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MCAMC: minimizing the cost and makespan of cloud service using non-dominated sorting genetic algorithm-II

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Abstract

The number of cloud users and their aspiration for completion of tasks at less energy consumption and operating cost are rapidly increasing. Hence, the authors of this paper aim to minimize the makespan and operating cost by optimally scheduling the tasks and allocating the resources of cloud service. The optimum task scheduling and resource allocation are obtained for each objective function using the simple genetic algorithm. Further, the non-dominated solutions of the dual objectives are obtained using the non-dominated sorting genetic algorithm-II, the most successful multi-objective optimization technique. A complex cloud service problem consisting of ten tasks, fifteen subtasks and fifteen heterogeneous resources is considered to investigate the proposed method. The numerical results obtained in the single objective and multi objective optimization problems show that the makespan and the operating cost are significantly reduced using the simple genetic algorithm and a wide range of non-dominated solutions are obtained in the multi-objective optimization problem, by which the cloud users shall be benefitted to choose the most appropriate solution based on the other design constraints they have.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, PSNA College of Engineering and Technology, Dindigul, India

    A Sathya Sofia

  2. Department of Mechanical Engineering, PSNA College of Engineering and Technology, Dindigul, India

    P Emmanuel Nicholas

  3. Department of Information Technology, PSNA College of Engineering and Technology, Dindigul, India

    P Ganeshkumar

Authors
  1. A Sathya Sofia
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  2. P Emmanuel Nicholas
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  3. P Ganeshkumar
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Correspondence to A Sathya Sofia.

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Sathya Sofia, A., Emmanuel Nicholas, P. & Ganeshkumar, P. MCAMC: minimizing the cost and makespan of cloud service using non-dominated sorting genetic algorithm-II. Sādhanā 44, 215 (2019). https://doi.org/10.1007/s12046-019-1200-3

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  • DOI: https://doi.org/10.1007/s12046-019-1200-3

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