Antonio Rodrigues

Antonio Rodrigues

Ph.D. Student in Computer Networking

Carnegie Mellon University & Faculty of Engineering of University of Porto

Short Bio

I'm a Ph.D. student enrolled in the Carnegie Mellon University | Portugal dual-degree Ph.D. program, in Electrical and Computer Engineering.

My research is mainly about Computer Networks, but I've also worked in other areas of Computer Science, such as Dependable and Fault-tolerant Systems. My advisors are Peter Steenkiste at CMU and Ana Aguiar at University of Porto.

Before the Ph.D., I was as a researcher at Fraunhofer Portugal AICOS, in Porto, Portugal. I've worked in different projects, which involved building systems related with embedded systems (hardware & software) and wireless sensor networks (mainly deployment and application layer development).

Publications & Projects

Enhancing WiFi Connectivity in 'Fast Mobility' Scenarios

My research relates to WiFi networks, focusing on serving clients that move at relatively high speeds (e.g., vehicles). More specifically, I'm interested in the design, implementation and evaluation of alternative AP selection methods for vehicles, taking advantage of 802.11 standard heterogeneity - e.g., 802.11n, ac, ad - and support from the infrastructure side.

Poster at CoNEXT'17 Student Workshop

Abstract at CoNEXT'17 Student Workshop

Improving VehicularWiFi Performance with Data-driven Brokerage (accepted Ph.D. proposal)

MU-MIMO in COTS 802.11ac devices

An experimental study about 802.11ac, using the TP-Link Talon AD7200 router. I was particularly interested in the conditions that trigger the usage of MU-MIMO, its performance and overhead. The resources below describe the experiments I've conducted and summarize their results.

Triggering the use of 802.11ac MU-MIMO with the TP-Link Talon AD7200

Variability of SU- and MU-MIMO parameters

Overhead of SU- and MU-MIMO channel sounding procedures

Sieve: Actionable Insights from Monitored Metrics in Distributed Systems

'Sieve' is a platform which derives actionable insights from monitored metrics in microservice applications. An example of 'actionable insight' is a set of rules for auto-scaling of microservices in Cloud deployments. On top of it, I have worked on a non-intrusive Root Cause Analysis method for anomalies detected in microservice applications, tested over a containerized deployment of Openstack.

Work developed while interning at NOKIA Bell Labs, in Stuttgart, Germany, under the supervision of Ruichuan Chen and Istemi Ekin Akkus. Plus, I came up with the system's name :P.

Paper in Middleware'17

View on GitHub

Enabling Name-based Packet Forwarding over Flat ID Network Architectures

Studied the tradeoffs involved in the usage of different types of content identifiers used in Information Centric Networks, namely hierarchical names and flat IDs. Focusing on the property of aggregation - essential for routing scalability, and exclusive to hierarchical identifiers - I’ve designed and implemented a method which allows for the usage of hierarchical names over flat ID ICNs. This method consists in encoding the inner prefixes of names into Bloom filters - which are effectively fixed-sized bit strings - keeping the property of aggregation.

I have also studied the influence of false positive matches on forwarding correctness. The study shows that forwarding errors increase when aggregation levels are high, uncovering a trade-off between forwarding efficiency and routing scalability. I also demonstrate the benefits of ‘namespace’ planning and in-network error resolution to mitigate the negative impact of such errors.

Paper in ICN'18

Data used in ICN'18 paper

Extended version of ICN'18 paper

False Positive Analysis tool, using Rocketfuel topologies (used in ICN'18 paper)

Implementation over the eXtensible Internet Architecture