Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 11
3291-3298

Load long sequence prediction method for microservice elastic scaling mechanisms

Xiong Chuanyue1
Yang Jing1,2
Li Shaobo1
Ruan Xiaoli1
Tang Xianghong1
1. State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China
2. Dept. of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 201100, China
DOI: 10.19734/j.issn.1001-3695.2025.04.0107

Abstract

Elastic scaling strategies for microservices often rely on short-term load predictions, leading to frequent policy changes that cause resilience lag and resource contention. This paper proposed a lightweight, long-sequence load prediction method. This approach captured both the local non-linear fluctuations and the global trend characteristics inherent in microservice workloads to accurately model temporal dependencies. It designed a multi-level long-term causal convolution architecture that integrated a fusion strategy between heterogeneous temporal blocks. It evaluated this method against seven state-of-the-art algorithms. The experimental results demonstrate that the proposed method achieves superior performance, ranking best in 59.09% of the 22 key metrics. This study confirms that this long-sequence prediction approach effectively stabilizes elastic scaling decisions, significantly mitigating resource scheduling lag and conflicts, and providing robust support for resource optimization and load scheduling in complex microservice environments.

Foundation Support

国家自然科学基金资助项目(62441608)
贵州省科技项目基金资助项目(QKHZC[2025]003, QKHCG[2025]YB007)
贵阳市科技人才培养对象及项目基金资助项目(ZKHT[2023]48-8)
贵州大学基础科研基金资助项目([2024]08)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.04.0107
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 11
Section: Algorithm Research & Explore
Pages: 3291-3298
Serial Number: 1001-3695(2025)11-011-3291-08

Publish History

[2025-07-11] Accepted Paper
[2025-11-05] Printed Article

Cite This Article

熊川越, 杨静, 李少波, 等. 面向微服务弹性扩缩机制的负载长序列预测方法 [J]. 计算机应用研究, 2025, 42 (11): 3291-3298. (Xiong Chuanyue, Yang Jing, Li Shaobo, et al. Load long sequence prediction method for microservice elastic scaling mechanisms [J]. Application Research of Computers, 2025, 42 (11): 3291-3298. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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