B2 — granted June 2026. A method for routing requests across edge nodes using causal dependency metadata.
Key Points
- Routes requests based on causal dependencies.
- Compact metadata header — no central coordinator.
- Run-length encoded for low bandwidth overhead.
Field of the Invention
The present disclosure relates generally to distributed computing systems, and more specifically to methods and systems for routing requests across geographically distributed edge nodes in a manner that preserves causal consistency.
Background
Modern content delivery networks and edge computing platforms route user requests to the nearest available point of presence. However, when requests carry causal dependencies — for example, a write that must occur after a related read — naive geographic routing can violate those dependencies, leading to inconsistent state across regions.
Summary
The described embodiments encode causal dependencies into a compact metadata header appended to each request. Edge nodes inspect this header to make routing decisions locally, without consulting a centralized coordinator, preserving causality while maintaining the latency benefits of geographic distribution.
Claims
- A method for routing requests across geographically distributed edge nodes, comprising: receiving a request at a first edge node; identifying a causal dependency associated with the request; and forwarding the request to a second edge node selected based on the causal dependency.
- The method of claim 1, wherein the causal dependency is encoded in a compact metadata header appended to the request.
- The method of claim 2, wherein the metadata header is compressed using a run-length encoding scheme.
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Comments3


This is exactly what I needed. The breakdown of architectural tradeoffs was particularly illuminating — especially the point about eventual consistency vs. strong consistency being a product decision, not just a technical one.

Agreed. Would love a follow-up that goes deeper on the consensus algorithms.

Curious how this approach holds up at 10x the scale. Have you tested with sharded clusters?