Joint Resource Management and Flow Scheduling for SFC Deployment in Hybrid Edge-and-Cloud Network
Joint Resource Management and Flow Scheduling for SFC Deployment in Hybrid Edge-and-Cloud Network
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DOI：
10.1109/infocom48880.2022.9796884
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发表时间：
2022-05
期刊：
影响因子：
--
通讯作者：
Yingling Mao;Xiaojun Shang;Yuanyuan Yang
中科院分区：
文献类型：
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作者：
Yingling Mao;Xiaojun Shang;Yuanyuan Yang
网络功能虚拟化（NFV）将网络功能从专有硬件迁移到边缘或云上的商业服务器，使网络服务更具成本效益，管理方便且灵活。为了促进这些优势，关键是在混合边缘和云环境中找到链式虚拟网络功能（即服务功能链（SFC））的最佳部署，同时考虑资源和延迟。这是一个NP难问题。本文首先将问题限制在边缘，设计了一个常数近似算法--链式下一拟合（CNF），并在CNF中设计了一个子算法--双生成树（DST）来处理虚拟网络嵌入问题。然后，我们将云资源和边缘资源都考虑在内，并创建了一个名为递减排序，链式下一次拟合（DCNF）的推广算法，该算法也具有可证明的常数近似比。仿真结果表明，DCNF与最优解的比值远小于理论界，平均接近1.25。此外，DCNF总是具有比基准测试更好的性能，这意味着它是混合边缘和云网络中联合资源和延迟优化的良好候选者。
Network Function Virtualization (NFV) migrates network functions from proprietary hardware to commercial servers on the edge or cloud, making network services more cost-efficient, manage-convenient, and flexible. To facilitate these advantages, it is critical to find an optimal deployment of the chained virtual network functions, i.e. service function chains (SFCs), in hybrid edge-and-cloud environment, considering both resource and latency. It is an NP-hard problem. In this paper, we first limit the problem at the edge and design a constant approximation algorithm named chained next fit (CNF), where a sub-algorithm called double spanning tree (DST) is designed to deal with virtual network embedding. Then we take both cloud and edge resources into consideration and create a promotional algorithm called decreasing sorted, chained next fit (DCNF), which also has a provable constant approximation ratio. The simulation results demonstrate that the ratio between DCNF and the optimal solution is much smaller than the theoretical bound, approaching an average of 1.25. Moreover, DCNF always has a better performance than the benchmarks, which implies that it is a good candidate for joint resource and latency optimization in hybrid edge-and-cloud networks.