Alternative Mixed Integer Linear Programming Optimization for Joint Job Scheduling and Data Allocation in Grid Computing
Abstract
This paper presents a novel approach to the joint optimization of job scheduling and data allocation in grid computing environments. We formulate this joint optimization problem as a mixed integer quadratically constrained program. To tackle the nonlinearity in the constraint, we alternatively fix a subset of decision variables and optimize the remaining ones via Mixed Integer Linear Programming (MILP). We solve the MILP problem at each iteration via an off-the-shelf MILP solver. Our experimental results show that our method significantly outperforms existing heuristic methods, employing either independent optimization or joint optimization strategies. We have also verified the generalization ability of our method over grid environments with various sizes and its high robustness to the algorithm hyper-parameters.
Cite
@article{arxiv.2502.00261,
title = {Alternative Mixed Integer Linear Programming Optimization for Joint Job Scheduling and Data Allocation in Grid Computing},
author = {Shengyu Feng and Jaehyung Kim and Yiming Yang and Joseph Boudreau and Tasnuva Chowdhury and Adolfy Hoisie and Raees Khan and Ozgur O. Kilic and Scott Klasky and Tatiana Korchuganova and Paul Nilsson and Verena Ingrid Martinez Outschoorn and David K. Park and Norbert Podhorszki and Yihui Ren and Frederic Suter and Sairam Sri Vatsavai and Wei Yang and Shinjae Yoo and Tadashi Maeno and Alexei Klimentov},
journal= {arXiv preprint arXiv:2502.00261},
year = {2025}
}