Meta-schedulers for grid computing based on multi-objective swarm algorithms

Job scheduling is a challenging task on grid environments because they must fulfill user requirements. Scientists often have deadlines and budgets for their experiments (set of jobs). But these requirements are in conflict with each other – cheaper resources are slower than the expensive ones. In this paper, we have implemented two multi-objective swarm algorithms. One of them is based on a biological behavior – Multi-Objective Artificial Bee Colony (MOABC) – and the other on physics – Multi-Objective Gravitational Search Algorithm (MOGSA). Multi-objective properties enhance the optimization of execution time and cost per experiment. These algorithms are evaluated regard to the standard and well-known multi-objective algorithm – Non-dominated Sorting Genetic Algorithm II (NSGA II) – in order to prove the goodness of our multi-objective proposals. Moreover, they are compared with real meta-schedulers as the Workload Management System (WMS) from the most used European grid middleware, gLite, and the Deadline Budget Constraint (DBC) from Nimrod-G, that takes into account the same requirements. Results show us that MOABC offers better results in all the cases using diverse workflows with dependent jobs over different grid environments.

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► We have implemented two multi-objective swarm algorithms, MOABC and MOGSA, for grid scheduling.
► These algorithms are evaluated regard to the standard and well-known multi-objective algorithm NSGA II.
► They are compared with real meta-schedulers as WMS and DBC.
► The experiments are carried out over different grid environments using diverse workflows with dependent jobs.
► MOABC offers better results in all the cases for both objectives, execution time and cost.