A Cost-Effective Deadline-Constrained Dynamic Scheduling Algorithm for Scientific Workflows in a Cloud Environment - 2018


Cloud computing, a distributed computing paradigm, allows delivery of IT resources over the Web and follows the pay-as-you-go billing model. Workflow scheduling is one in every of the foremost challenging issues in cloud computing. Although, workflow scheduling on distributed systems like grids and clusters are extensively studied, however, these solutions don't seem to be viable for a cloud atmosphere. It's as a result of, a cloud environment differs from different distributed setting in two major ways that: on-demand resource provisioning and pay-as-you-go pricing model. Thus, to achieve the true benefits of workflow orchestration onto cloud resources novel approaches which will capitalize the benefits and address the challenges specific to a cloud environment desires to be developed. This work proposes a dynamic price-effective deadline-constrained heuristic algorithm for scheduling a scientific workflow in a very public cloud. The proposed technique aims to use the advantages offered by cloud computing whereas taking under consideration the virtual machine (VM) performance variability and instance acquisition delay to spot a simply-in-time schedule of a deadline constrained scientific workflow at lesser costs. Performance evaluation on some well-known scientific workflows exhibit that the proposed algorithm delivers better performance in comparison to the current state-of-the-art heuristics.

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