Network QoS provision for distributed grid applications
Bibliographic record
Abstract
Grid computing provides a global-scale distributed computing infrastructure for executing scientific and \nbusiness applications. An important requirement is the need to make this infrastructure appear as a single logical coordinated resource – which in reality consists of a variety of resources aggregated across different administrative domains. The aggregation of network resources is often undertaken over a ‘best effort’ infrastructure as provided by the Internet – however many applications, which necessitate soft-real time constraints, such as collaborative working or remote visualisation, require more stringent traffic guarantees. Managing Quality of Service (QoS) requirements across these aggregated resources therefore becomes an important concern. Such QoS criteria must extend to computational, data and network resources, and are often expressed in a Service Level Agreement, multiples of which may co-exist over the entire collection of resources. In this paper we focus on network QoS as part of our wider work on the Grid QoS Management (G-QoSm) framework. Provisioning of such QoS support is provided via the Differentiated Services architecture, and relies on the use of a Bandwidth Broker (BB) component. Performance results of using the BB alongside other elements in G-QoSm are presented.
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 | 0.001 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".