DOI: https://doi.org/10.61435/ijred.2026.62635

Flexible Scheduling Strategy and Optimization Algorithm for Container Resources in Power Environment

Hainan Power Grid Co., Ltd., China

Received: 17 Apr 2026; Published: 31 Aug 2026.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2025 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract

Electric power business loads have significant periodicity, burstiness and high reliability requirements. Traditional static allocation of container resources or general elastic scheduling strategies are difficult to achieve efficient and energy-saving utilization of resources while ensuring service quality. Therefore, the load patterns and constraints of typical power business scenarios are analyzed. A mixed integer programming model is established with the goal of minimizing service response time, resource fragmentation rate, and system energy consumption. A hierarchical elastic scheduling framework is proposed, which combines threshold-based reactive scaling and a pre-scheduling mechanism based on bidirectional long short-term memory network load prediction. Compared with the Kubernetes default horizontal Pod autoscaler and the classic best adaptation algorithm, the optimized scheduling scheme reduced the average application response time by 31.2% in typical mixed load scenarios. The average cluster resource utilization increased from 58.72% to 86.63%. The service violation rate was controlled from 8.55% to less than 1.00%. Through intelligent node integration, the overall energy consumption of the system was reduced by approximately 22.3%. The research provides effective theoretical methods and practical reference for solving resource dynamic management and optimizing cloud native infrastructure in the power industry.

Keywords: Container scheduling; Elastic scaling; Resource optimization; Multi-objective particle swarm optimization; Kubernetes.

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Article Info
Section: Original Research Article
Language : EN
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