Network Function Virtualization (NFV) is a promising virtualization technology that has the potential to significantly reduce the expenses and improve the service agility. NFV makes it possible for Internet Service Providers (ISPs) to employ various Virtual Network Functions (VNFs) without installing new equipments. One of the most attractive approaches in NFV technology is a so-called Joint Placement and Allocation of Virtual Network Functions (JPA-VNF) which considers the balance between VNF investment with Quality of Services (QoS). We introduce a novel capability function to measure the potential of locating VNF instances for each server in the proposed OJPA-HS model. This model allows the servers in the network to be heterogeneous, at the same time combines and generalizes many classical JPA-VNF models. Despite its NP-hardness, we present a provable best-possible deterministic online algorithm based on dynamic programming (DP). To conquer the high complexity of DP, we propose two additional randomized heuristics, the Las Vegas (LV) and Monte Carlo (MC) randomized algorithms, which performs even as good as DP with much smaller complexity. Besides, MC is a promising heuristic in practice as it has the advantage to deal with big data environment. Extensive numerical experiments are constructed for the proposed algorithms in the paper.

中文翻译：

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虚拟网络功能与异构服务器的在线联合部署和分配

网络功能虚拟化（NFV）是一种很有前途的虚拟化技术，具有显着降低成本和提高服务敏捷性的潜力。NFV 使互联网服务提供商 (ISP) 可以在不安装新设备的情况下使用各种虚拟网络功能 (VNF)。NFV 技术中最具吸引力的方法之一是所谓的虚拟网络功能联合部署和分配 (JPA-VNF)，它考虑了 VNF 投资与服务质量 (QoS) 之间的平衡。我们引入了一种新的能力函数来衡量在提议的 OJPA-HS 模型中为每个服务器定位 VNF 实例的潜力。该模型允许网络中的服务器异构，同时结合和概括了许多经典的 JPA-VNF 模型。尽管它的 NP 硬度，我们提出了一种基于动态规划 (DP) 的可证明的最佳确定性在线算法。为了克服 DP 的高复杂性，我们提出了两个额外的随机启发式算法，拉斯维加斯 (LV) 和蒙特卡洛 (MC) 随机算法，它们的性能甚至与复杂度低得多的 DP 一样好。此外，MC 在实践中是一种很有前途的启发式方法，因为它具有处理大数据环境的优势。为论文中提出的算法构建了大量的数值实验。MC 在实践中是一种很有前途的启发式方法，因为它具有处理大数据环境的优势。为论文中提出的算法构建了大量的数值实验。MC 在实践中是一种很有前途的启发式方法，因为它具有处理大数据环境的优势。为论文中提出的算法构建了大量的数值实验。