Budget Constrained Priority Based Genetic Algorithm for Workflow Scheduling in Cloud

Data transfer is a big overhead for cloud workflows due to pay-as-use business model of cloud environment. In such environment, 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 Priority based Genetic Algorithm BCHGA to schedule workflow applications to cloud resources that optimize the total cost of workflow within the user’s specified budget. 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 BCHGA. The proposed algorithm is simulated in java and evaluated with synthetic workflows based on realistic workflows from the different areas by considering the pricing model of cloud service provider like Amazon. The simulation results show that our proposed algorithm has a promising performance as compared to Standard Genetic Algorithm (SGA).