Traffic engineering has been used in IP and MPLS networks for a number of years as a tool for making more efficient use of capacity by explicitly routing traffic demands where there is available network capacity that would otherwise be unused. Deployment of traffic engineering imposes an additional layer of complexity to network design and operations, however, which has constrained its adoption for capacity optimisation. The rise of Software Defined Networks has renewed interest in the use of traffic engineering approaches leveraging centralised network controllers for capacity optimisation. We argue that future networks can realise the network optimisation benefits of traffic engineering without incurring additional network complexity through closer coupling between the network and the applications and services using the network. This can be achieved through leveraging a network- and traffic-aware controller to directly influence where applications and services site or locate service instances, i.e. which implicitly impacts the paths that the applications or services traffic demands take through the network. We call this technique Demand Engineering. Demand Engineering has the additional benefit of providing an admission control capability, i.e. which can provide an assurance that network SLAs can be met. In this paper we describe the concept of Demand Engineering, give examples of its use and present simulation results indicating its potential benefits. We also compare demand engineering to traffic engineering.
The Greatest Teacher, Failure is: Using Reinforcement Learning for SFC Placement Based on Availability and Energy Consumption
Universidade Federal de Pernambuco (UFPE)（伯南布哥联邦大学（UFPE））；Dublin City University (DCU)（都柏林城市大学（DCU））；Universidade de Pernambuco (UPE)（伯南布哥大学（UPE））
软件定义网络（SDN）和网络功能虚拟化（NFV）正在使网络可编程，从而更加灵活和敏捷。为了满足服务水平协议，实现传统网络的更大利用率，更快的服务部署并减少支出，电信运营商正在部署越来越复杂的服务功能链（SFC）。尽管SFC具有诸多优势，但从云到边缘的异构性和动态性的增加带来了重大的SFC放置挑战，尤其是在保持可用性、服务质量和最小化成本的同时添加或删除网络功能。本文提出了一种基于强化学习（RL）的可用性和能量感知的动态SFC布局解决方案。两个策略感知RL算法，优势演员批评（A2C）和接近的政策优化（PPO2），使用模拟的地面实况网络拓扑结构的基础上，Rede Nacional de Ensino e Pesquisa（RNP）网络，巴西的国家教学和研究网络骨干。仿真结果表明，PPO2一般优于A2C和贪婪的方法，无论是在接受率和能源消耗。A2C仅在网络服务器拥有更多计算资源的情况下才优于PPO2。
When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Network
Sun Yat-sen University（中山大学）；Auburn University（奥本大学）
