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Meta-heuristic Algorithms to Optimize Two-Stage Task Scheduling in the Cloud

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Abstract

The development of cloud technology has led to more resources being made available on demand. The recent spike in the cloud service demand requires further improvement of cloud-based data centers. As a result, effective task scheduling is necessary for cloud computing. To ensure equal load distribution to systems with increased scalability and performance, data centers must have a suitable task scheduling mechanism. An efficient task scheduling strategy tries to optimize output, decrease response time, use fewer resources, and conserve energy by matching the appropriate resources to the workload. The suggested technique employs a two-stage task scheduling approach. In the first stage, virtual machines are created by performing classification and clustering techniques based on historical task data, and in the second stage, a hybrid ant genetic algorithm is used to schedule the best VM for the task by combining the advantages of genetic algorithms with pheromone values from ant colony algorithms. The suggested approach accomplished cost-effective task scheduling with a short make-span.

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Data Availability

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors acknowledged the S.R.M Institute of Science and Technology, Kattankulathur (Campus) and Ramapuram (Campus), Chennai, and Sathyabama Institute of Science and Technology, Chennai, India, for supporting the research work by providing the facilities.

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No funding was received for this research.

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Authors and Affiliations

  1. S.R.M Institute of Science and Technology, Kattankulathur, Chennai, India

    K. Deepa Thilak & K. Kalaiselvi

  2. Sathyabama Institute of Science and Technology, Chennai, India

    K. Lalitha Devi

  3. S.R.M Institute of Science and Technology, Ramapuram, Chennai, India

    C. Shanmuganathan

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  1. K. Deepa Thilak
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  2. K. Lalitha Devi
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  3. C. Shanmuganathan
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  4. K. Kalaiselvi
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This research endeavor was made possible by the collaboration and contributions of all authors.

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Correspondence to K. Deepa Thilak.

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This article is part of the topical collection “Advances in Computational Approaches for Image Processing, Wireless Networks, Cloud Applications and Network Security” guest edited by P. Raviraj, Maode Ma and Roopashree H R.

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Thilak, K.D., Devi, K.L., Shanmuganathan, C. et al. Meta-heuristic Algorithms to Optimize Two-Stage Task Scheduling in the Cloud. SN COMPUT. SCI. 5, 122 (2024). https://doi.org/10.1007/s42979-023-02449-x

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