Techniques for Handling Error in User-Estimated Execution Times During Resource Management on Systems Processing MapReduce Jobs
Bibliographic record
Abstract
In our previous work, we described a resource allocation and scheduling technique for processing an open stream of MapReduce jobs with SLAs (characterized by an earliest start time, an execution time, and a deadline) called the Hadoop Constraint Programming based Resource Management technique (HCP-RM). Since the user-estimated job execution times are used to perform resource allocation and scheduling, error/inaccuracies in the execution times can hinder the ability of HCP-RM from making effective scheduling decisions, leading to a degradation in system performance. This paper focuses on improving the robustness of HCP-RM by introducing a mechanism to handle errors/inaccuracies in user estimates of job execution times that are submitted as part of the job's SLA. A Prescheduling Error Handling technique (PSEH) is devised to adjust the user-estimated execution times of the jobs to make them more accurate before they are used by the resource management algorithm. Results of experiments conducted on a Hadoop cluster deployed on Amazon EC2 demonstrate the effectiveness of the PSEH technique in improving system performance.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".