P4Hauler: An Accelerator-Aware In-Network Load Balancer for Applications Performance Boosting
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Bibliographic record
Abstract
Programmable accelerators enable the execution of applications intended for running in usual servers. However, inappropriately running applications on these devices can lead to load imbalance and performance degradation. An alternative to tackle this problem is load balancing, but existing in-network load balancers typically have no visibility of accelerators and often hard code policies in the switch source code. In this article, we present <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P4Hauler</small> , an accelerator-aware in-network load balancer. In particular, our design discusses how to enforce load-balancing decisions in a programmable switch in a resource-aware manner, allowing different policies to handle traffic according to applications' needs. We use monitoring and compression techniques to store application resources in a programmable switch for resource-aware decisions. In addition, we propose building blocks that operators can dynamically choose to realize different load balancing policies on-the-fly. We implemented and evaluated a prototype of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P4Hauler</small> on a testbed to show its efficiency and deployment feasibility. Our results indicate that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P4Hauler</small> can support 27% more load and decrease the flow completion time by around 13% using only a single accelerator. Also, extensive simulations confirm the performance gain of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P4Hauler</small> at scale compared to the state-of-the-art.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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