A Scheduling Algorithm for Hadoop MapReduce Workflows with Budget Constraints in the Heterogeneous Cloud
Bibliographic record
Abstract
In recent years cloud services have gained much attention as a result of their availability, scalability, and low cost.One use of these services has been for the execution of scientific workflows as part of Big Data Analytics, which are employed in a diverse range of fields including astronomy, physics, seismology, and bioinformatics.There has been much research on heuristic scheduling algorithms for these workflows due to the problem's inherent complexity, however existing work has mainly considered execution in a utility grid environment using a generic distributed framework.For our research, we consider the ever-increasingly popular Apache Hadoop framework for scheduling workflows onto resources rented from cloud service providers.Contrary to other distributed frameworks, the Hadoop MapReduce model imposes a functional style onto application definition, and as such presents an interesting and unapproached challenge for workflow scheduling.Investigated in our work is budgetconstrained workflow scheduling on the Hadoop MapReduce platform, wherein we devise both an optimal and a heuristic approach to minimize workflow makespan while satisfying a given budget constraint.Firstly I would like to thank my parents for their ongoing support and encouragement throughout my academic career.As well, I would like to thank several of my close friends who helped to keep me motivated throughout my master's degree.I would also like to thank
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".