A priority based resource scheduling technique for multitenant storm clusters
Bibliographic record
Abstract
In this work in progress paper, we present our ongoing effort towards devising a priority based resource scheduling technique and framework for apache storm. Apache Storm is a popular distributed real time stream processing engine which has been widely adopted by key players in the industry including YAHOO and Twitter. An application running in storm is called a topology that is characterized by a Directed Acyclic Graph (DAG). To run multiple of such topologies in a storm cluster, storm provides with default, out of the box scheduler called Isolation Scheduler. Isolation Scheduler assigns resources to topologies based on static resource configuration and does not provide any means to prioritize topologies based on their varying business priority. As a result, performance degradation, even complete starvation of topologies with high business priority is possible when available cluster resources are insufficient. A priority based resource scheduling strategy is proposed in this paper to overcome this problem. A preliminary performance evaluation is performed to demonstrate effectiveness of the proposed scheduler over the default storm Isolation Scheduler.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".