Scheduling of scientific workflows using a chaos-genetic algorithm

The main idea of developing Grid is to make effective use of the computation power distributed all over the world. Economical issues are the most vital motivations of resource owners to share their services. This means that users are required to pay for access to services based on their usage and level of QoS they need. Therefore total cost of executing an application is becoming one of the most important parameters in evaluating QoS, which users tend to decrease. Since, many applications are described in the form of dependent tasks, scheduling of these workflows has become a major challenge in grid environment. In this paper, a novel genetic algorithm called chaos-genetic algorithm is used to solve the scheduling problem considering both user’s budget and deadline. Due to the nature of chaotic variables such as pseudo-randomness, ergodicity and irregularity, the evolutional process of chaos-genetic algorithm makes individuals of subgenerations distribute ergodically in the defined space and circumvents the premature of the individuals of traditional genetic algorithms (TGA). The results of applying chaos-genetic scheduling algorithm (CGS) showed greater performances of CGS compared to traditional genetic algorithm (TGS) on both balanced and unbalanced workflows.