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A service function chain mapping scheme based on functional aggregation in space-air-ground integrated networks
Journal of Network and Computer Applications ( IF 8.7 ) Pub Date : 2024-01-30 , DOI: 10.1016/j.jnca.2024.103829
Peiying Zhang , Kunkun Yan , Neeraj Kumar , Lizhuang Tan , Mohsen Guizani , Kostromitin Konstantin Igorevich , Jian Wang , Jianyong Zhang

Being a novel network architecture, the Space-air-ground integrated network (SAGIN) offers advantages such as extensive network coverage and seamless ubiquitous access. However, it also encounters the challenge of balancing a growing user demand with limited network service resources. In order to further improve the utilization rate of network resources, a group of virtual network functions can be connected according to certain business logic to form a dynamic reconfigurable service function chain. This can provide diversified and high-quality network services for users. So by aggregating the same type of network function, we can reduce the cost of service chain mapping. Based on the above ideas, this paper proposes an Aggregation of Service Function Chain mapping algorithm (A-SFC). The aggregated service function chain topology is mapped according to an improved isomorphic graph search mapping algorithm, resulting in a deployment solution that minimizes the mapping cost by aggregating static SFC requests by end-system division. Experimental results show that the algorithm is able to find an effective mapping scheme under resource constraints. In addition to reducing computation, the algorithm effectively minimizes the consumption of computational resources while delivering excellent performance in terms of service delay and mapping cost.

中文翻译:

天地一体化网络中基于功能聚合的服务功能链映射方案

天地空地一体化网络(SAGIN)作为一种新颖的网络架构,具有网络覆盖范围广、无缝泛在接入等优势。然而,它也面临着平衡不断增长的用户需求与有限的网络服务资源的挑战。为了进一步提高网络资源的利用率,可以将一组虚拟网络功能按照一定的业务逻辑连接起来,形成动态可重构的服务功能链。这可以为用户提供多样化、高质量的网络服务。因此通过聚合同类型的网络功能,我们可以降低服务链映射的成本。基于上述思想,本文提出一种服务功能链聚合映射算法(A-SFC)。聚合服务功能链拓扑根据改进的同构图搜索映射算法进行映射,从而形成通过按端系统划分聚合静态SFC请求来最小化映射成本的部署解决方案。实验结果表明,该算法能够在资源约束下找到有效的映射方案。除了减少计算量外,该算法还有效地最小化了计算资源的消耗,同时在服务延迟和映射成本方面提供了出色的性能。
更新日期:2024-01-30
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