List-Based Task Scheduling for Cloud Computing
Bibliographic record
Abstract
Cloud computing model provides global and on-demand access to resources in a seamless manner with minimal interaction with the service provider. A typical cloud data center consists of several computational resources interconnected with each other through high-speed networks. In cloud the program execution can be visualized as a collection of multiple tasks represented by Directed Acyclic Graph (DAG) that execute in their logical sequence. Prioritization of these tasks plays an important role to achieve high performance and improved efficiency in a cloud environment. In this paper, we propose a novel task scheduling algorithm named Median Deviation based Task Scheduling (MDTS), which uses Median Absolute Deviation (MAD) of the Expected Time to Compute (ETC) of a task as a major attribute to calculate ranks of the given tasks. We use coefficient-of-variation (COV) based technique that considers task and machine heterogeneity to estimate the ETC of a particular DAG. The proposed algorithm is evaluated under various conditions using synthetic DAGs and real world applications. Our evaluation shows that the proposed MDTS algorithm produces high quality schedules and significantly reduces the makespan of an application by up to 25.01%.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".