Bibliographic record
Abstract
Grids and more recently clouds are distributed system infrastructures that are rapidly gaining popularity among researchers and users. By providing the ability to acquire resources on demand these systems provide elasticity resource usage as well as a pay-as-you-go opportunity, both of which can lead to a substantial savings for the system users. Appropriate management of resources by the middleware used by service providers is required however for effectively harnessing the power of the underlying distributed resource infrastructure. The problems range from handling resource heterogeneity, providing adequate security during resource access, allocating resources to user requests efficiently as well as effectively scheduling the requests that are mapped to a given resource. This talk will focus on the challenges associated with resource management on grids and clouds and discuss solutions to some of these problems. Particular attention will be paid to resource allocation and scheduling. Existing literature on resource management on grids and clouds describe techniques that are based on the detailed knowledge of local resource management policies as well as user estimates of resource demands for their requests. It is often impractical to assume such a detailed a priroi knowledge of management policies for all the resources will be available to resource brokers a large and dynamic heterogeneous environment. Moreover, user estimates of resource demands are often error prone. Performing effective resource management in the dark and handling such uncertainties associated with the local resource management policies and user estimated resource demands will be discussed. Techniques for providing both user satisfaction by improving the fastness of responses as well as service provider benefits through generation of high revenue will be described.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.020 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".