Intelligent Dynamic Multi-Dimensional Heterogeneous Resource Scheduling Optimization Strategy Based on Kubernetes

Jialin Cai, Hui Zeng, Feifei Liu () and Junming Chen ()
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Jialin Cai: School of Art and Design, Guangzhou University, Guangzhou 510006, China
Hui Zeng: School of Design, Jiangnan University, Wuxi 214122, China
Feifei Liu: School of Art and Design, Guangzhou University, Guangzhou 510006, China
Junming Chen: School of Art and Design, Guangzhou University, Guangzhou 510006, China

Mathematics, 2025, vol. 13, issue 8, 1-16

Abstract: In this paper, we tackle the challenge of optimizing resource utilization and demand-driven allocation in dynamic, multi-dimensional heterogeneous environments. Traditional containerized task scheduling systems, like Kubernetes, typically rely on default schedulers that primarily focus on CPU and memory, overlooking the multi-dimensional nature of heterogeneous resources such as GPUs, network I/O, and disk I/O. This results in suboptimal scheduling and underutilization of resources. To address this, we propose a dynamic scheduling method for heterogeneous resources using an enhanced Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm that adjusts weights in real time and applies nonlinear normalization. Leveraging parallel computing, approximation, incremental computation, local updates, and hardware acceleration, the method minimizes overhead and ensures efficiency. Experimental results showed that, under low-load conditions, our method reduced task response times by 31–36%, increased throughput by 20–50%, and boosted resource utilization by over 20% compared to both the default Kubernetes scheduler and the Kubernetes Container Scheduling Strategy (KCSS) algorithm. These improvements were tested across diverse workloads, utilizing CPU, memory, GPU, and I/O resources, in a large-scale cluster environment, demonstrating the method’s robustness. These enhancements optimize cluster performance and resource efficiency, offering valuable insights for task scheduling in containerized cloud platforms.

Keywords: Kubernetes; TOPSIS algorithm; digital dynamics; heterogeneous resource (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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