Two-Stage Learning Approach for Semantic-Aware Task Scheduling in Container-Based Clouds

Abstract

Container-based task scheduling is critical for ensuring a reliable, flexible and cost-effective cloud computing mode. However, in different business cloud systems, state-of-the-art scheduling models are not as effective as those in the simulated world due to the sparsity issues associated with sample sizes and features. Herein, we propose a novel containerized task scheduling framework (SA2CTS) based on reinforcement learning (RL) that incorporates cross-modal contrastive learning (CL) loss. This framework optimizes the scheduler's understanding of the container-based cloud state in RL by adding a pretraining stage, promoting accurate scheduling action inference. Specifically, we design a two-stage learning pipeline. The initial stage involves pretraining the model on a large collection of aligned image-text pairs to extract fine-grained scheduling affinity features, and the high-level semantic representations of scheduling tasks are learned in the multimodal space. In the second stage, we fine-tune the pretrained model with multisource cluster feedback, i.e., build a mapping from state representations to scheduling actions through the RL paradigm, achieving task-oriented and semantic-aware scheduling. The experimental results obtained on three large-scale production cluster datasets substantiate that the proposed SA2CTS method can provide average convergence efficiency and resource utilization improvements of 17.57% and 10.42%, respectively, over the state-of-the-art RL scheduling methods.