Load Balancing Using ECMP in Multi-Stage Clos Topology in a Datacenter
Bibliographic record
Abstract
Datacenters most widely use 3-stage Clos topologies such as fat-tree topology and leaf-spine topology. These network topologies may face challenges at scale in future as the datacenters are growing at a large scale. Therefore, 5-stage Clos topology is explored in this paper with its benefits. The paper focuses on load balancing of elephant flows using Equal-Cost Multi-Path (ECMP) algorithm. This leads to improvement in performance of 5-stage Clos topology which leads to decrease in number of failures that are incurred because of unavailable bandwidth. This performance is compared to 3-stage Clos in terms of time taken by a request for completion and the number of failures appeared in the network during the simulation experiments. The experimental results show that the 5-stage Clos topology performs better in terms of network reliability due to less number of packet losses and it also shows comparable results to 3-stage Clos for the processing time taken by the requests inside the datacenter network.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".