Alternative Mixed Integer Linear Programming Optimization for Joint Job Scheduling and Data Allocation in Grid Computing

TL;DR

This paper introduces a novel MILP-based method for joint job scheduling and data allocation in grid computing, outperforming heuristics and demonstrating robustness across various environments.

Contribution

The paper proposes a new MILP-based iterative approach for joint optimization in grid computing, addressing nonlinearity and improving performance over existing heuristics.

Findings

Significantly outperforms existing heuristic methods

Demonstrates robustness across different grid sizes

Effective in various grid environments

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.