SFC Placement and Dynamic Resource Allocation Based on VNF Performance-Resource Function and Service Requirement in Cloud-Edge Environment

With the continuous development of network functions virtualization (NFV) and software-defined networking (SDN) technologies and the explosive growth of network traffic, the requirement for computing resources in the network has risen sharply. Due to the high cost of edge computing resources, coordinating the cloud and edge computing resources to improve the utilization efficiency of edge computing resources is still a considerable challenge. In this paper, we focus on optimizing the placement of network services in cloud-edge environments to maximize the efficiency. It is first proved that, in cloud-edge environments, placing one service function chain (SFC) integrally in the cloud or at the edge can improve the utilization efficiency of edge resources. Then a virtual network function (VNF) performance-resource (P-R) function is proposed to represent the relationship between the VNF instance computing performance and the allocated computing resource. To select the SFCs that are most suitable to deploy at the edge, a VNF placement and resource allocation model is built to configure each VNF with its particular P-R function. Moreover, a heuristic recursive algorithm is designed called the recursive algorithm for max edge throughput (RMET) to solve the model. Through simulations on two scenarios, it is verified that RMET can improve the utilization efficiency of edge computing resources.

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Publication Details

Journal
Journal of Systems Engineering and Electronics
Published
2024-08-01
DOI
https://doi.org/10.23919/jsee.2024.000092
Citations
6
Primary Topic
Advanced Optical Network Technologies
Type
article
Field-Weighted Citation Impact
1.25
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article

SFC Placement and Dynamic Resource Allocation Based on VNF Performance-Resource Function and Service Requirement in Cloud-Edge Environment

Wentao Fan, Weixiao Meng, Yingchao Han
6 citations
Journal of Systems Engineering and Electronics
Advanced Optical Network Technologies
1.25
article

SFC Placement and Dynamic Resource Allocation Based on VNF Performance-Resource Function and Service Requirement in Cloud-Edge Environment

Wentao Fan, Weixiao Meng, Yingchao Han
article en
6 citations

Abstract

With the continuous development of network functions virtualization (NFV) and software-defined networking (SDN) technologies and the explosive growth of network traffic, the requirement for computing resources in the network has risen sharply. Due to the high cost of edge computing resources, coordinating the cloud and edge computing resources to improve the utilization efficiency of edge computing resources is still a considerable challenge. In this paper, we focus on optimizing the placement of network services in cloud-edge environments to maximize the efficiency. It is first proved that, in cloud-edge environments, placing one service function chain (SFC) integrally in the cloud or at the edge can improve the utilization efficiency of edge resources. Then a virtual network function (VNF) performance-resource (P-R) function is proposed to represent the relationship between the VNF instance computing performance and the allocated computing resource. To select the SFCs that are most suitable to deploy at the edge, a VNF placement and resource allocation model is built to configure each VNF with its particular P-R function. Moreover, a heuristic recursive algorithm is designed called the recursive algorithm for max edge throughput (RMET) to solve the model. Through simulations on two scenarios, it is verified that RMET can improve the utilization efficiency of edge computing resources.

Journal of Systems Engineering and ElectronicsVol. 35(4)
China Mobile (China) (CN), Harbin Institute of Technology (CN)
Decent work and economic growth
Openalex Percentile: Top 20%
Advanced Optical Network Technologies
1.25
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SFC Placement and Dynamic Resource Allocation Based on VNF Performance-Resource Function and Service Requirement in Cloud-Edge Environment — Wentao Fan, Weixiao Meng, et al. · Journal of Systems Engineering and Electronics (2024) | TGRS Research Map | TGRS