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A PSO Algorithm-Based Task Scheduling in Cloud Computing

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Soft Computing: Theories and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 742))

Abstract

Cloud computing is one of the most acceptable emerging technologies, which involves the allocation and de-allocation of the computing resources using the Internet as the core technology to compute the tasks or jobs submitted by the users. Task scheduling is one of the fundamental issues in cloud computing and lots of efforts have been made to solve this problem. For the success of any cloud-based computing model, efficient task scheduling mechanism is always needed which, in turn, is responsible for the allocation of tasks to the available processing machines in such a manner that no machine is over- or under-utilized while executing them. Scheduling of tasks belongs to the category of NP-Hard problem. Through this paper, we are proposing the particle swarm optimization (PSO)-based task scheduling mechanism for the efficient distribution of the task among the virtual machines (VMs) in order to keep the overall response time minimum. The proposed algorithm is compared using the CloudSim simulator with the existing greedy and genetic algorithm-based task scheduling mechanism and results clearly shows that the PSO-based task scheduling mechanism clearly outperforms the others techniques which are taken into consideration.

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Author information

Authors and Affiliations

  1. Department of Physics & Computer Science, Dayalbagh Educational Institute, Agra, India

    Mohit Agarwal & Gur Mauj Saran Srivastava

Authors
  1. Mohit Agarwal
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  2. Gur Mauj Saran Srivastava
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Corresponding author

Correspondence to Mohit Agarwal .

Editor information

Editors and Affiliations

  1. Department of Physics, Amity School of Applied Sciences, Amity University Rajasthan, Jaipur, Rajasthan, India

    Kanad Ray

  2. Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Rajasthan, Jaipur, Rajasthan, India

    Tarun K. Sharma

  3. Department of Electronics and Communication Engineering, SEEC, Manipal University Jaipur, Jaipur, Rajasthan, India

    Sanyog Rawat

  4. Institute of Basic Science, Bundelkhand University, Jhansi, Uttar Pradesh, India

    R. K. Saini

  5. Advanced Key Technologies Division, Nano Characterization Unit, Surface Characterization Group, National Institute for Materials Science, Tsukuba, Ibaraki, Japan

    Anirban Bandyopadhyay

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Agarwal, M., Srivastava, G.M.S. (2019). A PSO Algorithm-Based Task Scheduling in Cloud Computing. In: Ray, K., Sharma, T., Rawat, S., Saini, R., Bandyopadhyay, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 742. Springer, Singapore. https://doi.org/10.1007/978-981-13-0589-4_27

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