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.