Falcon: Live Reconfiguration for Stateful Stream Processing on the Edge
Bibliographic record
Abstract
Stream processing is an attractive paradigm for deploying applications in geo-distributed edge-cloud environments. However, the reverse economics of scale in edge networks and the movement of data sources between edges require the ability to dynamically reconfigure the deployment of stateful applications to adapt to workload variations and user mobility. Unfortunately, existing stream processing engines either do not support the reconfiguration of stateful operators or are ill-suited to edge-cloud environments since they stop application processing during reconfiguration or require costly duplication of application state. We propose Falcon, a new stream processing engine. At its core lies a live key migration approach to allow reconfiguration to occur with minimal disruption to processing, even across distant datacenters. Falcon supports the reconfiguration of stateful operators including different windowing approaches and source mobility across different edge regions. It scales gracefully with network latency, the number of datacenters, and the size and number of keys. Our evaluation in geo-distributed edge-cloud deployments shows that Falcon reduces the length of processing interruptions and their impact on latency by 2 to 4 orders of magnitude compared to the existing state-of-the-art frameworks such as Apache Flink, Trisk, and Meces.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".