Data Transmission with Aggregation and Mitigation Model Through Probabilistic Model in Data Center

Abstract

With the increasing demand for data storage and processing, data centers have become critical infrastructures. Efficient data transmission and aggregation in data centers are essential for improving performance and reducing energy consumption. This research paper presents a novel approach called DAWPM (Data Aggregation Weighted Probabilistic Model) specifically designed for data centers. DAWPM leverages probabilistic models to dynamically adjust data transmission and aggregation strategies based on network conditions, effectively mitigating congestion and improving overall system performance. The proposed model optimizes data aggregation algorithms to reduce the amount of transmitted data while maintaining data accuracy and minimizing the impact on system resources. It employs probabilistic algorithms to analyze data patterns and make informed decisions on data aggregation and transmission. Simulation results demonstrate that DAWPM outperforms existing models in terms of data accuracy, communication overhead, energy consumption, and packet loss rate. The proposed model offers a reliable and efficient solution for data transmission in data centers, enabling improved data processing, reduced network congestion, and enhanced overall system performance.