Bibliographic record
Abstract
As increasingly large volumes of raw data are generated at geographically distributed datacenters, they need to be efficiently processed by data analytic jobs spanning multiple datacenters across wide-area networks. Designed for a single datacenter, existing data processing frameworks, such as Apache Spark, are not able to deliver satisfactory performance when these wide-area analytic jobs are executed. As wide-area networks interconnecting datacenters may not be congestion free, there is a compelling need for a new system framework that is optimized for wide-area data analytics. In this paper, we design and implement a new proactive data aggregation framework based on Apache Spark, with a focus on optimizing the network traffic incurred in shuffle stages of data analytic jobs. The objective of this framework is to strategically and proactively aggregate the output data of mapper tasks to a subset of worker datacenters, as a replacement to Spark's original passive fetch mechanism across datacenters. It improves the performance of wide-area analytic jobs by avoiding repetitive data transfers, which improves the utilization of inter-datacenter links. Our extensive experimental results using standard benchmarks across six Amazon EC2 regions have shown that our proposed framework is able to reduce job completion times by up to 73%, as compared to the existing baseline implementation in Spark.
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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 | 0.001 | 0.000 |
| Open science | 0.005 | 0.005 |
| 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".