Improving Cloud Scalability for Internet of Things Data Storage with Machine Learning Algorithms

With the rise of the Internet of Things (IoT), there has been a massive increase in the data generated by these connected devices, which represents a formidable challenge to conventional cloud storage systems. These systems are highly scalable but fall short when it comes to fulfilling the unique offerings that are required by the actual IoT environments: real-time processing, improved resource management, dynamic scalability. Working to address these hardships, this study analyzes the novel use of machine learning (ML) algorithms to optimize cloud scalability for storage of IoT data. Machine learning can use predictive models to manage cloud infrastructures, dynamically adjusting resource allocation (e.g., computation and storage), load balance, and storage management of cloud infrastructures according to changing IoT workloads. In this study multiple ML techniques reinforcement learning, clustering, and predictive analytics were assessed to automate and improve various cloud operations. The presented method empowers cloud systems with predictive analytics for data traffic, storage resource allocation, thereby avoiding over-provisioning and under-use. Additionally, coupling edge computing with machine learning-based cloud storage will reduce latency and enhance the overall efficacy of IoT networks. Results of experimentation show that machine learning introduces a part of the cloud storage that allows systems to increase scalability, cut operational costs, heighten the ability to adapt to changing demands for IoT data. This international research further enhances progresses towards journey for automated adaptive, self-optimized cloud architectures designed in order to meet the dynamic and emerging nature of IoT ecosystems.