Federated Cloud Ecosystems: Optimizing Multi-Tenant Architectures for Decentralized Data Processing

Abstract

The growing demand for scalable and decentralized data processing can be addressed with the promising framework of federated cloud ecosystems. Nevertheless, optimizing multi-tenant architectures in such ecosystems is still challenging as they suffer from resource contention, tenant isolation and distribution of efficient data. This research proposes a new approach for optimizing multi-tenant architectures for decentralized data processing in federated cloud systems. The proposed architecture improves scalability, fault tolerance and latency using advanced resource allocation algorithms, tenant-aware data distribution models, and decentralized processing techniques. We perform a comprehensive evaluation and show that the proposed strategies result in significant performance improvements over existing models, demonstrating the potential for the proposed methods to address critical challenges in federated cloud systems. The study also compares state-of-the-art models in detail, providing a basis for comparing their strengths and tradeoffs. These results open the door towards building more efficient and robust federated cloud ecosystems that can support diverse and high-demand workloads.