Contributions to a Green IT project: definition of use cases and first steps towards a power model for routers
Bibliographic record
Abstract
One of the current research hot topics on IP networks is how to minimize energy-related communications. Currently, network technologies are responsible for 2% of global emissions of CO2 and its reduction would mean a significant improvement of environmental conditions and a reduction in the rate of global warming.\nThere are several efforts related to Green IT such as the migration and consolidation of virtual machines, the impact of virtualization and cloud computing, and energy efficiency metrics. The proposed master thesis focuses on an exploration, in the sense of reviewing the state of the art (what other authors have done), to identify areas where contributions can be made, and\nsuggest some sort of improvement.\nIn this context, the i2CAT Foundation is taking part in the Canadian project GSN, an innovative project focusing on the relationship between networks and green datacenters, powered by renewable energies and following the “follow the sun/follow the wind approach”, in order to migrate Green ICT services and therefore reducing the carbon footprint. This first part of this thesis describes some contributions to the GSN project, such as helping in the installation of a solar-powered node, and the definition of the GSN Use Case. The second part focuses on a specific task not included in GSN but of vital importance and of great interest: the development of a power model for routers, specifically for\nvirtual routers as those that can be migrated in the GSN Project.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".