Murmuration: On-the-fly DNN Adaptation for SLO-Aware Distributed Inference in Dynamic Edge Environments
Bibliographic record
Abstract
The proliferation of Virtual and Augmented Reality (VR/AR) and the Internet of Things (IoT) applications is driving the demand for efficient Deep Neural Network (DNN) inference at the edge. These applications often impose stringent Service Level Objectives (SLOs), such as latency or accuracy, that must be met under the constraints of limited resources and dynamic network conditions. In this study, we explore a novel approach to DNN inference across multiple edge devices, incorporating both model customization and partitioning dynamically, to better align with these constraints and SLOs. Unlike conventional methods that employ a single fixed DNN network, our system, termed Murmuration, combines one-shot Neural Architecture Search (NAS) and Reinforcement Learning (RL) to dynamically customize and partition DNN models. This approach adapts in real-time to the capabilities of the edge devices, network conditions, and varying SLO requirements. The design of Murmuration allows it to effectively navigate the large search space defined by DNN models, network delays, and bandwidth, offering a significant improvement in managing trade-offs between accuracy and latency.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".