Adoop: MapReduce for ad-hoc cloud computing
Bibliographic record
Abstract
MapReduce is a widely adopted distributed programming model for big data analytics. It facilitates processing large data sets using a dedicated cloud containing many worker nodes. For most implementations of MapReduce to work, only a few nodes may fail at any given time. This poses a challenge for organizations trying to harness the power of underutilized computing resources by provisioning cloud services on top of existing IT infrastructure. Exploiting ad-hoc clouds for MapReduce operations could yield significant advantages. It could reduce operational costs, improve resource utilization, and enable big data analytics. To the best of our knowledge, only few previous researchers have tried to optimize MapReduce for such volatile, non-dedicated, environments. This paper investigates how Hadoop --the most widely used open-source implementation of MapReduce-- can be optimized to run efficiently in ad-hoc cloud environments, despite the challenges these environments impose. To address these challenges, we present Adoop: a history-based scheduling approach to MapReduce, where the availability history of each node affects Hadoop scheduling decisions. Adoop maintains an availability and utilization based score of all the participating nodes, and dynamically re-adapts task assignments accordingly. A proof-of-concept implementation of Adoop has been provided and made publically available. Our initial experiments show that Adoop outperforms Hadoop in a simulated ad-hoc environment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".