Data-Locality-Aware Task Assignment and Scheduling for Distributed Job Executions
<|reference_start|>Data-Locality-Aware Task Assignment and Scheduling for Distributed Job Executions: This paper investigates a data-locality-aware task assignment and scheduling problem aimed at minimizing job completion times for distributed job executions. Without prior knowledge of future job arrivals, we propose an optimal balanced task assignment algorithm (OBTA) that minimizes the completion time of each arriving job. We significantly reduce OBTA's computational overhead by narrowing the search space of potential solutions. Additionally, we extend an approximate algorithm known as water-filling (WF) and nontrivially prove that its approximation factor equals the number of task groups in the job assignment. We also design a novel heuristic, replica-deletion (RD), which outperforms WF. To further reduce the completion time of each job, we expand the problem to include job reordering, where we adjust the order of outstanding jobs following the shortest-estimated-time-first policy. Extensive trace-driven evaluations validate the performance and efficiency of the proposed algorithms.<|reference_end|>
@article{zhao2024data-locality-aware,
    title={Data-Locality-Aware Task Assignment and Scheduling for Distributed Job
  Executions},
    author={Hailiang Zhao, Xueyan Tang, Peng Chen, Jianwei Yin, Shuiguang Deng},
    journal={arXiv preprint arXiv:2407.08584},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08584},
    primaryClass={cs.DC}
}