Dynamic load balancing in SDN-based data center networks
Why this work is in the frame
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Bibliographic record
Abstract
With the migration of computational powers and applications to the cloud, Data Center Networks (DCNs) have become the backbone of the underlying infrastructure. Operation of the data centers relies on huge computational resources and bandwidth, that often undergo high operational costs, frequent link congestions, and imbalanced traffic loads. Software Defined Networking (SDN) based traffic load management improves accessing resources by distributing traffic among multiple paths efficiently and in a timely manner. In data centers, SDN-based traffic management techniques control paths of incoming flows and optimize flows during their transmissions. In this paper, we propose an SDN-based dynamic load management algorithm for optimizing link utilization in DCNs while considering the flow priority. The algorithm finds the shortest paths from each host to others and calculates every link's cost. When congestion occurs in a certain path, it replaces the old path with the alternative best route that has the minimum link cost and lower traffic flow. Performance of the algorithm is evaluated by measuring throughput, delay and packets loss in a fat-tree DCN. Simulation results show improved performance in load balancing over time as the algorithm keeps on running.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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