Design of a Cognitive Cloud-Network Framework for Intelligent System Integration in Distributed Data Centers

The rapid growth of cloud service and data-driven applications has highlighted the limitations of the traditional static resource allocation frameworks in distributed data centers. In this paper, a Cognitive Cloud-Network Framework (CCNF) is presented which aims to optimise system integration by integrating intelligent decision-making mechanisms. In particular, the framework uses Long Short-Term Memory (LSTM) networks to predict workload, Proximal Policy Optimisation (PPO) to orchestrate, and fuzzy logic controllers to adapt to an adaptive control setting in a Software-Defined Networking (SDN) setting. This modular, stratified architecture allows real-time prediction, effective task scheduling and dynamic resource management. In simulation experiments, which are run with a real-world dataset of Alibaba and Google using CloudSim and NS-3, the performance benefits are measurable, that is, 23 % gain in resource utilisation, 18 % decrease in latency, and 0.9 % SLA violation rate. These findings confirm the effectiveness of AI-based orchestration in distributed infrastructures and make CCNF a scalable and generalisable methodology of next-generation cloud systems.