AbstractWorkflow scheduling involves mapping large tasks onto cloud resources to improve scheduling efficiency. This has attracted the interest of many researchers, who devoted their time and resources to improve the performance of scheduling in cloud computing. However, scientific workflows are big data applications, hence the executions are expensive and time consuming. In order to address this issue, we have extended our previous work "Cost Optimised Heuristic Algorithm (COHA)" and presented

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... a novel workflow scheduling algorithm named Multi-Objective Workflow Optimization Strategy (MOWOS) to jointly reduce execution cost and execution makespan. MOWOS employs tasks splitting mechanism to split large tasks into sub-tasks to reduce their scheduling length. Moreover, two new algorithms called MaxVM selection and MinVM selection are presented in MOWOS for task allocations. The design purpose of MOWOS is to enable all tasks to successfully meet their deadlines at a reduced time and budget. We have carefully tested the performance of MOWOS with a list of workflow inputs. The simulation results have demonstrated that MOWOS can effectively perform VM allocation and deployment, and well handle incoming streaming tasks with a random arriving rate. The performance of the proposed algorithm increases significantly in large and extra-large workflow tasks than in small and medium workflow tasks when compared to the state-of-art work. It can greatly reduce cost by 8%, minimize makespan by 10% and improve resource utilization by 53%, while also allowing all tasks to meet their deadlines.