Work

A Multi-Objective Optimization-Based Container Cloud Resource Scheduling Method

Year: 2026

Type: article

Abstract: Container-based cloud platforms enable flexible and lightweight application deployment, yet container scheduling remains challenged by resource fragmentation, load imbalance, excessive energy consumption, and service-level agreement (SLA) violations. To address these issues, this paper proposes a hybrid multi-objective optimization approach, termed HHO-GWO, which combines Harris Hawks Optimization (HHO) with the Grey Wolf Optimizer (GWO) for container initial placement in cloud environments. A unified fitness function is designed to jointly consider resource utilization, load balancing, resource fragmentation, energy consumption, and SLA violation rate. In addition, a dynamic weight adjustment mechanism and Lévy flight perturbation are incorporated to improve search adaptability and prevent premature convergence. The proposed method is evaluated through extensive simulations under different workload scales and compared with several representative metaheuristic algorithms. The results show that HHO-GWO achieves improved convergence behavior, solution quality, and stability, particularly in large-scale container deployment scenarios. These findings suggest that the proposed approach provides a practical and energy-aware solution for multi-objective container scheduling in cloud data centers.

Source: Future Internet

DOI: 10.3390/fi18010058

Authors: Danping Zhang, Xiaolan Xie, Yuhui Song

Institutions: Guilin University of Technology, Guangxi Key Laboratory of Embedded Technology and Intelligent System

Language: English

Topic: Cloud Computing and Resource Management

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,930