Deadline constraint heuristic-based genetic algorithm for workflow scheduling in cloud

Task scheduling and resource allocation are the key challenges of cloud computing. Compared with grid environment, data transfer is a big overhead for cloud workflows. So, the cost arising from data transfers between resources as well as execution costs must also be taken into account during scheduling based upon user's Quality of Service (QoS) constraints. In this paper, we present Deadline Constrained Heuristic based Genetic Algorithms (HGAs) to schedule applications to cloud resources that minimise the execution cost while meeting the deadline for delivering the result. Each workflow's task is assigned priority using bottom-level (b-level) and top-level (t-level). To increase the population diversity, these priorities are then used to create the initial population of HGAs. The proposed algorithms are simulated and evaluated with synthetic workflows based on realistic workflows. The simulation results show that our proposed algorithms have a promising performance as compared to Standard Genetic Algorithm (SGA).