JASDA: Introducing Job-Aware Scheduling in Scheduler-Driven Job Atomization
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866911214537277440 |
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| author | Konopa, Michal Fesl, Jan ánek, Ladislav Ber |
| author_facet | Konopa, Michal Fesl, Jan ánek, Ladislav Ber |
| contents | The increasing complexity and temporal variability of workloads on MIG-enabled GPUs challenge the scalability of traditional centralized scheduling. Building upon the SJA concept, this paper introduces JASDA-a novel paradigm that extends SJA from a largely centralized scheduling model toward a fully decentralized negotiation process. In JASDA, jobs actively generate and score feasible subjobs in response to scheduler-announced execution windows, while the scheduler performs policy-driven clearing that balances utilization, fairness, and temporal responsiveness. This bidirectional, iterative interaction embeds feedback, calibration, and probabilistic safety directly into the scheduling loop, enabling adaptive and transparent decision-making. By coupling principles from auction theory and online optimization with the temporal granularity of GPU workloads, JASDA provides a scalable foundation for market-aware and fairness-driven resource management-bridging theoretical scheduling models with practical deployment in modern MIG-enabled environments relevant to Artificial Intelligence and Agriculture 4.0. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_14599 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | JASDA: Introducing Job-Aware Scheduling in Scheduler-Driven Job Atomization Konopa, Michal Fesl, Jan ánek, Ladislav Ber Distributed, Parallel, and Cluster Computing D.4.1; C.4; C.1.4; D.1.3 The increasing complexity and temporal variability of workloads on MIG-enabled GPUs challenge the scalability of traditional centralized scheduling. Building upon the SJA concept, this paper introduces JASDA-a novel paradigm that extends SJA from a largely centralized scheduling model toward a fully decentralized negotiation process. In JASDA, jobs actively generate and score feasible subjobs in response to scheduler-announced execution windows, while the scheduler performs policy-driven clearing that balances utilization, fairness, and temporal responsiveness. This bidirectional, iterative interaction embeds feedback, calibration, and probabilistic safety directly into the scheduling loop, enabling adaptive and transparent decision-making. By coupling principles from auction theory and online optimization with the temporal granularity of GPU workloads, JASDA provides a scalable foundation for market-aware and fairness-driven resource management-bridging theoretical scheduling models with practical deployment in modern MIG-enabled environments relevant to Artificial Intelligence and Agriculture 4.0. |
| title | JASDA: Introducing Job-Aware Scheduling in Scheduler-Driven Job Atomization |
| topic | Distributed, Parallel, and Cluster Computing D.4.1; C.4; C.1.4; D.1.3 |
| url | https://arxiv.org/abs/2510.14599 |