CADMM: Context-aware Adaptive Data Management Middleware for Federated Cloud Environments

Abstract

As cloud computing continues to evolve, the demand for efficient data management solutions in federated cloud environments becomes increasingly critical in terms of processing cost. In the proposed work, a novel middleware framework called Context-aware Adaptive Data Management Middleware (CADMM) is designed specifically for federated cloud environments. The proposed middleware framework integrates adaptive algorithms and intelligent data management techniques to optimize resource utilization, enhance data accessibility, and ensure robustness in highly dynamic and distributed cloud environment. Leveraging advanced algorithms and adaptive strategies the middleware framework dynamically adapts its data management policies and configurations based on changing workload patterns, resource availability, and network conditions. The comprehensive experimentation and performance evaluation is carried out to demonstrate the effectiveness and scalability in real-world federated cloud environmentsusing CADMM. The obtained results shows a significant improvement in processing cost, data access latency, resource utilization efficiency, and overall system robustness as compared to existing approaches which were based on nonadaptive and non-context-aware techniques. The work represents a significant advancement in the field of federated cloud computing by offering an adaptive middleware framework to address the evolving challenges like Multidisciplinary Data, Vendor Lock-in, Interoperability Issue and Disparity of Services of data management in distributed cloud infrastructures.