Maximizing Energy Efficiency: Optimized Deep Reinforcement Learning Model for Big Data in Cloud Environments
Keywords:
Big Data Analytics, Cloud Computing, Energy Efficiency, Load Prediction, Deep Reinforced Learning, and Optimization.Abstract
The use of big data analytics in cloud settings has turned into a key requirement for handling the large and intricate datasets made by modern applications. The combination of cloud computing with big data analytics provides scalable, adaptable and affordable methods to handle processing tasks, allowing real-time handling as well as making decisions that are beneficial in different fields. Still, current ways for load prediction and resource management have to tackle notable difficulties: they cannot scale up easily; their forecasting precision is restricted; there is an issue regarding energy use efficiency. The continuing advancement in these areas may help enhance the effectiveness of big data analytics in cloud environments. The research is noticeably lacking in developing combined models that can improve both forecasting precision and resource allocation for better energy efficiency at the same time. This paper shows a high-level method for load prediction in big data cloud settings by joining Recurrent Embedded Attention-based Reinforcement (REAR) with Artificial Rabbit Optimization (ARO) models. Old-fashioned techniques of load prediction and resource control in cloud surroundings sometimes have issues with being able to scale up, precision, and energy effectiveness. The REAR-ARO model we suggest tackles these difficulties by using the benefits of deep learning and optimization inspired by nature. REAR helps improve the accuracy of predictions by using attention mechanisms for capturing complex time-based relationships, while ARO optimizes resource distribution to minimize energy use and cut down on resource competition. Experiments with good detail show better performance of the REAR-ARO model. It gives more accurate results in predicting load and uses less energy, making it a hopeful choice for enhancing sustainability and operation efficiency of cloud data centers as they handle rising requirements from big data analytics.Top of Form
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