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 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 | 0.000 |
| 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".