Enhancing Edge Computing with Machine Learning for Low-Latency Applications
Raghu Dhumpati, Lakshmi Chandrakanth Kasireddy, V Gunavardini, Sanjay Kumar Suman, L. Bhagyalakshmi, Suresh Talwar
Abstract
Edge computing represents a transforming technology that speeds up applications through data processing proximal to origins thus reducing both transmission times and network utilization. As a vital enhancement of edge computing frameworks machine learning (ML) demonstrates its capability to optimize resource allocation performance and task offloading processes and data analytical efficiency. This document describes a brand-new framework that integrates ML within edge computing to serve real-time applications while managing their resource limitations and performance delays. The proposed framework uses adjustable and compact ML models consisting of supervised learning methods with unsupervised learning and reinforcement learning to estimate workloads and ensure time-sensitive task prioritization along with resource management. Our system achieves a 30% decrease in average latency performance due to edge-based intelligence placement which produces better results than regular edge computing approaches. The system architecture operates through three interconnected layers starting from the device level to the edge and cloud level which optimizes local data processing to decrease remote cloud server data transfers. Contemporary experimental studies indicate major progress in three essential domains namely IoT, healthcare facilities and autonomous driving systems through reduced delays combined with better resources usage and increased business task success rates. The research contributes additional strength to edge intelligence research through its scalable real-time data processing method which enables smart cities and next-generation communication network development.