Resource Allocation (RA) is one of the important factors in network function virtualization (NFV) deployment. As physical (PHY) layer resources are limited, e.g., transmitted energy and channel uses, the RA problem at the PHY layer for NFV deployment has become a fast-growing problem, especially for supporting ultra-reliable and low-latency communications (URLLC). Moreover, different nodes in NFV have different requirements for end-to-end communication, e.g., a controller has more stringent reliability requirements than does a logical node. There is a need for efficient and robust RA algorithms at the PHY layer for NFV deployment. To illustrate these challenges, we consider an up-link (UL) transmission protocol for NFV deployment, in which wireless transmissions with short packets are considered, and both the packet length and the transmission power are adjustable. Then, for three NFV deployment scenarios, we formulate three RA problems as three optimization problems to obtain the optimal parameters. Since these optimization problems are highly non-convex and they include excessive constraint conditions, the global optimal solutions are hard to obtain and are even infeasible for the conventional heuristic algorithms due to their low convergence efficiency. To address these problems, in this paper, the intelligent scheme of the modified shuffled frog-leaping algorithm (MSFLA) based on improved extremal optimization (EO) is applied to design RA algorithms. Three RA algorithms are designed for three NFV deployment scenarios to evaluate the quality of the solutions produced by the MSFLA-EO scheme. We perform simulations of three proposed RA algorithms in terms of various performance parameters. The experimental results are encouraging and demonstrate the efficiency of the proposed RA algorithms.

中文翻译：

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网络功能虚拟化部署的物理层资源分配

资源分配 (RA) 是网络功能虚拟化 (NFV) 部署中的重要因素之一。由于物理 (PHY) 层资源有限，例如传输能量和信道使用，用于 NFV 部署的 PHY 层的 RA 问题已成为一个快速增长的问题，尤其是支持超可靠和低延迟通信 (URLLC) . 此外，NFV中不同节点对端到端通信的要求也不同，例如控制器对可靠性的要求比逻辑节点更严格。NFV 部署需要在 PHY 层使用高效且稳健的 RA 算法。为了说明这些挑战，我们考虑用于 NFV 部署的上行链路 (UL) 传输协议，其中考虑了具有短数据包的无线传输，并且包长度和发射功率都是可调的。然后，对于三个 NFV 部署场景，我们将三个 RA 问题公式化为三个优化问题，以获得最优参数。由于这些优化问题具有很强的非凸性，并且包含过多的约束条件，使得全局最优解很难获得，对于传统的启发式算法，由于收敛效率低，甚至是不可行的。针对这些问题，本文将基于改进极值优化（EO）的改进混洗蛙跳算法（MSFLA）的智能方案应用于RA算法的设计。针对三种 NFV 部署场景设计了三种 RA 算法，以评估 MSFLA-EO 方案产生的解决方案的质量。我们根据各种性能参数对三种提出的 RA 算法进行了模拟。实验结果令人鼓舞，并证明了所提出的 RA 算法的效率。