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Data-Intensive Scientific Workflow Scheduling Based on Genetic Algorithm in Cloud Computing

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Artificial Intelligence and Its Applications (AIAP 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 413))

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Abstract

Cloud Computing is increasingly recognized as a new way to use on-demand, computing, storage and network services in a transparent and efficient way. Cloud Computing environment consists of large customers requesting for cloud resources. Nowadays, task scheduling problem and data placement are the current research topic in cloud computing. In this work, a new technique for task scheduling and data placement are proposed based on genetic algorithm to fulfill a final goal such as minimizing total workflow response time. The scheduling of scientific workflows is considered to be an NP-complete problem, i.e. a problem not solvable within polynomial time with current resources The performance of this proposed algorithm has been evaluated using CloudSim toolkit, Simulation results show the effectiveness of the proposed algorithm in comparison with well-known algorithms such as genetic algorithm with Random data placement.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Faculty of Technology, University of Saida Dr Moulay Tahar, Saida, Algeria

    Siham Kouidri & Chaima Kouidri

  2. Department of Computer Science, Mustapha ISTAMBOULI University of Mascara, Mascara, Algeria

    Siham Kouidri & Chaima Kouidri

Authors
  1. Siham Kouidri
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  2. Chaima Kouidri
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Correspondence to Siham Kouidri .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Echahid Hamma Lakhdar, El-Oued, Algeria

    Brahim Lejdel

  2. Department of Industrial and Information Engineering and Economics, University of L'Aquila, L'Aquila, Italy

    Eliseo Clementini

  3. Department of Computer Science, Umm Al-Qura University, Makkah, Saudi Arabia

    Louai Alarabi

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Kouidri, S., Kouidri, C. (2022). Data-Intensive Scientific Workflow Scheduling Based on Genetic Algorithm in Cloud Computing. In: Lejdel, B., Clementini, E., Alarabi, L. (eds) Artificial Intelligence and Its Applications. AIAP 2021. Lecture Notes in Networks and Systems, vol 413. Springer, Cham. https://doi.org/10.1007/978-3-030-96311-8_49

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