Alternative mixed integer linear programming optimization for joint job scheduling and data allocation in grid computing

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 setting.

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Publication Details

Journal
Future Generation Computer Systems
Published
2025-08-18
DOI
https://doi.org/10.1016/j.future.2025.108075
Primary Topic
Distributed and Parallel Computing Systems
Type
article
Field-Weighted Citation Impact
0.00

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article

Alternative mixed integer linear programming optimization for joint job scheduling and data allocation in grid computing

Yihui Ren, Tatiana Korchuganova, Ozgur O. Kilic, Wei Yang et al.
Future Generation Computer Systems
Distributed and Parallel Computing Systems
article

Alternative mixed integer linear programming optimization for joint job scheduling and data allocation in grid computing

Yihui Ren, Tatiana Korchuganova, Ozgur O. Kilic, Wei Yang, Raees Khan, Shinjae Yoo, David S. Park, T. Maeno, Norbert Podhorszki, Adolfy Hoisie, P. Nilsson, Sairam Sri Vatsavai, Frédéric Suter, A. Klimentov, Shengyu Feng, Jaehyung Kim, Yiming Yang, Verena Ingrid Martinez Outschoorn, Joseph Boudreau, Scott Klasky, T. Chowdhury
article en

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 setting.

Future Generation Computer SystemsVol. 175
Oak Ridge National Laboratory (US), University of Pittsburgh (US), Yonsei University (KR), Brookhaven National Laboratory (US), University of Massachusetts Amherst (US), SLAC National Accelerator Laboratory (US), Carnegie Mellon University (US)
U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research
Decent work and economic growth
Openalex Percentile: Top 77%
Distributed and Parallel Computing Systems
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