Scalable Edge Computing for Low Latency Data Dissemination in Topic-Based Publish/Subscribe
Bibliographic record
Abstract
Advances in Internet of Things (IoT) give rise to a variety of latency-sensitive, closed-loop applications that reside at the edge. These applications often involve a large number of sensors that generate volumes of data, which must be processed and disseminated in real-time to potentially a large number of entities for actuation, thereby forming a closed-loop, publish-process-subscribe system. To meet the response time requirements of such applications, this paper presents techniques to realize a scalable, fog/edge-based broker architecture that balances data publication and processing loads for topic-based, publish-process-subscribe systems operating at the edge, and assures the Quality-of-Service (QoS), specified as the 90th percentile latency, on a per-topic basis. The key contributions include: (a) a sensitivity analysis to understand the impact of features such as publishing rate, number of subscribers, per-sample processing interval and background load on a topic's performance; (b) a latency prediction model for a set of co-located topics, which is then used for the latency-aware placement of topics on brokers; and (c) an optimization problem formulation for k-topic co-location to minimize the number of brokers while meeting each topic's QoS requirement. Here, k denotes the maximum number of topics that can be placed on a broker. We show that the problem is NP-hard for k >=3 and present three load balancing heuristics. Empirical results are presented to validate the latency prediction model and to evaluate the performance of the proposed heuristics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".