Abstract Cloud Computing provides a promising platform for executing large scale workflow applications with enormous computational resources to offer on-demand services. Tasks in a workflow may need different type of computing resources such as storage, compute and memory type. However, inappropriate selection of these resources may lead to higher makespan and resource wastage. In this paper, we propose an effective two-phase algorithm for provisioning of cloud resources for workflow applications by using its structural features to minimize makespan and resource wastage. The proposed approach considers the nature of the tasks which may be compute intensive, memory intensive or storage intensive. We assume a realistic cloud model similar to Amazon EC2 that provides virtual machines for different types of workloads. Most importantly, the workflow model used in our approach is assumed to contain limited information about the task which is applicable for real situation. The performance of the proposed work is measured using five benchmark scientific workflows. The simulation results show that the proposed approach outperforms two existing algorithms for all these workflows.
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