MADRL-Based Collaborative Computation Offloading and Resource Orchestration for Multitask Data Sharing in Smart Agriculture

Multiple different computation tasks may be simultaneously offloaded to mobile edge computing (MEC) servers in smart agriculture scenarios, where the redundant transmission of shared data among different tasks leads to insufficient utilization of system resources (i.e., computing resources, communication resources, and caching resources) and lagging processing efficiency. Existing schemes optimizing multitask computation offloading with shared data almost focus on identical tasks, which are difficult to apply in real-world scenarios with different tasks to meet various service demands of fairness, low latency, and low energy consumption. In this article, we propose a fair, real-time, and green collaborative optimization scheme of computation offloading and resource orchestration for multitask data sharing in smart agriculture based on multiagent deep reinforcement learning (MADRL), aiming to improve offloading efficiency and system resource utilization to meet diverse tasks’ service demands. First, a collaborative optimization problem of computation offloading and resource orchestration is formulated to minimize the system latency, energy consumption, and caching space occupancy under constraints of redundant data transmission and limited system resources. It is difficult for traditional optimization methods to solve the formulated optimization problem characterized by dynamics, high-dimensionality, nonlinearity, and mixed-integer. Then, we propose an MADRL algorithm named MATD3-CO-RO-MDS based on a hierarchical reward mechanism to solve it and approximate the optimal offloading and orchestration strategy. Finally, experimental results prove that our proposed algorithm achieves smaller latency, energy consumption, and caching space occupancy compared with existing algorithms. It even has a 76.7% advantage in reducing latency when more tasks participate in offloading.