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The ability to accurately estimate job runtime properties allows a scheduler to effectively schedule jobs. State-of-the-art online cluster job schedulers use history-based learning, which uses past job execution information to estimate the runtime properties of newly arrived jobs. However, with fast-paced development in cluster technology (in both hardware and software) and changing user inputs, job runtime properties can change over time, which lead to inaccurate predictions. In this paper, we explore the potential and limitation of real-time learning of job runtime properties, by proactively sampling and scheduling a small fraction of the tasks of each job. Such a task-sampling-based approach exploits the similarity among runtime properties of the tasks of the same job and is inherently immune to changing job behavior. Our analytical and experimental analysis of 3 production traces with different skew and job distribution shows that learning in space can be substantially more accurate. Our simulation and testbed evaluation on Azure of the two learning approaches anchored in a generic job scheduler using 3 production cluster job traces shows that despite its online overhead, learning in space reduces the average Job Completion Time (JCT) by 1.28x, 1.56x, and 1.32x compared to the prior-art history-based predictor. Finally, we show how sampling-based learning can be extended to schedule DAG jobs and achieve similar speedups over the prior-art history-based predictor.
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This content will become publicly available on November 15, 2026
Learning to Schedule: A Supervised Learning Framework for Network-Aware Scheduling of Data-Intensive Workloads
Distributed cloud environments running data-intensive applications often slow down because of network congestion, uneven bandwidth, and data shuffling between nodes. Traditional host metrics such as CPU or memory do not capture these factors. Scheduling without considering network conditions causes poor placement, longer data transfers, and weaker job performance. This work presents a network-aware job scheduler that uses supervised learning to predict job completion time. The system collects real-time telemetry from all nodes, uses a trained model to estimate how long a job would take on each node, and ranks nodes to choose the best placement. The scheduler is evaluated on a geo-distributed Kubernetes cluster on the FABRIC testbed using network-intensive Spark workloads. Compared to the default Kubernetes scheduler, which uses only current resource availability, the supervised scheduler shows 34–54% higher accuracy in selecting the optimal node. The contribution is the demonstration of supervised learning for real-time, network-aware job scheduling on a multi-site cluster.
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- PAR ID:
- 10647697
- Publisher / Repository:
- ACM
- Date Published:
- Page Range / eLocation ID:
- 848 to 853
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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