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A Comparative Study of Meta-Heuristic-Based Task Scheduling in Cloud Computing

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Artificial Intelligence and Sustainable Computing

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

The massive advances in web, mobile and computer technologies and its users are exponentially growing. Now the era has come where every user is very much conscious about the word “cloud computing”. The users are rolling in cloud services (storage space, computational power and standalone applications). Hence, the cloud service providers (CSP) are concerned about the quality of service (QoS) to their clients. To make it into action, task scheduling was introduced. The principle goal of task scheduling is to carry out successfully the objectives of both server and its clients. As the traditional task scheduling is not enough to attain the best performance. So, meta-heuristic techniques are required, which can produce a solution close to optimal. This optimal solution decides the mapping of tasks on resources and comes up with results that match the desirable objectives. This paper presented a comparative investigation of meta-heuristic centric task scheduling algorithms, such as ant colony optimization (ACO), particle swarm optimization (PSO), gray wolf optimization (GWO), whale optimization algorithm (WOA) and flower pollination algorithm (FPA) which are being used by many researchers for developing new techniques from last decade.

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Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, Guru Jambheshwar University of Science & Technology, Hisar, India

    Ravi Kumar & Jai Bhagwan

Authors
  1. Ravi Kumar
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  2. Jai Bhagwan
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Madhav Institute of Technology and Science, Gwalior, India

    Hari Mohan Dubey

  2. Department of Electrical Engineering, Madhav Institute of Technology and Science, Gwalior, India

    Manjaree Pandit

  3. Department of Electrical Engineering, Madhav Institute of Technology and Science, Gwalior, India

    Laxmi Srivastava

  4. Department of Electrical Engineering, Indian Institute of Technology Delhi, Delhi, India

    Bijaya Ketan Panigrahi

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Kumar, R., Bhagwan, J. (2022). A Comparative Study of Meta-Heuristic-Based Task Scheduling in Cloud Computing. In: Dubey, H.M., Pandit, M., Srivastava, L., Panigrahi, B.K. (eds) Artificial Intelligence and Sustainable Computing. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-1220-6_12

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