Responsible Cloud Intelligence Ethical AI and Real-Time Automation for Adaptive Software-Defined Networking Systems
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Abstract
The rapid evolution of cloud-networking infrastructures, combined with the programmability of software-defined networking (SDN), enables unprecedented automation of network behaviour through real-time analytics and decision-making. Yet, as artificial intelligence (AI) is increasingly applied to SDN-cloud systems for flow optimisation, resource reallocation, anomaly detection and self-healing, the imperatives of ethics, transparency, accountability and sustainability become critical. In this paper we propose a Responsible Cloud Intelligence framework that embeds ethical AI and real-time automation in adaptive SDN systems operating in the cloud. The architecture integrates a real-time telemetry & control loop, an AI decision engine for dynamic adaptation of network flows and policies, and an ethics/governance layer ensuring transparency, fairness and auditability of automated actions. We present the components: (i) cloud-based monitoring and orchestration, (ii) SDN control and policy enforcement, (iii) AI module for prediction, adaptation and automation, and (iv) ethical governance subsystem for decision logging, bias mitigation and human-override. A prototype simulation is implemented in a cloud-SDN environment under dynamic load, fault injection and policy-change scenarios. Key metrics include latency of adaptation, flow throughput, automation rate, transparency index and fairness variance. Results show the proposed framework reduces adaptation latency by ~28 %, improves flow throughput by ~18 %, and enhances transparency/auditability by ~35 % relative to a baseline without the ethics layer. We discuss the trade-offs between automation performance, governance overhead and ethical assurance. Our findings highlight the feasibility of embedding ethical AI into real-time SDN-cloud systems and contribute guidelines for practitioners. Future work includes large-scale deployment, multi-domain orchestration, continuous ethics-monitoring and lifecycle sustainability of network intelligence.
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