Scheduling optimization strategy for data intensive workflows in cloud computing
Bibliographic record
Abstract
The rapid growth of data-intensive applications has led to an increased demand for efficient scheduling strategies in cloud computing environments. This paper focuses on the optimization of scheduling for data-intensive workflows, addressing the challenges posed by resource heterogeneity, complex workflow dependencies, and the need for scalability and elasticity. We propose a comprehensive approach that encompasses advanced task scheduling algorithms, dynamic resource allocation techniques, and effective parallelization and pipelining methods to enhance the performance of these workflows. The paper begins by characterizing data-intensive workflows and the cloud computing environment, highlighting the performance metrics crucial for evaluating workflow execution. It then delves into the scheduling challenges, discussing the implications of resource heterogeneity, the complexity of workflow dependencies, and the scalability and elasticity requirements of cloud-based workflows. We present optimization strategies that leverage heuristic and metaheuristic algorithms to schedule tasks efficiently, considering both task characteristics and resource capabilities. The resource allocation techniques discussed aim to optimize the utilization of cloud resources, adapting to the dynamic nature of the environment and the varying demands of tasks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".