Performance Comparison of Six Efficient Pure Heuristics for Scheduling Meta-tasks on Heterogeneous Distributed Environments

Scheduling is one of the core steps to efficiently exploit the capabilities of heterogeneous distributed computing systems and represents an NP-complete problem. Therefore, using meta-heuristic algorithms is a suitable approach in order to cope with its difficulty. In many meta-heuristic algorithms, generating individuals in the initial step has an important effect on the convergence behavior of the algorithm and final solutions. Using some pure heuristics for generating one or more near-optimal individuals in the initial step can improve the final solutions obtained by meta-heuristic algorithms. Pure heuristics may be used solitary for generating schedules in many real-world situations in which using the meta-heuristic methods are too difficult or inappropriate. Different criteria can be used for evaluating the efficiency of scheduling algorithms, the most important of which are makespan and flowtime. In this paper, we propose an efficient pure heuristic method and then we compare the performance with five popular heuristics for minimizing makespan and flowtime in heterogeneous distributed computing systems. We investigate the effect of these pure heuristics for initializing simulated annealing meta-heuristic approach for scheduling tasks on heterogeneous environments.