Reinforcement learning (RL), which is a class of machine learning, provides a framework by
which a system can learn from its previous interactions with its environment to efficiently …

After decades of unprecedented development, modern networks have evolved far beyond
expectations in terms of scale and complexity. In many cases, traditional traffic engineering …

Network modeling is a key enabler to achieve efficient network operation in future self-
driving Software-Defined Networks. However, we still lack functional network models able to …

Network models are an essential block of modern networks. For example, they are widely
used in network planning and optimization. However, as networks increase in scale and …

Abstract Deep Reinforcement Learning (DRL) has shown a dramatic improvement in
decision-making and automated control problems. Consequently, DRL represents a …

Recently, we have seen a boom of attempts to improve the operation of networking protocols
using machine learning techniques. The proposed reinforcement learning (RL) based …

Network planning is critical to the performance, reliability and cost of web services. This
problem is typically formulated as an Integer Linear Programming (ILP) problem. Today's …

Network modeling is a critical component for building self-driving Software-Defined
Networks, particularly to find optimal routing schemes that meet the goals set by …

Traditional Traffic Engineering (TE) solutions can achieve the optimal or near-optimal
performance by rerouting as many flows as possible. However, they do not usually consider …

Traditional routing algorithms cannot dynamically change network environments due to the
limited information for routing decisions. Meanwhile, they are prone to performance …