A Scalable Wide-Area Grid Resource Management Framework
Bibliographic record
Abstract
Grid computing systems federate resources belonging to several organizations to support applications with large computation and storage needs. Effective resource management is crucial for realizing the promise of the grid. However, current grid resource management frameworks have a number of limitations including poor scalability, and inadequate support for quality of service (QoS). This paper describes a novel and scalable grid resource management framework that can address these limitations. The framework relies on a hierarchical organization of resources and resource managers (RM) within an organization. Resources are assigned to jobs through decentralized inter and intra organizational collaborations between RMs. The framework employs a hierarchical information aggregation scheme that permits scalable grid resource management. Such a capability allows more intelligent placement of workloads across the grid than is feasible with traditional resource managers. For example, loads can be balanced across the grid clusters to avoid over utilization of resources resulting in better QoS for jobs. Hierarchical segmentation of grid resources allows the framework to handle dynamic situations (e.g., failure recovery, and nodes joining the Grid). The improved scalability of the framework, however, comes at the price of incurring additional complexity and overhead. Sophisticated protocols need to be designed to build and provide the functionality of the hierarchy. Considering the benefits and limitations of our approach, we believe the hierarchical framework is best suited for managing planetary scale grid systems supporting "embarrassingly" parallel jobs that require computational resources beyond the borders of an organization
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 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".