The real-time processing of large-scale, heterogeneous tasks—including CPU-only, general-purpose GPU, and specialized GPU tasks—poses significant challenges in Internet of Things (IoT) systems, driven by severe GPU resource fragmentation, inefficient CPU and memory resource utilization on edge servers. These issues often compromise system processing performance and server stability. To address these issues, we formulate a multi-stage mixed-integer nonlinear programming (MINLP) model, to jointly optimize GPU fragmentation rate and system processing capability. We then introduce a novel deviation-based Lyapunov optimization framework that explicitly maintains memory utilization around a predefined optimal threshold, effectively balancing resource usage and system stability. Finally, to achieve real-time decision-making for massive tasks in dynamic systems with randomly arriving tasks, we propose the MA-LHTO algorithm, a multi-agent deep reinforcement learning approach that incorporates a multi-head architecture, entropy-based exploration, and a parameter reset mechanism. Experimental results confirm that our algorithm significantly improves resource utilization, and exhibits good performance under various working conditions.

Achieving real-time processing of tasks has become a crucial objective in the Internet of Vehicles (IoV) field. During the online generation of tasks in IoV systems, many dependency tasks arrive randomly within continuous time frames, and it is impossible to predict the number of arriving tasks and the dependencies between sub-tasks. Offloading dependent tasks, which are quantity-intensive and have complex dependencies, to appropriate vehicle edge servers (VESs) for online processing of large-scale tasks remains a challenge. Firstly, we innovatively propose a VES task parallel processing framework incorporating a multi-level feedback queue to enhance the cross-slot parallel processing capabilities of the IoV system. Secondly, to reduce the complexity of problem-solving, we employ the Lyapunov optimization method to decouple the online task offloading control problem into single-stage mixed-integer nonlinear programming problem. Finally, we design an online task decision-making algorithm based on multi-agent reinforcement learning to achieve real-time task offloading decisions in complex dynamic IoV environments. To validate our algorithm’s superiority in dynamic IoV systems, we compare it with other online task offloading decision-making algorithms. Simulation results show that ours significantly reduces the all-task processing latency of IoV system by 15% compared to the comparison algorithms, and the task average latency time is reduced by 14%.