Mathematica

The dynamic nature of grid resources and the demands of users produce complexity in the grid scheduling problem that cannot be addressed by deterministic algorithms with polynomial complexity. One of the best methods for grid scheduling is the genetic algorithm (GA); the simple and parallel features of this algorithm make it applicable to several optimization problems. A GA searches the problem space globally and is unable to search locally. Therefore, scholars have investigated combining GAs with other meta-heuristic methods to resolve the local search problem.  This is the focus of the present contribution, where we have developed a new hybrid scheduling algorithm that combines a GA and the gravitational emulation local search (GELS) algorithm denotes GGA. The noteworthy feature of the proposed optimal scheduler is that it decreases run-time and the number of submitted tasks whose deadlines are missed. A comparison of the performance of our proposed joint optimal scheduler to similar methods shows that it produces more optimal computation time.