Genetic-based algorithms applied to a workflow scheduling algorithm with security and deadline constraints in clouds

There have been a number of metaheuristic scheduling techniques for cloud described in the literature, as well as their applications. The efficiency of metaheuristic techniques has been established in a wide range of workflow scheduling algorithms for cloud environments. However, it is still unknown whether the metaheuristic that is chosen, is suitable for solving the problem of optimization. This paper examines the effect of both Particle Swarm Optimization (PSO) and Genetic-based algorithms (GA) on attempts to optimize workflow scheduling. A security and cost-aware workflow scheduling algorithm was selected to evaluate the performance of the metaheuristics. Three algorithms were evaluated in three real-world workflows with a risk rate constraint that ranged between 0 and 1 with a 0.1 step. The findings indicate that GA-based algorithms significantly outperformed the PSO both in term of cost-effectiveness and response time.