Journal of Engineering, Project, and Production Management, 2026, 16(4), 2025-266
Dynamic Multi-Objective Resource Allocation in Cloud Computing
1
Lecturer, School of Economics and Management, Southwest Petroleum
University, Chengdu, 610500, China
2
Supervisor, School of Economics and Management, Southwest Petroleum
University, Chengdu, 610500, China, E-mail:
wu.xming@outlook.com (corresponding author)
Project Management
Received November 12, 2025; revised April 8, 2026; accepted May 2, 2026
Available online May 29, 2026
Abstract: In response to issues of low efficiency in distributed project resource allocation and poor service quality in cloud computing environments, this paper constructs a multi-objective optimization model that explicitly accounts for deadline constraints, task priorities, and virtual machine load conditions. This model proposes a hybrid optimization algorithm that combines dynamic load assessment with active migration mechanisms. The algorithm achieves predictive control of Service Level Agreement (SLA) violations using an irregular cost function, manages dynamic workloads with a task-arrival time aware strategy, and performs load rebalancing through active migration. Experimental results from the CloudSim simulation platform demonstrate that the proposed algorithm significantly outperforms traditional benchmark algorithms across key metrics, including completion time, resource utilization, and SLA violation rate, thereby enhancing system performance and guaranteeing service quality. This research offers a practical technical solution for cloud service providers to optimize data center resource management, ensuring service quality while improving resource utilization efficiency, and holds valuable theoretical and practical significance.
Keywords: CloudSim simulation, resource allocation, load balancing, multi-objective optimization, task scheduling.
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Citation: Yang, Y. and Wu, X. (2026). Dynamic Multi-Objective Resource Allocation in Cloud Computing. Journal of Engineering, Project, and Production Management, 16(4), 2025-266.
