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Research Scholar, Department of Computer Science, Alagappa University , Karaikudi, Tamil Nadu , India
Professor, Department of Computer Science, Alagappa University , Karaikudi, Tamil Nadu , India
The proposed research paper suggests a hybrid system to improve speed and data extraction in a large data server setup, but the focus will be on how to maximize the use of resources and provide actionable information on the unstructured data. The architecture also incorporates fragmentation-enabled virtual machine (VM) migration and high-end data searching tools to enhance the system efficiency to a high degree. A new strategy of starting VM migration is proposed, which is based on cumulative values, including temperature and CPU usage, to perform real-time and dynamic resource allocation. The system adjusts the computing workload of the physical machines that are hosting several VMs by observing these parameters and migrating them to a different physical machine in case predetermined limits are surpassed. The migration of VMs made possible by this fragmentation results in better performance and resource utilization of the system, which ensures a smooth operation within the big data server environments. Also, the architecture encompasses an improved similarity measure algorithm, which deals with the challenges of locating and mining information from unstructured data. This is the best way of optimizing the pretreatment, extraction, and representation of unstructured data, hence enhancing the accuracy and efficiency of data searches. The design supports making informed decisions in any sector with the use of data, which eases the process of retrieving meaningful information out of the multifaceted, unstructured data sources. The combined architecture in the context of big data servers has significant gains in performance and the use of resources, as well as insights into data. It offers an all-encompassing solution to improving the efficiency of the systems and deriving actionable data out of the unstructured data assets. The study's findings demonstrate an 18% improvement in data retrieval speed, a 25% reduction in resource consumption, and a 15% increase in overall system performance when compared to traditional data management techniques. Such outcomes will help to optimize the configuration of big data servers to allow organizations to make more data-driven decisions.
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