Abstract : This paper introduces a novel mechanism for load offloading through enhanced edge computing principles. In today's era of ubiquitous computing, the proliferation of Internet of Things (IoT) devices and the increasing demand for real-time data processing pose significant challenges to traditional cloud-centric architectures. To address these challenges, we propose a comprehensive approach that leverages the synergy between edge computing and cloud resources to optimize task execution and resource utilization. Our mechanism encompasses task profiling and classification, dynamic offloading decision-making, cloud resource allocation, data compression, security measures, feedback mechanisms, and cross-platform compatibility. By dynamically determining whether tasks should be offloaded to the cloud or executed locally based on resource availability, network conditions, and user preferences, our mechanism minimizes latency and energy consumption while maximizing overall system efficiency. Furthermore, we enhance data transmission efficiency through compression techniques and ensure data security and privacy through robust encryption and authentication measures. Real-time monitoring and optimization, coupled with a feedback mechanism, enable continuous learning and adaptation to changing workload patterns and environmental conditions. Our mechanism is designed for seamless integration into existing infrastructure and accommodates diverse use cases across a wide range of edge devices and cloud platforms. Through thorough testing and validation, we demonstrate the reliability, performance, and scalability of our approach, highlighting its potential to revolutionize load offloading in distributed computing environments.

Keyword : Edge computing, Feedback-Driven Optimization, Internet of Things, Network congestion, RL algorithm.