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Task Scheduling for Heterogeneous Multi-Core Processors Based on Deep Reinforcement Learning
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DOI：10.1155/int/7562400.png)
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Abstract
En 中文
Heterogeneous multicore processor systems are commonly used for scheduling tasks of DAG applications. Deep reinforcement learning, with its superior ability to perceive decisions directly and handle high-dimensional state actions, has become a prevalent solution for scheduling these systems. However, the incomplete environment models and large action spaces of deep reinforcement learning present significant challenges to scheduling. This paper investigates a scheduling problem in a heterogeneous multicore processor environment. Initially, system environment information is extracted and encoded using a graph convolutional neural network based on integrating adapter and AdapterFusion into the transformer architecture. Then, by separating task selection and processor allocation, the decision space is reduced: the former uses a deep neural network to learn to select nodes, and the latter allocates processors using a heuristic scheduling algorithm combining earliest completion time-based node replication and rolling technology. The entire scheduling process is a Markov decision problem. Therefore, the PPO algorithm with dynamic adjustment of the clipping factor, combined with an advantage actor-critic network, is employed for training, optimizing, and evaluating the algorithm to find the optimal scheduling strategy. The training process adopts a reward function for the time and power consumption required for completed task scheduling to ensure that multiple DAG application task scheduling can achieve optimal performance. Experiments conducted in various environments with different parameters show that, compared to other algorithms, this algorithm reduces the overall execution time and power consumption cost of heterogeneous multicore processor tasks by 11.09%.
Keywords:
advantage actor-critic network
DAG scheduling
heterogeneous multicore processor systems
PPO algorithm
transformer architecture
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