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Scientific communities are motivated to schedule their large-scale data analysis workflows in heterogeneous cluster environments because of privacy and financial issues. In such environments containing considerably diverse resources, efficient resource allocation approaches are essential for reaching high performance. Accordingly, this research addresses the scheduling problem of workflows with bag-of-task form to minimize total runtime (makespan). To this aim, we develop a mixed-integer linear programming model (MILP). The proposed model contains binary decision variables determining which tasks should be assigned to which nodes. Also, it contains linear constraints to fulfill the tasks requirements such as memory and scheduling policy. Comparative results show that our approach outperforms related approaches in most cases. As part of the post-optimality analysis, some secondary preferences are imposed on the proposed model to obtain the most preferred optimal solution. We analyze the relaxation of the makespan in the hope of significantly reducing the number of consumed nodes. [ABSTRACT FROM AUTHOR]

Scientific communities are motivated to schedule the data-intensive scientific workflows in multi-cloud environments, where considerable diverse resources are provided by multiple clouds and resource limitation imposed by individual clouds is overcome. However, this scheduling involves two conflicting objectives: minimizing cost and makespan. In general, dealing with such conflicting criteria is a difficult task. But fortunately recent efficient methods for solving multi-objective optimization problems motivated us to provide a multi-objective model considering minimization of cost and makespan as objectives. For solving this model, we use different scalarization procedures such as weighted-sum, Benson's scalarization and weighted min–max under different scenarios. Moreover, we investigate the stability of obtained solutions and propose a new approach for determining the most stable solution related to weighted-sum and weighted min–max as post-optimality analysis. Results indicate that our proposed weighted-sum approach outperforms the previously developed methods in terms of hypervolume. [ABSTRACT FROM AUTHOR]

Given that multi-cloud environments contain considerably diverse resources, scheduling workflows in these environments significantly reduces financial costs and overcomes the resource limitations imposed by commercial cloud providers. Accordingly, this study addressed the problem of scientific workflow scheduling in multi-cloud settings under deadline constraint to minimize associated financial costs. To this end, we proposed integer linear programming models that can be solved in a reasonable time by available solvers. In a mathematical model, the objective of a problem and real and physical constraints or restrictions are formulated using exact mathematical functions. Such formulation enabled us to comprehensively understand the system under evaluation, consider secondary preferences and post-optimality analysis and apply useful revisions to inappropriately selected input data. We analyzed the treatment of optimal cost under variations in deadline and workflow size. As part of the post-optimality analysis, sensitivity analysis and deadline revision were implemented. Results indicated that our proposed approach outperforms previously developed methods in terms of financial cost reduction. [ABSTRACT FROM AUTHOR]