The computer networking community has been steadily increasing investigations into machine learning to help solve tasks such as routing, traffic prediction, and resource management. The traditional best-effort nature of Internet connections allows a single link to be shared among multiple flows competing for network resources, often without consideration of in-network states. In particular, due to the recent successes in other applications, Reinforcement Learning has seen steady growth in network management and, more recently, routing. However, if there are changes in the network topology, retraining is often required to avoid significant performance losses. This restriction has chiefly prevented the deployment of Reinforcement Learning-based routing in real environments. In this paper, we approach routing as a reinforcement learning problem with two novel twists: minimize flow set collisions, and construct a reinforcement learning policy capable of routing in dynamic network conditions without retraining. We compare this approach to other routing protocols, including multi-agent learning, with respect to various Quality-of-Service metrics, and we report our lesson learned.
@article{routing-tnsm,author={Bhavanasi, Sai Shreyas and Pappone, Lorenzo and Esposito, Flavio},journal={IEEE Transactions on Network and Service Management},title={Dealing with Changes: Resilient Routing via Graph Neural Networks and Multi-Agent Deep Reinforcement Learning},year={2023},volume={},number={},pages={1-1},keywords={},url={https://www.comsoc.org/publications/journals/ieee-tnsm},issn={1932-4537},month={},}
CCNC ’23
A Federated Learning Approach to Traffic Matrix Estimation using Super-resolution Techniques
Roberto Amoroso, Lorenzo Pappone, and Flavio Esposito
Network measurement and telemetry techniques are central to the management of modern computer networks. Traffic matrix estimation is a popular technique that supports several applications. Existing approaches use statistical methods, which often make invalid assumptions about the structure of the traffic matrix. Data-driven methods, instead, leverage detailed information about the network topology that may be unavailable or impractical to collect. In this work, we propose a super-resolution technique for traffic matrix estimation that can infer fine-grained network traffic. In our experiment, we demonstrate that the proposed approach with high precision outperforms existing data interpolation techniques. We also expand our design by employing a federated learning model to address scalability and improve performance. We find that our model increases the accuracy of the inference with respect to its centralized counterpart.
@article{federated,author={Amoroso, Roberto and Pappone, Lorenzo and Esposito, Flavio},booktitle={2023 IEEE Consumer Communications & Networking Conference (CCNC)},title={A Federated Learning Approach to Traffic Matrix Estimation using Super-resolution Techniques},year={2023},volume={},number={},pages={},doi={10.1109/CCNC51644.2023.10060210},url={https://ccnc2023.ieee-ccnc.org},}
Under Review
Mimic: Learning Congestion Control from Existing Transport Protocols via Contextual Multi-Arm Bandit
L Pappone, A Sacco, and F Esposito
2023
Under Review
ResCue: Inferring Internet Traffic Visibility via Distributed Deep Residual Networks
L Pappone, and F Esposito
2023
Under Review
Meta-WGAN: a Meta-Learning Generative Adversiarial Network for Network Intrusion Detection with Limited Data
L Pappone, F Esposito, and I Matta
2023
2022
NFV-SDN ’22
Routing with Graph Convolutional Networks and Multi-Agent Deep Reinforcement Learning
Sai Shreyas Bhavanasi, Lorenzo Pappone, and Flavio Esposito
The computer networking community has been steadily increasing investigations into machine learning to help solve tasks such as routing, traffic prediction, and resource management. In particular, due to the recent successes in other applications, Reinforcement Learning (RL) has seen steady growth in network management and, more recently, in routing. However, changes in the network topology prevent RL-based routing approaches from being employed in real environments due to the need for retraining. In this paper, we approach routing as an RL problem with two novel twists: minimizing flow set collisions and dealing with routing in dynamic network conditions without retraining. We compare this approach to other routing protocols, including multi-agent learning, to various Quality-of-Service metrics, and we report our lesson learned.
@article{routing,author={Bhavanasi, Sai Shreyas and Pappone, Lorenzo and Esposito, Flavio},booktitle={2022 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)},title={Routing with Graph Convolutional Networks and Multi-Agent Deep Reinforcement Learning},year={2022},volume={},number={},pages={72-77},url={https://nfvsdn2022.ieee-nfvsdn.org},}
IFIP ’22
Prediction of Mobile-App Network-Video-Traffic Aggregates using Multi-task Deep Learning
Lorenzo Pappone, Francesco Cerasuolo, Valerio Persico, and 3 more authors
Traffic prediction has proven to be useful for several network management domains and represents one of the main enablers for instilling intelligence within future networks. Recent solutions have focused on predicting the behavior of traffic aggregates. Nonetheless, minimal attempts have tackled the prediction of mobile network traffic generated by different video application categories. To this end, in this work we apply Multi-task Deep Learning to predict network traffic aggregates generated by mobile video applications over short-term time scales. We investigate our approach leveraging state-of-art prediction models such as Convolutional Neural Networks, Gated Recurrent Unit, and Random Forest Regressor, showing some surprising results (e.g. NRMSE < 0.075 for upstream packet count prediction while NRMSE < 0.15 for the downstream counterpart), including some variability in prediction performance among the examined video application categories. Furthermore, we show that using smaller time intervals when predicting traffic aggregates may achieve better performances for specific traffic profiles.
@article{mobileprediction,author={Pappone, Lorenzo and Cerasuolo, Francesco and Persico, Valerio and Ciuonzo, Domenico and Pescapé, Antonio and Esposito, Flavio},booktitle={2022 IFIP Networking Conference (IFIP Networking)},title={Prediction of Mobile-App Network-Video-Traffic Aggregates using Multi-task Deep Learning},year={2022},volume={},number={},pages={1-6},doi={10.23919/IFIPNetworking55013.2022.9829800},url={https://networking.ifip.org/2022/},}