MaxFlowSDN: SDN-Based Maximum Flow Routing for High-Throughput Data Center Networks
Bibliographic record
Abstract
Large data centers continuously move data from one server to another due to up-or downscaling virtual server specifications to meet user requirements.Most data center topologies allow multipath routing between any pair of nodes within their network to increase throughput, as well as resilience against link failures.Several approaches have been proposed and developed to utilize these routes to improve network performance.Existing methods often face challenges in achieving maximum throughput across diverse topologies without requiring kernel modifications, leaving room for improvement in practicality and scalability.In this paper, we propose a novel system, MaxFlowSDN, which uses the maximum flow algorithm along with traditional SDN and TCP to deliver higher throughput in data centers.MaxFlowSDN yielded 80% higher throughput in the Fat-Tree topology compared to StandardTCP, ParallelTCP, and MPTCP.In DCell and BCube topologies, it achieved approximately 190% higher throughput than StandardTCP and nearly 50% improvement over ParallelTCP and MPTCP.For evaluation, we deployed our system in different data center topologies and compared our results against existing methods.These results demonstrate that MaxFlowSDN provides maximum flow throughput in the data center environment while addressing the limitations of current approaches.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".