Bibliographic record
Abstract
Recently, Hadoop, an open source implementation of MapReduce, has become very popular due to its characteristics such as simple programming syntax, and its support for distributed computing and fault tolerance. Although Hadoop is able to automatically reschedule failed tasks, it is powerless to deal with tasks with poor performance. Managing such tasks is vital as they lower the whole job's performance. Thus in this work, we design a novel collection technique that identifies and collects garbage tasks. Three research questions are addressed in this work. The first, does collecting (shutting down) (slow) tasks help in reducing the total job completion time and resources cost? The second, when is it most efficient to invoke the Garbage Collector? The third, how to identify (slow) tasks and what are the major factors causing a task to slow down?. The proposed Garbage Collector is evaluated on Amazon EC2 using two metrics: (i) the time for a single job completion, and (ii) resource costs. The empirical results using the TeraSort benchmark show that collecting tasks does reduce the job completion time by 16% and resources cost by 27%. The results also show that the Garbage Collector needs to be invoked before the job is 40% completed, otherwise it would be better to leave the slow tasks till the end of the job because at this point the cost of re-executing these slow tasks becomes high. Finally, our results show that CPU utilization is a good indicator of slow tasks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".