This paper presents a novel Edge-Cloud collaborative computing framework designed to meet the low-latency and high-performance requirements of emerging IoT applications. As the demand for real-time processing and efficient resource utilization continues to grow, traditional cloud-centric or edge-only architectures face critical limitations, such as high latency, limited computational power, and energy inefficiencies. The proposed framework dynamically allocates computational tasks between edge nodes and cloud servers based on factors such as latency sensitivity, computational complexity, and energy constraints. Utilizing machine learning algorithms for intelligent task scheduling and predictive workload management, the system achieves a balanced distribution of processing loads, enhancing responsiveness and scalability. Experimental evaluation through simulation and real-world prototyping demonstrates significant improvements in latency reduction, throughput, task completion time, and energy efficiency compared to baseline models. The framework also incorporates secure communication protocols and modular architecture, ensuring adaptability, data privacy, and compatibility with heterogeneous IoT environments. These findings highlight the potential of Edge-Cloud collaboration as a practical and scalable solution for enabling next-generation IoT services with stringent performance and reliability demands.
Edge computing, Cloud computing, IoT, Low latency, Resource optimization
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