QoS-Aware, Cost-Efficient Scheduling for Data-Intensive DAGs in Multi-Tier Computing Environment
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Bibliographic record
Abstract
In today’s scientific landscape, Directed Acyclic Graphs (DAGs) are pivotal for representing task dependencies in data-intensive applications. Traditionally, two dominant bottom-up DAG scheduling approaches exist: one overlooks communication contention and the other fails to exploit parallelization for improving latency. This study distinguishes itself by advocating a top-down approach prioritizing latency or cost optimization in multi-tier environments to fulfill QoS and SLA requirements. Our strategy effectively mitigates bandwidth contention and facilitates parallel executions, leading to substantial completion time reductions. Our findings suggest that myopic knowledge-based scheduling, emphasizing latency or cost minimization, can yield benefits comparable to its look-ahead counterparts. Through latency-efficient and cost-efficient topological sorting, our <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">wDAGSplit</i> strategy introduces a two-stage partitioning and scheduling approach. Its simplicity and adaptability extend its usability to DAGs of any scale. Evaluated on over 100,000 real-world DAG applications, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">wDAGSplit</i> demonstrates latency enhancements of up to 80x compared to Edge-only scenarios, 15x to Near-Edge-only, and 6x to Cloud-only. In terms of cost, our approach achieves enhancements of up to 60x compared to Edge-only scenarios, 250x to NE-only, and 70x to Cloud-only. Moreover, for DAGs with 50 tasks, we achieve 5x reduced latency compared to previous approaches, along with a complexity reduction of up to 24 times.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it