Multi-level hierarchic genetic-based scheduling of independent jobs in dynamic heterogeneous grid environment
Task scheduling and resource allocation are the key rationale behind the computational grid. Distributed resource clusters usually work in different autonomous domains with their own access and security policies that have a great impact on the successful task execution across the domain boundaries. Heuristics and metaheuristics are the effective technologies for scheduling in grids due to their ability to deliver high quality solutions in reasonable time. In this paper, we develop a Hierarchic Genetic Scheduler (HGS-Sched) for improving the effectiveness of the single-population genetic-based schedulers in the dynamic grid environment. The HGS-Sched enables a concurrent exploration of the solution space by many small dependent populations. We consider a bi-objective independent batch job scheduling problem with makespan and flowtime minimized in hierarchical mode (makespan is a dominant criterion). The empirical results show the high effectiveness of the proposed method in comparison with the mono-population and hybrid genetic-based schedulers.
scheduling computational grid genetic algorithm hierarchic genetic strategy expected time to compute matrix model