Adaptive Task Offloading in Hybrid Cloud-Edge Systems: A Performance-Driven Approach

Optimizing workload distribution between cloud and edge environments is critical for reducing latency, im- proving energy efficiency, and managing bandwidth in hybrid computing systems. This study presents a dynamic offloading framework that leverages heuristic optimization techniques to balance computational loads across cloud and edge servers. By incorporating adaptive decision-making models, the approach minimizes response time while optimizing resource utilization in mobile and IoT-based applications. Experimental results demonstrate significant improvements in system performance, bandwidth efficiency, and energy consumption. This research provides a scalable strategy for intelligent workload management in hybrid cloud environments.