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Cloud computing becomes a promising technology to reduce computation cost by providing users with elastic resources and application-deploying environments as a pay-per-use model. More scientific workflow applications have been moved or are being migrated to the cloud. Scheduling workflows turns to the main bottleneck for increasing resource utilization and quality of service (QoS) for users. This work formulates workflow scheduling as multiobjective optimization problems and proposes a Scoring and Dynamic Hierarchy-based NSGA-II (Nondominated Sorting Genetic Algorithm II), called SDHN for short, to minimize both makespan and cost of workflow execution. First, a scoring criterion is developed to calculate the total score for each individual during population updating, which is used as a quantitative index to evaluate the dominance degree of individuals among the whole population. Hence, SDHN can distinguish individuals within the same dominance level and target its search toward the directions of elite solutions as their different dominance degrees and accordingly improve search efficiency. Second, a population-based dynamic hierarchical structure (HS) and its evolutionary rules are presented to update HS by comparing each child with all parental individuals from bottom to up until finding a proper dominant level. Since traversing all HS levels is not needed in most cases, the number of individual comparisons is reduced and SDHN’s updating efficiency is greatly improved, especially for large-scale and complex applications. Third, to guarantee its converging to the near-optimal solutions, adaptive adjustment strategies (AASs) are designed to prevent the search from falling into local optima or diverging by checking the number of individuals at the highest HS level and then modifying the relevant genetic operations to guide the evolutionary process to approach the global Pareto Front. Extensive experiments are conducted to verify SDHN, and the results show that it outperforms the existing algorithms in the quality and diversity of resulting solutions as well as convergence time. Note to Practitioners—Most scientific applications are computation and/or data-intensive and need large-scale or high-performance resources for their execution. More and more scientists use workflows to manage their applications, but how to efficiently run them in the cloud is a big challenge due to their large scale as well as the dynamic characteristics of the elastic and heterogeneous cloud resources. In this article, we develop a novel multiobjective optimization technique for workflow scheduling such that the makespan and cost can be minimized simultaneously. A scoring criterion, dynamic hierarchical structure and its evolutionary rules, and adaptive adjustment strategies are designed to cooperate with each other and increase the search ability and efficiency of the original and widely used NSGA-II. Adequate experiments are conducted to verify the proposed method’s performance, and the experimental results show that it can provide more near-optimal solutions than the existing methods. It can be readily applied for implementing more efficient and effective cloud data centers to execute large-scale scientific workflows. [ABSTRACT FROM AUTHOR]

In the literature, most scheduling models are developed using mathematical methods such as programming models or graphic models like Petri net. Although they are efficient in computing, they are difficult to build and understand by industry users. In order to make the scheduling model more capable and practical for industrial use, a new scheduling model based on the workflow management technique (SMWMT) is proposed here. It is a process-oriented compound model that features various process constraints definitions, activity behaviour descriptions, flexible resource definition and mapping, and graphic presentations that offer intuitional understandings to users. The corresponding scheduling system based on workflow simulation is provided. A cost-oriented iterative algorithm is proposed to enhance the scheduling performance. The example of the model and the simulation results show that the proposed model and scheduling method are effective. [ABSTRACT FROM AUTHOR]

Nowadays, many scientific applications are deployed in the cloud to execute at a lower cost. However, the growing scale of workflows makes scheduling problems challenging. To minimize the workflow execution cost under deadline constraints, this article proposes a Mutation and Dynamic Objective-based Farmland Fertility (MDO-FF) algorithm for obtaining a near-optimal solution within a relatively shorter time. A Dynamic Objective Strategy (DOS) is introduced to accelerate the convergence speed, while a multi-swarm evolutionary approach and mutation strategies are incorporated to enhance the search diversity and help to escape from local optima. By seeking new potential solutions and searching in its corresponding neighborhoods, our proposed MDO-FF can make a good trade-off between exploration and exploitation. Extensive experiments are conducted on well-known scientific workflows with different types and sizes. The experimental results demonstrate that in most cases, our MDO-FF outperforms the existing algorithms in terms of constraint satisfiability and solution quality. • Applying the Farmland Fertility algorithm to address workflow scheduling problems. • Introducing a mutation operator to modify the population updating mechanism. • Developing a multi-swarm evolutionary approach with different searching strategies. • Adopting a Dynamic Objective Strategy to efficiently search for feasible solutions. [ABSTRACT FROM AUTHOR]