SFC-GS: A Multi-Objective Optimization Service Function Chain Scheduling Algorithm Based on Matching Game

Shi Kuang, Moshu Niu, Sunan Wang (), Haoran Li, Siyuan Liang and Rui Chen
Additional contact information
Shi Kuang: Transmission Operation and Inspection Center, State Grid Zhengzhou Electric Power Supply Company, Zhengzhou 450007, China
Moshu Niu: Transmission Operation and Inspection Center, State Grid Zhengzhou Electric Power Supply Company, Zhengzhou 450007, China
Sunan Wang: College of Electronics & Communication Engineering, Shenzhen Polytechnic University, Shenzhen 518005, China
Haoran Li: College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou 450007, China
Siyuan Liang: College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou 450007, China
Rui Chen: College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou 450007, China

Future Internet, 2025, vol. 17, issue 11, 1-21

Abstract: Service Function Chain (SFC) is a framework that dynamically orchestrates Virtual Network Functions (VNFs) and is essential to enhancing resource scheduling efficiency. However, traditional scheduling methods face several limitations, such as low matching efficiency, suboptimal resource utilization, and limited global coordination capabilities. To this end, we propose a multi-objective scheduling algorithm for SFCs based on matching games (SFC-GS). First, a multi-objective cooperative optimization model is established that aims to reduce scheduling time, increase request acceptance rate, lower latency, and minimize resource consumption. Second, a matching model is developed through the construction of preference lists for service nodes and VNFs, followed by multi-round iterative matching. In each round, only the resource status of the current and neighboring nodes is evaluated, thereby reducing computational complexity and improving response speed. Finally, a hierarchical batch processing strategy is introduced, in which service requests are scheduled in priority-based batches, and subsequent allocations are dynamically adjusted based on feedback from previous batches. This establishes a low-overhead iterative optimization mechanism to achieve global resource optimization. Experimental results demonstrate that, compared to baseline methods, SFC-GS improves request acceptance rate and resource utilization by approximately 8%, reduces latency and resource consumption by around 10%, and offers clear advantages in scheduling time.

Keywords: NFV; SFC; resource scheduling; matching game (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/17/11/484/pdf (application/pdf)
https://www.mdpi.com/1999-5903/17/11/484/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:17:y:2025:i:11:p:484-:d:1777358

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().