Load balancing with minimal flow remapping for network processors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Maintaining high performance in parallel processing routers while preserving packet ordering within the flows is a difficult problem. To preserve packet ordering, hashing at the flow level has been used to distributed packet processing workload among the router processing units. Even though it preserves ordering, hashing alone may cause significant workload imbalance and thus adaptive methods are usually needed. In this paper, we present an input port selection scheme that can be augmented with the adaptive Highest Random Weight (adaptive HRW) method. The adaptive HRW is a hash-based method that works at the flow level and is used to balance packet processing workload among the router processing units. When imbalance occurs, the adaptive HRW method triggers all input ports to re-balance their workload among the processing units. When augmented the selection scheme, the adaptive HRW method should be able to identify the subset of input ports responsible for the imbalance. The simulation results show that deploying the selection scheme with the adaptive HRW significantly reduces the number of flows remapped while balancing the packet processing workload among the router processing units.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it