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AI-driven job scheduling in cloud computing: a comprehensive review.

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Title: AI-driven job scheduling in cloud computing: a comprehensive review.
Authors: Sanjalawe, Yousef, Al-E'mari, Salam, Fraihat, Salam, Makhadmeh, Sharif
Source: Artificial Intelligence Review; Jul2025, Vol. 58 Issue 7, p1-113, 113p
Subject Terms: IMAGE processing software, ARTIFICIAL intelligence, EVIDENCE gaps, TECHNOLOGICAL innovations, COMMUNICATION infrastructure
Abstract: The demand for efficient job scheduling in cloud computing has grown significantly with the rise of dynamic and heterogeneous cloud environments. While effective in simpler systems, traditional scheduling algorithms fail to meet the complex requirements of modern cloud infrastructures. These limitations motivate the need for AI-driven solutions that offer adaptability, scalability, and energy efficiency. This paper comprehensively reviews AI-based job scheduling techniques, addressing several key research gaps in current approaches. The existing methods face challenges such as resource heterogeneity, energy consumption, and real-time adaptability in multi-cloud systems. Accordingly, the support of AI-based job scheduling in cloud computing is summarized here toward machine learning, optimization techniques, heuristic techniques, and hybrid AI models. This paper pointedly underlines the strengths and weaknesses of various approaches through deep comparative analysis and focuses on how AI will overcome traditional algorithm shortcomings. Is worth noticing that several important improvements this kind of AI-driven model provides, for example, in resource allocation, cost efficiency, energy consumption, and complex dependencies between jobs and system faults. In the end, AI-driven job scheduling seems to be a promising avenue toward effectively responding to the booming demands of cloud infrastructures. Future research should concentrate on three major outlooks: scalability, better integration of AI with traditional scheduling methods, and the use of other emerging technologies like edge computing and blockchain for better optimization of cloud-based job scheduling. The paper underscores the need for more adaptive, secure, and energy-efficient scheduling frameworks to meet the evolving challenges of cloud environments. [ABSTRACT FROM AUTHOR]
Copyright of Artificial Intelligence Review is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: The demand for efficient job scheduling in cloud computing has grown significantly with the rise of dynamic and heterogeneous cloud environments. While effective in simpler systems, traditional scheduling algorithms fail to meet the complex requirements of modern cloud infrastructures. These limitations motivate the need for AI-driven solutions that offer adaptability, scalability, and energy efficiency. This paper comprehensively reviews AI-based job scheduling techniques, addressing several key research gaps in current approaches. The existing methods face challenges such as resource heterogeneity, energy consumption, and real-time adaptability in multi-cloud systems. Accordingly, the support of AI-based job scheduling in cloud computing is summarized here toward machine learning, optimization techniques, heuristic techniques, and hybrid AI models. This paper pointedly underlines the strengths and weaknesses of various approaches through deep comparative analysis and focuses on how AI will overcome traditional algorithm shortcomings. Is worth noticing that several important improvements this kind of AI-driven model provides, for example, in resource allocation, cost efficiency, energy consumption, and complex dependencies between jobs and system faults. In the end, AI-driven job scheduling seems to be a promising avenue toward effectively responding to the booming demands of cloud infrastructures. Future research should concentrate on three major outlooks: scalability, better integration of AI with traditional scheduling methods, and the use of other emerging technologies like edge computing and blockchain for better optimization of cloud-based job scheduling. The paper underscores the need for more adaptive, secure, and energy-efficient scheduling frameworks to meet the evolving challenges of cloud environments. [ABSTRACT FROM AUTHOR]
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  Label:
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  Data: <i>Copyright of Artificial Intelligence Review is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10462-025-11208-8
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              M: 07
              Text: Jul2025
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