M2SDN: Achieving multipath and multihoming in data centers with software defined networking
Bibliographic record
Abstract
The increasing virtualization in data centers brings growing inter-node communication by running various applications on virtual machines located physically separated. Meanwhile, the virtual machines on a physical server also compete for limited Ethernet interface I/O resources. Load balancing with multipath and multihoming is usually the key to address the bandwidth and Ethernet I/O bottlenecks. Even though various variants of equal cost multipath (ECMP) schemes have been widely applied for load balancing, the equal and fairness assumption of ECMP results in imbalance and underutilization in asymmetric networks without considering network topology and traffic situation. The multihoming solutions usually require protocol modification and peer's support. In this paper, we propose a utilization & topology-aware multipath routing and multihoming scheduling with Software Defined Networking (SDN) in data centers to address these bottlenecks. The utilization & topology-aware multipath routing takes global network situation to avoid congestions and balances utilization of multiple paths. At a multi-homed server end, the multihoming scheduler balances the traffic among multiple Ethernet interfaces and ensures QoS guarantees when accessing the network without changing network stack. We compared our approach with traditional single path and equal cost multipath schemes, and the results showed that the utilization & topology-aware multipath routing and multihoming scheduling achieved much higher network utilization and better load balancing, especially for asymmetric networks.
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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 | 0.001 | 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 | 0.001 | 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 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".