COMM

We present the design and early implementation of p4rl, a system that uses reinforcement learning-guided fuzz testing to execute the verification of P4 switches automatically at runtime. p4rl system uses our novel user-friendly query language, p4q to ...

Communication networks have not only become a critical infrastructure of our digital society, but are also increasingly complex and hence error-prone. This has recently motivated the study of more automated and "self-driving" networks: networks which ...

Access to packet traces is required not only to detect and diagnose various network issues related to performance and security, but also to train intelligent learning models enabling networks that can run themselves. However, packets in a network carry ...

In mobile network, a complaint hotspot problem often affects even thousands of users' service and leads to significant economic losses and bulk complaints. In this paper, we propose an approach to predict a customer complaint based on real-time user ...

Machine learning (ML) solutions to challenging networking problems, while promising, are hard to interpret; the uncertainty about how they would behave in untested scenarios has hindered adoption. Using a case study of an ML-based video rate adaptation ...

Transport protocols use congestion control to avoid overloading a network. Nowadays, different congestion control variants exist that influence performance. Studying their use is thus relevant, but it is hard to identify which variant is used. While ...

Cellular networks dynamically adjust the transmission parameters for a wireless link in response to its time-varying channel state. This is known as link adaptation, where the typical goal is to maximize the link throughput. State-of-the-art outer loop ...

In this paper, we study the network traffic classification task. Different from existing supervised methods that rely heavily on the labeled statistic features in a long period (e.g., several hours or days), we adopt a novel view of unsupervised ...

Content delivery networks (CDNs) distribute much of the Internet content by caching and serving the objects requested by users. A major goal of a CDN is to maximize the hit rates of its caches, thereby enabling faster content downloads to the users. ...

Packet networks are agnostic to applications, which have served to keep the Internet infrastructure simple and scalable over the past several decades. However, the best-effort model is now seen as an inhibitor to meeting user experience expectations for ...

Network monitoring has been evolving over several years to be able to identify and react to issues at a faster rate to reduce network downtime. With the expansion of cloud-users and the need for higher networking capability, the deployments are vast and ...

With the recent advances in wireless technologies, base stations are becoming more sophisticated. Network operators are also able to collect more data to improve network performance and user experience. In this paper we concentrate on modeling ...

Deep reinforcement learning (RL) has recently been successfully applied to networking contexts including routing, flow scheduling, congestion control, packet classification, cloud resource management, and video streaming. Deep-RL-driven systems automate ...