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MOPT: list-based heuristic for scheduling workflows in cloud environment

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Abstract

Cloud computing is a popular and widely adopted computing platform for the execution of scientific workflows as it provides flexible infrastructure and offers access to collection of autonomous heterogeneous resources. Effective scheduling of computationally complex workflows which contain many interconnected tasks is a complex problem and becomes more challenging in cloud environment. Optimal solutions can be obtained by considering not only the heterogeneity of computation costs involved, but also by taking into account the communication costs among the tasks in a way that schedule length of the application is reduced. In this paper, we propose a list scheduling heuristic, namely minimal optimistic processing time (MOPT), with optimized duplication approach. The additional feature is introduced for the entry task and is applied only in scenarios in which duplication is more practical and effective. The prioritization phase of the proposed work is based on an optimistic processing time matrix that is used for ranking of the tasks. The algorithm has same time complexity as state-of-the-art existing algorithms, but notable improvements are acquired in terms of makespan and other performance evaluation parameters. Extensive experimental analysis of the proposed algorithm is carried out using synthesized graphs and graphs from the real-world applications. The results prove that MOPT achieves quality schedules with reduced makespans. As communication cost among the tasks grows higher, performance of the proposed algorithm becomes more effective, thus providing the evidence that the MOPT algorithm is well-suited for communication-intensive applications.

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Authors and Affiliations

  1. Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan

    Samia Ijaz & Ehsan Ullah Munir

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  1. Samia Ijaz
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  2. Ehsan Ullah Munir
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Correspondence to Samia Ijaz.

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Ijaz, S., Munir, E.U. MOPT: list-based heuristic for scheduling workflows in cloud environment. J Supercomput 75, 3740–3768 (2019). https://doi.org/10.1007/s11227-018-2726-6

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