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A Comprehensive Review of Task Scheduling Problem in Cloud Computing: Recent Advances and Comparative Analysis

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New Horizons for Fuzzy Logic, Neural Networks and Metaheuristics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1149))

Abstract

Cloud computing has become one of the most studied information technologies by researchers since in recent years it has emerged as a dominant paradigm for the delivery of scalable and on-demand computing resources over the Internet. Task scheduling is a crucial aspect of cloud computing, it plays a vital role in optimizing resource utilization, minimizing execution time, and improving overall system performance. This document presents an exhaustive review of the Task Scheduling Problem in Cloud Computing, focusing on the most recent and representative specialized literature published in the last five years to date. We introduce the concept of cloud computing and its architectural components, highlighting the importance of cloud computing in meeting the increasing demands of modern applications. As well as the fundamental differences between traditional Task Scheduling and Cloud Task Scheduling approaches, emphasizing the unique challenges and opportunities presented by the cloud computing environment. The review then delves into the various strategies and techniques proposed by researchers to address the problem of task scheduling in the cloud. It covers both heuristic and metaheuristic approaches. The paper critically examines state-of-the-art methods, highlighting their strengths, limitations, and comparative performance based on published results. In addition, we include the performance metrics and benchmarks most used in the literature to evaluate task scheduling algorithms in the cloud. We look at metrics such as performance, resource utilization, energy efficiency, and load balancing, as well as provide insight into their importance and applicability in different scenarios. Overall, this paper provides a comprehensive overview of the Task Scheduling Problem in Cloud Computing, shedding light on recent advances in heuristic and metaheuristic approaches. It serves as a valuable resource for researchers and practitioners seeking to understand the state-of-the-art, identify research gaps, and explore potential directions for future work in this dynamic and evolving field.

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Author information

Authors and Affiliations

  1. Tecnológico Nacional de México, Instituto Tecnológico de Ciudad Madero, Cd. Madero, Tamaulipas, México

    Jessica González-San-Martín, Laura Cruz-Reyes, Claudia Gómez-Santillán & Héctor Fraire-Huacuja

  2. CONACyT Research Fellow at Graduate Program Division, Tecnológico Nacional de México, Instituto Tecnológico de Ciudad Madero, Cd. Madero, Tamaulipas, México

    Nelson Rangel-Valdez

  3. Computer Science Engineering Department, University of Cadiz, Puerto Real, Cadiz, Spain

    Bernabé Dorronsoro

  4. Artificial Intelligence Research Center, Universidad Veracruzana, Xalapa-Enriquez, Veracruz, México

    Marcela Quiroz-Castellanos

Authors
  1. Jessica González-San-Martín
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  3. Claudia Gómez-Santillán
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  4. Héctor Fraire-Huacuja
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  7. Marcela Quiroz-Castellanos
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Corresponding author

Correspondence to Jessica González-San-Martín .

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Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  2. Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

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González-San-Martín, J. et al. (2024). A Comprehensive Review of Task Scheduling Problem in Cloud Computing: Recent Advances and Comparative Analysis. In: Castillo, O., Melin, P. (eds) New Horizons for Fuzzy Logic, Neural Networks and Metaheuristics. Studies in Computational Intelligence, vol 1149. Springer, Cham. https://doi.org/10.1007/978-3-031-55684-5_20

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