JASDA: Introducing Job-Aware Scheduling in Scheduler-Driven Job Atomization

Abstract

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.