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Integer linear programming-based multi-objective scheduling for scientific workflows in multi-cloud environments

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Abstract

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.

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Notes

  1. https://aws.amazon.com/ec2.

  2. https://docs.microsoft.com/en-us/azure/azure-subscription-service-limits#virtual-machines-limits.

  3. The Epigenome workflow with 4000 tasks has eight levels. Assume the execution time of each task is in seconds on VM with 1 CCU. The level 5 has 495 tasks with execution time = 9635.01 s. For executing the tasks in level 5 on VMs of Azure with performance 9 CCU, the 56 VMs are needed in which these tasks are executed in three bags with six tasks and 53 bags with nine tasks.

  4. https://www.rightscale.com/lp/state-of-the-cloud.

  5. CloudHarmony Compute Unit.

  6. http://blog.cloudharmony.com/2010/09/benchmarking-of-ec2s-new-cluster.html.

  7. https://confluence.pegasus.isi.edu/display/pegasus/WorkflowGenerator.

  8. ILOG CPLEX. http://www.ilog.com/products/cplex.

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Authors and Affiliations

  1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Somayeh Mohammadi

  2. Department of Mathematics, Razi University, Kermanshah, Iran

    Latif PourKarimi

  3. Department of Computer Engineering, Amir Kabir University of Technology, Tehran, Iran

    Hossein Pedram

Authors
  1. Somayeh Mohammadi
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  2. Latif PourKarimi
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  3. Hossein Pedram
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Correspondence to Latif PourKarimi.

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Mohammadi, S., PourKarimi, L. & Pedram, H. Integer linear programming-based multi-objective scheduling for scientific workflows in multi-cloud environments. J Supercomput 75, 6683–6709 (2019). https://doi.org/10.1007/s11227-019-02877-8

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