Data Driven Priority Scheduling on a Spark Streaming System
Bibliographic record
Abstract
Big data has become essential for businesses as it enables companies and organizations to gather insights from their data and use it to determine marketing opportunities, assist decision-making or even to find new business opportunities. Companies spend a great deal of effort collecting large amounts of data, which in some cases must be processed in real-time in order to capitalize on business opportunities. Predicting the expected input load at a given point in time can be very difficult and sometimes impossible. As a result, a great deal of effort is put into creating techniques to address varying input loads. A widely used approach is dynamic resource provisioning, but resource provisioners may not react in time to address the resource shortage which can result in increased processing latencies. This paper presents a priority scheduling technique that can be used in conjunction with dynamic and static resource provisioning. This approach allows users to assign a priority to input data items. The scheduler ensures that higher priority data items are given precedence over lower priority data items. This means that when resources become constrained the higher priority data items receive a greater share of resources and experience lower queueing delays in comparison to low priority items. A prototype for the data driven priority scheduler is implemented on the Spark Streaming system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".