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

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Опубликовано в::Mathematics vol. 13, no. 8 (2025), p. 1342
Главный автор: Cai Jialin
Другие авторы: Zeng, Hui, Liu, Feifei, Chen, Junming
Опубликовано:
MDPI AG
Предметы:
Scheduling
Central processing units > CPUs
Task scheduling
Computer memory
Deep learning
Graphics processing units
Optimization
Resource scheduling
Algorithms
Clusters
Resource utilization
Cloud computing
Efficiency
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Краткий обзор: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.
ISSN:2227-7390
DOI:10.3390/math13081342
Источник:Engineering Database