Ontology-Based Resource Description and Discovery Framework for Low Carbon Grid Networks
Bibliographic record
Abstract
Using smart grids to build low carbon networks is one of the most challenging topics in ICT (Information and Communication Technologies) industry. One of the first worldwide initiatives is the GreenStar Network, completely powered by renewable energy sources such as solar, wind and hydroelectricity across Canada. Smart grid techniques are deployed to migrate data centers among network nodes according to energy source availabilities, thus CO2emissions are reduced to minimal. Such flexibility requires a scalable resource management support, which is achieved by virtualization technique. It enables the sharing, aggregation, and dynamic configuration of a large variety of resources. A key challenge in developing such a virtualized management is an efficient resource description and discovery framework, due to a large number of elements and the diversity of architectures and protocols. In addition, dynamic characteristics and different resource description methods must be addressed. In this paper, we present an ontology-based resource description framework, developed particularly for ICT energy management purpose, where the focus is on energy-related semantic of resources and their properties. We propose then a scalable resource discovery method in large and dynamic collections of ICT resources, based on semantics similarity inside a federated index using a Bayesian belief network. The proposed framework allows users to identify the cleanest resource deployments in order to achieve a given task, taking into account the energy source availabilities. Experimental results are shown to compare the proposed framework with a traditional one in terms of GHG emission reductions.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".