<![CDATA[To ensure efficient operations and cost-effectiveness, resource management in cloud computing entails managing cloud resources to satisfy application needs, financial restrictions, and security. In this regard, utilizing data analytics tools for the allocation of resources in cloud computing to efficiently predict, track, allocate, and monitor resources enables businesses to make informed decisions based on real-time data, which plays a crucial role in resource allocation. Organizations adopting cloud computing services face increased network traffic, limiting traffic routing flexibility and causing excess traffic to reach unprepared physical nodes due to an inability to adjust to real-time traffic changes. This paper uses a systematic literature review to investigate the data analytics techniques used for resource allocation in cloud computing. It uses data from 2019 to 2024, sourced from different research databases. The results show that the majority of data analytics tools, including ARIMA and SVM, are employed for resource allocation in cloud computing. This study offers guidance to organizations regarding data analytics tools for the allocation of resources in cloud computing, and the recommendations can be utilized for the enhancement of the results in cloud computing, as well as to scholars by suggesting techniques to further investigate resource allocation to address the current gaps in cloud computing]]>
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