AI-Powered Edge Computing in Cloud Ecosystems: Enhancing Latency Reduction and Real-Time Decision-Making in Distributed Networks
Keywords:
AI-powered edge computing, cloud ecosystems, latency reduction, real-time decision-making, distributed networks, machine learning, predictive models, data processing, network efficiencyAbstract
Integrating edge computing with AI offers a profoundly enabling opportunity to reduce latency and allow real-time decision-making in distributed networks within cloud ecosystems. This study investigates solutions to address the fundamental latency challenges present in traditional cloud computing models by deploying machine learning algorithms and real-time analytics at the network edge. Edge computing eliminates long handoffs by processing data near its source, significantly improving response times and network efficiency. The framework proposed in this study uses AI to optimize the edge's data processing and decision-making process and foster an easy flow with the core cloud services. Methodologies of interest are predictive models for latency reduction and AI-driven decision-making to support quick and automatic decision-making. Experimental evaluations indicate the framework sustains significant latency reduction and improved decision accuracy over traditional cloud approaches. The results suggest that edge computing leaps can enable AI-enhanced scalability and adaption to growing demands in real-time applications, enabling distributed network operations. Future research paths include applying more advanced AI models in more challenging decision-making tasks and extending the framework to other industry sectors. By contributing to the discourse on evolving edge computing and its role in shaping the cloud ecosystem, this study stresses the importance of AI in making network environments efficient.
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