Intelligent Strategies for Optimal Virtual Network Function Placement in 5G Core

拟议的项目旨在为5G核心网络部署开发新的算法，以满足关键性能指标(KPI)。最终目标是在满足时延要求的同时，最大限度地减少5G分组核心中的虚拟网络功能数量。因此，该项目旨在解决5G网络延迟和运营成本之间的权衡问题，预计到2025年，5G网络将覆盖82%的人口。更高的速度、更低的延迟和更大的容量将使5G网络能够改善直播和增强现实。这些改进将使各种服务成为可能，如联网汽车、先进游戏、触觉互联网、远程医疗和许多其他服务。拟议的项目将遵循围绕三个主要研究主题的整体方法：1)为5G分组核心中的用户和控制平面虚拟网络功能(VNF)布局开发实时可行的算法，2)VNF布局的优化和AI使能模型，以及3)所开发算法的试验台实现和验证。实现这些方案中的每一个都将能够满足高效和低延迟5G网络的核心逻辑组件。提出的研究主题新颖，回应了5G分组核心管控需求不断增长的新兴需求。因此，该项目依靠启发式、最优化和基于机器学习的规划方案，为每个研究主题制定智能和具有成本效益的战略、技术、算法和方法。因此，通过节省因无法满足延迟要求而导致的SLA违规恢复费用，以及通过在网络核心中放置多个VNC而节省的运营支出，这将带来数百万美元的收益。此外，在解决合作伙伴公司Ciena的延迟和高效管理需求的同时，全国各地的许多企业将受益于结果，因为它们将服务于加拿大信息和通信技术(ICT)行业在5G支持的AI支持的5G网络和生态系统中处于领先地位的需求。

The proposed project aims at developing novel algorithms for the 5G Core Network Placement to meet Key Performance Indicators (KPIs). The ultimate goal is to minimize the number of virtual network functions in 5G packet core while meeting the latency requirements. Thus, this project aims to address the trade-off between latency and operational costs in a 5G network, which is expected to cover 82% of the population by 2025. Higher speed, lower latency and greater capacity of 5G networks will enable improved livestreaming and improved augmented reality. These improvements will enable various services such as connected cars, advanced gaming, tactile Internet, remote healthcare and many others. The proposed project will follow a holistic approach anchored around three main research themes: 1) Development of real time and feasible algorithms for user and control plane virtual network function (VNF) placement in the 5G packet core, 2) Optimization and AI-enabled models for VNF placement , and 3) Test bed implementation and verification of the developed algorithms. Achieving each of these schemes will enable meeting the core logical components of efficient and low latency 5G networks. The proposed research themes are novel and respond to the emerging needs of the increasing demands in the management and control of 5G packet core. Accordingly, this project relies on heuristic, optimization and machine learning-based planning schemes to develop intelligent and cost-efficient strategies, techniques, algorithms, and methods for each of the studied themes. This will consequently result in million dollars of gains by saving from what is spent for recovering SLA violations due to not being able to meet latency requirements, as well as savings from operational expenditures for placing many VNFs across the network core. Furthermore, while addressing the latency and efficient management needs of the partner company, Ciena, many businesses across the country will benefit from the outcomes as they will serve for the needs of the Canadian Information and Communication Technology (ICT) sector to take the lead in the AI-enabled 5G networks and ecosystems enabled by 5G.