Efficient Resource Allocation Using Service Function Chain in Agricultural

As the level of intelligence in agricultural consumer electronics continues to advance, data-driven devices are often faced with challenges such as resource shortages, high real-time requirements, and complex data processing. In particular, the diversity and variability of massive agricultural consumer electronics in the agricultural Internet of Things (IoT) complicate the resource allocation process. Traditional centralized cloud computing architectures often struggle to meet these demands. To address these issues, this paper proposes a resource collaborative allocation method for agricultural consumer electronics based on Service Function Chain (SFC). First, we formalize the resource requirements of various agricultural consumer electronics applications based on SFC in a resource mapping model. Subsequently, we develop a multidimensional resource balancing model with computing resources, storage resources, and bandwidth resources requirements of agricultural consumer electronics. We also devise a fine-grained end-to-end latency model to comprehensively consider the effective utilization of multidimensional resources and latency guarantees for agricultural consumer electronics. Finally, we employ a deep reinforcement learning algorithm with experience replay to optimize the resource allocation strategy in real-time, thereby enhancing the agricultural consumer electronics system’s responsiveness and resource utilization efficiency. Experimental results demonstrate that our proposed strategy outperforms existing methods in terms of improving resource utilization and reducing latency.