Independent Job Scheduling is one of the most useful versions of scheduling in Grid systems. It aims at computing efficient and optimal mapping of jobs and/or applications submitted by independent users to the Grid resources. Besides traditional restrictions, mappings of jobs to resources should be computed under high degree of heterogeneity of resources, the large scale and the dynamics of the system. In view of the challenging nature of the problem, heuristic and metaheuristic approaches are the most feasible candidate methods due to their ability to deliver high quality solutions in reasonable computing time. One class of meta-heuristics, less explored for the problem, is that of Hierarchic Genetic Strategy (HGS), a variant of Genetic Algorithms (GAs) that distinguishes for its capability of concurrent search of the solution space. In this work we present an implementation of HGS for Independent Job Scheduling in dynamic Grid environments. We consider the bi-objective version of the problem in which makespan and flowtime are simultaneously optimized. Building on our previous work, we improve the HGS scheduling strategy for the problem, by enhancing its main branching operation. The resulting HGS-based scheduler is evaluated under the heterogeneity, the large scale and dynamics conditions using a Grid simulator. The experimental study showed that the HGS implementation outperforms existing GA-based schedulers proposed in the literature.
Implementation of genetic algorithm by structural hierarchical population for tasks scheduling in grid computing is described in Kolodziej et al. REF .
Enhancing the genetic-based scheduling in computational grids by a structured hierarchical population
Future Gener. Comput. Syst.
Future Gener. Comput. Syst.
Computer Science