# Algorithmic Techniques for GPU Scheduling: A Comprehensive Survey
## 3 Scheduling Algorithms and Their Performance
### Learning-Based Adaptive Algorithms
To address this, compact abstractions like DeepRM's image grids, Decima's graph embeddings, and transformer-based attention mechanisms help focus the agent on key features and keep learning manageable [165]. Stability and safety require that learned strategies adhere to hard constraints--such as maximum wait times or per-tenant GPU caps--so modern systems often embed the RL policy within a rule-based guardrail that ensures correctness before actions are executed. Finally, interpretability remains a challenge: opaque policies make it difficult to understand why a specific scheduling decision was made, undermining operator trust in mission-critical environments [166]. Despite these hurdles, momentum is building. Industrial teams at Alibaba and several hyperscalers are already prototyping deep RL for resource management, motivated by the steady improvements observed in research prototypes. Adaptive scheduling via feedback control: Not all adaptive scheduling approaches rely on machine learning; some use principles from feedback control systems [167; 168; 169]. These methods continuously monitor system metrics and adjust scheduling behavior in response, forming a closed-loop system. For instance, a basic feedback-driven scheduler might monitor average GPU utilization over the last minute: if it falls below a predefined threshold, the system could respond by increasing GPU packing density (e.g., enabling more aggressive time-sharing) or by admitting more jobs from the queue. Conversely, if the average job slowdown rises above a target, the scheduler could throttle job admissions or boost the priority of small or short jobs. Such strategies can be viewed as control systems:monitor a key performance indicator, and if it deviates from the target, adjust a parameter in the scheduling policy accordingly. Similarly, Elan [169] provides efficient elastic scaling for deep learning jobs by combining a hybrid batch size scaling mechanism with asynchronous coordination and topology-aware state replication. Unlike checkpoint-based systems, Elan allows jobs to scale out, scale in, and migrate without restarting, minimizing disruption and achieving sub-second elasticity with negligible overhead. By balancing training efficiency and model convergence during resource changes, Elan enables higher GPU utilization and faster job completion in production-scale clusters. A more sophisticated example of feedback control in GPU scheduling is incremental job scaling, as implemented by systems like Pollux and AntMan [68; 168]. Pollux uses an online feedback loop per job to monitor its goodput (effective throughput in terms of training progress per unit time) and adapt GPU allocations accordingly. If additional GPUs do not significantly improve goodput (i.e., the job hits diminishing returns), the system reduces its GPU share and reallocates resources to other jobs. Conversely, if a job scales efficiently, more GPUs are allocated up to the point of diminishing returns. This feedback-driven adjustment occurs periodically and adapts to the evolving performance characteristics of each job. AntMan adopts a similar philosophy but extends it by modifying the underlying deep learning frameworks to allow fine-grained, real-time scaling of both GPU memory and compute resources. By continuously monitoring mini-batch performance and dynamically adjusting resource allocation without requiring job restarts, AntMan opportunistically improves GPU utilization and cluster throughput under multi-tenant workloads. Both systems exemplify adaptive behavior based on online measurements--a form of reinforcement through iterative performance feedback--positioning them between classical control and learning-based approaches, and leveraging empirical tuning without requiring offline model training. Another direction is predictive queueing control, where the scheduler uses observations of job inter-arrival and service times to anticipate load patterns and adjust parameters preemptively.