Intelligent agent training method, cross-domain heterogeneous environment task scheduling method and related device

Abstract

The invention provides an agent training method, a cross-domain heterogeneous environment task scheduling method and a related device. The agent training method comprises the following steps: acquiring pre-training data for simulating a task load in a real cross-domain heterogeneous environment; and training a deep reinforcement learning agent according to the pre-training data, so that the deep reinforcement learning agent is used for performing task scheduling on the cross-domain heterogeneous environment, and the deep reinforcement learning agent is constructed based on a D3QN model and a Markov decision process corresponding to a task scheduling problem of the cross-domain heterogeneous environment in advance. According to the task scheduling method and the task scheduling system, the intelligent agent which gives consideration to the real-time performance and the high efficiency of task scheduling can be constructed, the timeliness of task scheduling in a cross-domain heterogeneous environment by applying the intelligent agent can be effectively improved, the throughput of the whole task scheduling system can be increased, and computing resources provided in the cross-domain environment can be utilized to the maximum extent; therefore, a task scheduling result is ensured to consider the high efficiency of task processing and the load balance between the computing nodes.

Description

technical field [0001] The present application relates to the technical field of task scheduling in a heterogeneous network environment, and in particular, to a method for training an agent, a method for scheduling tasks in a cross-domain heterogeneous environment, and related devices. Background technique [0002] With the increasingly close international scientific research cooperation, the problem of task scheduling in the heterogeneous network environment of international cross-autonomous domains has gradually become a research hotspot. Tasks in a cross-domain heterogeneous environment are very dynamic. First, the task scheduling algorithm must be able to handle both low-load and high-load task load scenarios; second, the types of tasks to be scheduled are also diverse, both computing For intensive scientific research computing tasks, there are also IO-intensive data transmission tasks. At the same time, in the international cross-domain heterogeneous network environmen...

AI Technical Summary

Problems solved by technology

[0004] However, when dealing with task scheduling in a cross-domain heterogeneous environment, the existing task scheduling algorithms have long task processing time and waiting time, low throughput of the scheduling system, poor load balance between nodes, or inability to adapt to cross-domain heterogeneity. Dynamic task loads and characteristics of heterogeneous computing resources in heterogeneous environments

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