Construction and performance evaluation of AI automatic scheduling algorithm for multi-task collaboration

In this paper, an automatic scheduling algorithm based on hybrid AI strategy is proposed to meet the needs of multi-task collaboration scenarios in modern computing systems. The algorithm aims to solve the limitations of traditional scheduling algorithms in dynamic and heterogeneous environments, and realize dynamic resource allocation and adaptive adjustment of task priorities in multi-task collaborative scenarios. Firstly, this paper models tasks and resources. Tasks are defined as five-tuple including computing requirements, data volume, deadline, priority weights and dependencies, while resources cover heterogeneous computing nodes and network links. Then, a three-layer scheduling architecture is designed, including global task decomposition layer, dynamic strategy generation layer and local execution layer, in order to realize task scheduling and resource adaptation in dynamic environment. In the dynamic strategy generation layer, a hybrid scheduling strategy is formed by combining static rules with AI intelligent decision-making, and attention mechanism is introduced to optimize heterogeneous resource allocation. At the same time, in order to improve the dynamic adaptability of the system, resource awareness module and task preemption strategy are introduced. In the aspect of performance evaluation, multi-dimensional evaluation indexes are designed, covering four aspects: efficiency, resource efficiency, fairness and robustness. Through the discrete event simulation platform based on Python+TensorFlow+SimPy, the traditional Min-Min algorithm, improved genetic algorithm (GA) algorithm and pure deep reinforcement learning (DRL) scheduling algorithm are compared. Experimental results show that the proposed algorithm is superior to the comparison method in terms of average task completion time, resource utilization rate, overtime rate, quality of service (QoS) compliance rate and other indicators in two typical dynamic scenarios, namely, smart factory and autonomous driving, showing good scalability and robustness.