Explainable reinforcement learning for routing in software-defined networking

• Grant number: 22/08847-1
• Support Opportunities: Research Grants - Visiting Researcher Grant - International
• Start date: November 09, 2022
• End date: December 08, 2022
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Nelson Luis Saldanha da Fonseca
• Grantee: Nelson Luis Saldanha da Fonseca
• Visiting researcher: Oscar Mauricio Caicedo Rendon
• Visiting researcher institution: Universidad del Cauca (Unicauca) , Colombia
• Host Institution: Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• City of the host institution: Campinas
• Associated research grant: 15/24494-8 - Communications and processing of big data in cloud and fog computing, AP.TEM

Abstract

This proposal of visiting researcher focuses on addressing the following question: How to provide an efficient, intelligent, and explainable routing scheme for SDN? We argue that eXplainable Reinforcement Learning (XRL) and eXplainable Deep Reinforcement Learning (XDRL), fields of eXplainable Artificial Intelligence (XAI) that have attracted considerable attention recently, allowing networking stakeholders to make RL and DRL models interpretable, manageable, and trustworthy. XRL and XDRL lead to understanding the reasoning and actions performed by RL and DRL agents in making their decisions. However, designing appropriately fast and accurate XAI and XRL/XDRL models, in particular, is an open research challenge. There is a tremendous need for achieving the interpretability and explainability of closed-box methods working with agents that act autonomously in the real world as RL and DRL agents do. Specifically, we will explore explainability in our SDN routing solutions, RSIR and DRSIR. RSIR adds a Knowledge Plane and defines a routing algorithm based on Q-learning that considers link-state information to explore, learn, and exploit potential paths for intelligent routing, even during dynamic traffic changes. This algorithm capitalizes on interaction with the environment, the intelligence provided by RL, and the global view and control of the network furnished by SDN. It computes and installs, in advance, optimal routes in the routing tables of the switches on the Data Plane. DRSIR enhances RSIR by using DQN with path-state metrics and Target and Online Neural Networks (NNs). Using path-state metrics reduces the knowledge abstraction needed by the routing agent since this approach directly explores different path options instead of link state information. Target and Online NNs allow DRSIR to reduce the error in estimations based on path information. Moreover, the DQN agent uses Experience Replay Memory to accelerate learning. (AU)