Spear: Optimized Dependency-Aware Task Scheduling with Deep Reinforcement Learning
Bibliographic record
Abstract
Modern data parallel frameworks, such as Apache Spark, are designed to execute complex data processing jobs that contain a large number of tasks, with dependencies between these tasks represented by a directed acyclic graph (DAG). When scheduling these tasks, the ultimate objective is to minimize the makespan of the schedule, which is equivalent to minimizing the job completion time. With task dependencies, however, minimizing the makespan of the schedule is non-trivial, especially when tasks in the DAG have different resource demands with respect to multiple resource types. In this paper, we present Spear, a new scheduling framework designed to minimize the makespan of complex jobs, while considering both task dependencies and heterogeneous resource demands at the same time. Inspired by recent advances in artificial intelligence, Spear applies Monte Carlo Tree Search (MCTS) in the specific context of task scheduling, and trains a deep reinforcement learning model to guide the expansion and rollout steps in MCTS. With deep reinforcement learning, search efficiency can be significantly improved by focusing on more promising branches. With both simulations and experiments using traces from production workloads, we compare the scheduling performance of Spear with state-of-the-art job schedulers in the literature, and Spear can outperform those approaches by up to 20%. Our results have validated our claims that MCTS and deep reinforcement learning can readily be applied to optimize the scheduling of complex jobs with task dependencies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".