Bibliographic record
Abstract
It is without a doubt that the data center is the core of most IT industries, providing services to billions of users today.The preferred data center network (DCN) architecture used for this platform is the 2-tier leaf-spine topology, which is not only energy efficient but has a good performance metrics.Furthermore, due to the exponential increase of information and services from datacenters, it is projected that a 14.1 zeta bits of bandwidth would be needed to meet the current demands of data by the end of 2020 in USA data centers alone.This would require a tremendous amount of energy to run those data centers.Hence, in recent years there is more focus on reducing the energy consumption in a data center.In trying to reduce the energy consumption of a data center, this thesis approaches the problem by utilizing Segment Routing-Traffic Engineering, and other power algorithms for an efficient DCN.The proposed approach makes it possible to deactivate spine switches and links on the data center networks, which results in energy savings.Our experimental results yielded an approximate of 80% in energy savings of the spine switches consumption whilst maintaining an average similar traffic performance.First, and foremost, I would like to acknowledge, my supervisor Prof Chung-Horng Lung of Carleton University, for giving the opportunity to carry out this thesis work.He became not only an academic supervisor, but a resourceful help, and beacon of hope to complete my research work.I want to acknowledge Mr Busuyi, he became an uncle where I needed one, an elderly friend.His constant help are still invaluable to
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".