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doi: 10.33317/ssurj.561
This review paper provides a comprehensive assessment of scheduling methods for cloud computing, with an emphasis on optimizing resource allocation in cloud computing systems. The PRISMA methodology was utilized to identify 2,487 articles for this comprehensive review of scheduling methods in cloud computing systems. Following a rigorous screening process, 30 papers published between 2018 and 2023 were selected for inclusion in the review. These papers were analyzed in-depth to provide an extensive overview of the current state of scheduling methods in cloud computing, along with the challenges and opportunities for improving resource allocation. The review evaluates various scheduling approaches, including heuristics, optimization, and machine learning-based methods, discussing their strengths and limitations and comparing results from multiple studies. The paper also highlights the latest trends and future directions in cloud computing scheduling research, offering insights for practitioners and researchers in this field.
TK7885-7895, Computer engineering. Computer hardware, Energy efficiency, Electronic computers. Computer science, T1-995, HHeuristics-based scheduling, Machine learning-based scheduling, Optimization-based scheduling, QA75.5-76.95, Load balancing, Performance optimization, Technology (General)
TK7885-7895, Computer engineering. Computer hardware, Energy efficiency, Electronic computers. Computer science, T1-995, HHeuristics-based scheduling, Machine learning-based scheduling, Optimization-based scheduling, QA75.5-76.95, Load balancing, Performance optimization, Technology (General)
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 3 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 10 | |
| downloads | 8 |

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