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Neural Inspired Ant Lion Algorithm for Resource Optimization in Cloud

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Sustainable Smart Cities

Part of the book series: Studies in Computational Intelligence ((SCI,volume 942))

Abstract

There are various task scheduling models that are in use currently, but the one which we will be focusing on is the ANN (Artificial Neural Network) based model. This model was set up to estimate the task execution status for resource allotment among the candidates. An ANN-based model makes use of various scheduling algorithms to find the best results possible in terms of quality of service (QoS), total cost, service satisfaction, etc. Through our paper, we are simulating various task scheduling algorithms in a virtual environment and comparing their efficiency based on the results we obtain from these simulations. While our focus will be on an emerging meta-heuristic optimization algorithm called the Ant lion Algorithm, we are also running simulations for the Whale Optimization algorithm, and the Genetic Algorithm. For the prediction and allocation of cloud resources we use the Ant Lion Optimization Algorithm. Artificial Neural Network (ANN) is used for resource allocation. We discuss the results that depicts we get better results compared to the existing methods with proper allocation of resources and minimal cost.

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Authors and Affiliations

  1. Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India

    Devansh Gulati, Mehul Gupta, Dinesh Kumar Saini & Punit Gupta

Authors
  1. Devansh Gulati
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  2. Mehul Gupta
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  3. Dinesh Kumar Saini
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  4. Punit Gupta
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Corresponding author

Correspondence to Punit Gupta .

Editor information

Editors and Affiliations

  1. Narsee Monjee Institute of Management Studies (NMIMS), School of Technology Management and Engineering, Chandigarh Campus, India

    Pradeep Kumar Singh

  2. Polish Academy of Sciences, Warsaw, Poland

    Marcin Paprzycki

  3. ENSIAS College of Engineering, Mohammed V University, Rabat, Morocco

    Mohamad Essaaidi

  4. Department of Computer Science and Engineering, Mississippi State University, Starkville, MS, USA

    Shahram Rahimi

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Gulati, D., Gupta, M., Saini, D.K., Gupta, P. (2023). Neural Inspired Ant Lion Algorithm for Resource Optimization in Cloud. In: Singh, P.K., Paprzycki, M., Essaaidi, M., Rahimi, S. (eds) Sustainable Smart Cities. Studies in Computational Intelligence, vol 942. Springer, Cham. https://doi.org/10.1007/978-3-031-08815-5_12

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