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Latency-aware VNF Chain Deployment with Efficient Resource Reuse at Network Edge

机译:延迟感知的VNF链部署以及在网络边缘的有效资源重用

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With the increasing demand of low-latency network services, mobile edge computing (MEC) emerges as a new paradigm, which provides server resources and processing capacities in close proximity to end users. Based on network function virtualization (NFV), network services can be flexibly provisioned as virtual network function (VNF) chains deployed at edge servers. However, due to the resource shortage at the network edge, how to efficiently deploy VNF chains with latency guarantees and resource efficiency remains as a challenging problem. In this work, we focus on jointly optimizing the resource utilization of both edge servers and physical links under the latency limitations. Specifically, we formulate the VNF chain deployment problem as a mixed integer linear programming (MILP) to minimize the total resource consumption. We design a novel two-stage latency-aware VNF deployment scheme: highlighted by a constrained depth-first search algorithm (CDFSA) for selecting paths, and a path-based greedy algorithm (PGA) for assigning VNFs by reusing as many VNFs as possible. We demonstrate that our proposed algorithm can efficiently achieve a near-optimal solution with a theoretically-proved worstcase performance bound. Extensive simulation results show that the proposed algorithm outperforms three previous heuristic algorithms.
机译:随着对低延迟网络服务的需求不断增长,移动边缘计算(MEC)成为一种新的范例,它提供了与最终用户非常接近的服务器资源和处理能力。基于网络功能虚拟化(NFV),可以灵活地将网络服务配置为部署在边缘服务器上的虚拟网络功能(VNF)链。然而,由于网络边缘的资源短缺,如何有效地部署具有延迟保证和资源效率的VNF链仍然是一个具有挑战性的问题。在这项工作中,我们专注于在延迟限制下共同优化边缘服务器和物理链路的资源利用率。具体来说,我们将VNF链部署问题公式化为混合整数线性规划(MILP),以最大程度地减少总资源消耗。我们设计了一种新颖的两阶段延迟感知VNF部署方案:突出显示了用于选择路径的受限深度优先搜索算法(CDFSA),以及通过重用尽可能多的VNF来分配VNF的基于路径的贪婪算法(PGA) 。我们证明了我们提出的算法可以通过理论上证明的最坏情况性能范围有效地实现接近最优的解决方案。大量的仿真结果表明,该算法优于之前的三种启发式算法。

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