Grace: Toward Routing in Dynamic Network Environments With Graph Embedding
Bibliographic record
Abstract
Recent efforts have explored adaptive routing via deep reinforcement learning (DRL) techniques without handcrafted parameter engineering. Intrinsically, routing decision-making is essentially a process used to find a subgraph in a graph-structured network. However, previous works seldom took topological relationships into consideration when providing adaptive routing algorithms, causing them to suffer from suboptimal routes in dynamic network environments involving both varying traffic loads and burst traffic. In this paper, we presentGrace, a novel graph embedding-based Deep Reinforcement Learning framework tailored for distributed routing algorithm optimization within the Software-Defined Networking (SDN) paradigm. Specifically,Graceleverages graph embedding to translate graph-structured entities into low-dimensional vectors, thereby enabling multiple DRL agents to learn optimal routing paths under dynamic network environments. Unfortunately, training multiple agents encounters inherent challenges in complicated and dynamic network scenarios. In response, we design an adaptive incremental training method forGracethat makes the model adapt to task complexity in a gradual manner, while speeding up its retraining efforts when environments change. To further accelerate convergence, we integrate intrinsic curiosity intoGraceto tackle large environments with sparse rewards. Extensive experiments conducted on two real-world topologies demonstrate the rationality and effectiveness ofGrace, and the results show throughput improvements of up to 40.1% compared to other state-of-the-art DRL routing algorithms under bursty traffic conditions.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".