Deep Reinforcement Learning for Dynamic Resource Allocation in IoT-enabled Big Data Networks
Keywords:
Deep Reinforcement Learning, Dynamic Resource Allocation, IoT, Big DataAbstract
In the area of Internet of Things (IoT)-enabled big data networks, the dynamic and diverse character of these settings presents a significant problem in terms of the optimal allocation of resources. Deep Reinforcement Learning (DRL) has emerged as a viable technique to overcome this issue by dynamically adjusting resource allocation algorithms depending on changing network circumstances and demands. This approach has the potential to handle other problems as well. The purpose of this study is to provide a complete assessment and analysis of traditional research efforts that revolve around the use of DRL approaches for dynamic resource allocation in big data networks that are enabled by the Internet of Things (IoT). Furthermore, we emphasize the possible advantages and limits of applying DRL in such complex systems by analyzing the techniques, problems, and successes of previous research that have been conducted in this field. We have identified important research gaps and potential for future investigations via this study. These studies are focused at enhancing the efficacy and scalability of DRL-based resource allocation solutions in big data networks that are enabled by the Internet of Things (IoT).
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