Embedding Multiple-Step-Ahead Traffic Prediction in Network Energy Efficiency Problem
Bibliographic record
Abstract
Adaptive Link Rate (ALR) is widely used to save energy consumption of network by adjusting the link rate according to the carried traffic through a network-level optimization of the flow allocation process. Existing ALR solution is mainly reactive, in which link speed is changed only when new traffic demand is requested. Also, they focus on energy consumption, and do not consider the cost of changes in the network (e.g., change in traffic routes, and link rates). Once bandwidth has been allocated for a demand, the link rate remains constant during the entire session. Therefore, this solution may result in sub-optimal schemes and requires multiple re-optimizations as traffic flows are fluctuating during the session, hence reducing the overall network performance. In this paper, we improve the ALR with a multiple-step-ahead method to optimize link rates based on forecasting traffic demand predictively. We formulate the proposed Predictive ALR (PALR) as an Integer Linear Programming (ILP) model and then design a heuristic simulated annealing (SA) -based algorithm to solve it. Our experimental results show our approach provides energy saving while it decreases on average 18% of link state transition and 11% of the flow reroutings compared to the original ALR.
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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 | 0.000 | 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 | 0.001 | 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 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".