A Collaborative and Distributed Learning-Based Solution to Autonomously Plan Computer Networks
A Collaborative and Distributed Learning-Based Solution to Autonomously Plan Computer Networks
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DOI：
10.1109/icin56760.2023.10073505
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发表时间：
2023-03
期刊：
影响因子：
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通讯作者：
Doriana Monaco;Alessio Sacco;Enrico Alberti;G. Marchetto;Flavio Esposito
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作者：
Doriana Monaco;Alessio Sacco;Enrico Alberti;G. Marchetto;Flavio Esposito
软件定义网络（SDN）范式提供的高可编程性促进了机器学习（ML）方法的集成，以设计一系列新的网络管理方案。其中，我们可以引用自动驾驶网络，其中ML用于分析数据并定义策略，然后由SDN控制器将这些策略转换为网络配置，使网络自治并能够根据网络需求自动扩展决策。尽管它们具有吸引力，但大多数建议解决方案的集中式设计无法跟上网络规模的增长。为此，本文研究了在SDN环境中使用多代理强化学习（MARL）模型进行自动扩展决策。特别是，我们研究了两种可能的替代方案分布操作：一个合作的，其中控制器共享相同的意见，和一个单独的，其中控制器根据自己的逻辑做出决定，只共享一些基本信息，如网络拓扑结构。在Mininet和GENI上进行的实验活动后，结果表明这两种方法都可以保证高吞吐量，同时最小化活动资源的集合。
The high programmability provided by Software Defined Networking (SDN) paradigm facilitated the integration of Machine Learning (ML) methods to design a new family of network management schemes. Among them, we can cite self-driving networks, where ML is used to analyze data and define strategies that are then translated into network configurations by the SDN controllers, making the networks autonomous and capable of auto-scaling decisions based on the network’s needs. Despite their attractiveness, the centralized design of the majority of proposed solutions cannot keep up with the increasing size of the network. To this end, this paper investigates the use of a multi-agent reinforcement learning (MARL) model for auto-scaling decisions in an SDN environment. In particular, we study two possible alternatives for distributing operations: a collaborative one, where controllers share the same observations, and an individual one, where controllers make decisions according to their own logic and share only some basic information, such as the network topology. After an experimental campaign performed both on Mininet and GENI, results showed that both approaches can guarantee high throughput while minimizing the set of active resources.