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Bio-inspired Based Task Scheduling in Cloud Computing

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Machine Learning Paradigms: Theory and Application

Part of the book series: Studies in Computational Intelligence ((SCI,volume 801))

Abstract

Cloud computing meets numerous challenges at increasing number of users because the demand of resources sharing and usage are increased rapidly. Therefore, load balancing between resources is an important field for scheduling tasks to achieve better performance. In this chapter, a Hybrid Artificial Bee and Ant Colony optimization (H_BAC) load balancing algorithm is proposed. It depends on joining the important behavior of Ant Colony Optimization (ACO) such as discovering good solutions rapidly and Artificial Bee Colony (ABC) algorithm such as collective interaction of bees and sharing information by waggle dancing. The performance of the proposed algorithm is compared with ACO, ABC, and an existing hybrid algorithm. The simulation results show that H_BAC improves execution time, response time, makespan, resource utilization and standard deviation. This improvement reaches about 40% in the execution time and response time and 30% in the makespan over the other algorithms.

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Author information

Authors and Affiliations

  1. Electrical Engineering Department, Suez Canal University, Ismailia, Egypt

    Marwa Gamal & Basem Elhady

  2. Electrical Engineering Department, Port Said University, Port Said, 42523, Egypt

    Rawya Rizk

  3. Computers and Systems Engineering Department, Ain Shams University, Cairo, Egypt

    Hani Mahdi

Authors
  1. Marwa Gamal
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  2. Rawya Rizk
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  3. Hani Mahdi
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  4. Basem Elhady
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Corresponding author

Correspondence to Rawya Rizk .

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Editors and Affiliations

  1. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

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Gamal, M., Rizk, R., Mahdi, H., Elhady, B. (2019). Bio-inspired Based Task Scheduling in Cloud Computing. In: Hassanien, A. (eds) Machine Learning Paradigms: Theory and Application. Studies in Computational Intelligence, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-030-02357-7_14

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