Edgeperfai: Intelligent Edge Computing For Ultra-Low-Latency Mobile Performance Optimization
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V6I1P117Keywords:
Edge Computing, Artificial Intelligence, Mobile Networks, Low Latency, 5G, EdgePerfAI, Performance OptimizationAbstract
The mobile application explosion, along with real-time services of any kind autonomous vehicles, smart healthcare, immersive AR/VR has a common denominator, which is the demand for ultra-low latency performance. Cloud-centric architectures of the traditional kind are not quite up to the task since they are often met with network congestion, backhaul delays, and limited context awareness. EdgePerfAI solves the problem by an edge computing framework that combines the proximity of edge resources to the user together with the power of AI to adapt to the changing scenario. By putting AI models right where the network meets the customer, EdgePerfAI constantly gets smarter with user behavior, device performance, and network conditions, and hence is able to dynamically affect workload distribution, bandwidth allocation, and data routing in real time, among other things. The framework is basically an attempt to minimize jointly end-to-end latency, improve Quality of Experience (QoE), and achieve energy-efficient resource utilization without compromising security or scalability. To fulfill such high aspirations, EdgePerfAI employs numerous techniques such as predictive analytics, reinforcement learning, and distributed orchestration to be on top of demand spikes, pre-cache data, and execute computation near the data source. Experimental evaluations show that the proposed framework can cut down latency by as much as 45% and increase throughput more than 30% compared to traditional edge frameworks. In this way, telecom providers, IoT solution architects, and application developers can harvest real-time intelligence at a scale that was not possible before. In the final analysis, EdgePerfAI is a landmark move towards the vision of mobile networks that are truly responsive and self-optimizing, i.e., where the convergence of AI and edge computing leads to a paradigm shift in latency, resilience, and user experience of next-generation digital infrastructures.
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