Performance Optimization of Distributed Cloud Databases Using Advanced Partitioning Techniques

Another main factor is the sheer volume of data being consumed in contemporary applications that make it imperative to achieve optimal solutions for distributed cloud databases. This paper aims at examining ways advanced partitioning improves the efficiency of distributed cloud databases. Without doubt, the traditional approaches to query partitioning pose major problems of load imbalance, high response time and suboptimal query processing to increase the server power and system performance. To overcome these challenges, we introduced a dynamic partitioning framework which includes workload-aware partitioning, hybrid horizontal-vertical segmentation, and real time data balancing. Flaws of the proposed approach have less impact on query response time, loading of nodes and inter node communication as compared with previously discussed approaches. We assess the efficacy of our proposed framework using a distributed cloud system with benchmark data sets. Analysis shows that the performance of the proposed approach outperforms the previous approaches in system throughput, overall latency, and resource management in partitioning. The findings of this research show that there is a feasible approach to enhance distributed cloud databases' performance for big data through innovative partition solutions.