Evaluation and Comparison of Integer Programming Solvers for Hard Real-Time Scheduling

Abstract

In order to obtain a feasible schedule of a hard real-time system, heuristic based techniques are the solution of choice. In the last few years, optimization solvers have gained attention from research communities due to their capability of handling large number of constraints. Recently, some works have used integer linear programming (ILP) for solving mono processor scheduling of real-time systems. In fact, ILP is commonly used for static scheduling of multiprocessor systems. However, two main solvers are used to solve the problem indistinctly. But, which one is the best for obtaining a schedulable system for hard real-time systems? This paper makes a comparison of two well-known optimization software packages (CPLEX and GUROBI) for the problem of finding a feasible schedule on monoprocessor hard real-time systems.

A comparison among optimization software to solve bi-objective sectorization problem

“…Luppold et al [6] investigate the effects of different types of variables, constraints, and logical expressions on the performance of solvers. Describing the complexity of hard real-time systems with large numbers of constraints, Guasque and Balbastre [7] emphasize the importance of heuristic methods to obtain feasible solutions for them. They also make a comparison between CPLEX and Gurobi.…”

“…Almost all optimization software and solvers have evolved considerably over time, but they still have significant differences [1] . Therefore, in the literature, there are studies comparing solvers [2] , [3] , [4] , [5] , [6] , [7] . Likewise, some studies compare the evolutionary algorithm (EA) with solvers and discuss the advantages and disadvantages [8] , [9] , [10] .…”