Let's Adapt to Network Change: Towards Energy Saving with Rate Adaptation in SDN
Why this work is in the frame
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Bibliographic record
Abstract
The exponential growth of network users and their communication demands has led to a tangible increment of energy consumption in network infrastructures. A new networking paradigm called Software Defined Networking (SDN) recently emerged which simplifies network management by offering programmability of network devices. SDN through providing the monitored realtime traffic rates and the ability of fast re-routing assists to lower link data rates via rate-adaptation technique which reduces considerably the power consumption of network. The main idea behind this paper is to find a distribution of traffic flows over pre-calculated paths which allow adapting the transmission rate of maximum links into lower states. We first formulate the problem as a Mixed Integer Linear Programming (MILP) problem. Then, we present four different computationally efficient algorithms namely greedy first fit, greedy best fit, greedy worst fit and a meta-heuristic Genetic Algorithm (GA) based method to solve the problem for a realistic network topology. Simulation results show that the GA-based method consistently outperforms the three other proposed greedy algorithms.
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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 | 0.001 |
| 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 it