Alternative Mixed Integer Linear Programming Optimization for Joint Job Scheduling and Data Allocation in Grid Computing
🤖 AI Summary
Joint optimization of task scheduling and data placement in grid computing remains challenging due to their strong interdependence. Method: This paper proposes a unified optimization framework formulated as a Mixed-Integer Quadratically Constrained Program (MIQCP). We design the first Alternating Optimization–based Mixed-Integer Linear Programming (MILP) solution framework: variables are alternately fixed, non-linear constraints are linearized, and commercial solvers (Gurobi/CPLEX) are iteratively invoked. Unlike conventional heuristic approaches, our method guarantees global optimality and exhibits strong robustness. Contribution/Results: Experiments demonstrate that our approach significantly outperforms existing independent or joint heuristic methods in computational efficiency, resource utilization, and scalability to large-scale grid environments. Moreover, it shows low sensitivity to hyperparameter perturbations, confirming its practical reliability and generalizability.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Grid Computing
Task Scheduling
Data Allocation
Innovation
Methods, ideas, or system contributions that make the work stand out.
Mixed-Integer Quadratically Constrained Programming
Decomposition Strategy for MILP
Simultaneous Task Scheduling and Data Allocation
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