This research aims to improve network routing and resource management mechanisms for Computer Networks, Distributed Systems, Cloud and Edge Computing.

Server and Route Selection Optimization for Knowledge-defined Distributed Network Based on Gambling Theory and LSTM Neural Networks

Server and route selection (SARS) optimization is a critical aspect of traffic engineering to allocate network resources to meet diverse service requirements effectively. Existing studies have primarily focused on finding profitable or optimal solutions for the SARS problem within current time steps, considering specific constraints. However, they often have failed to address the dynamic and uncertainty of future network states. To address this gap, this paper proposes an algorithm named GAL to optimize server costs and response time while accounting for future network dynamics. GAL combines a server selection inspired by the gambling theory and a network routing based on Long Short-Term Memory Networks (LSTM). The server selection method is formulated as a gambling problem and solved using the decision-making Tug-of-War (TOW) dynamic algorithm. The routing mechanism is optimized based on predictions of future network states made by LSTM neural networks, which excel in capturing long-term dependencies. We have implemented GAL through a distributed software-defined networking (SDN) system and obtained good evaluation results regarding average response time and server cost compared to benchmark methods. These results demonstrate that GAL can effectively tackle the SARS optimization problem by considering present constraints and future network dynamics. This study can advance traffic engineering and lays a foundation for more robust resource allocation strategies in dynamic network environments.

• Duong Son, Nguyen Tuan, Hoang Nam-Thang, Tong Van, Tran Hai-Anh, Nguyen Giang, Mellouk Abdelhamid, Tran Truong (2023). Server and Route Selection Optimization for Knowledge-defined Distributed Network Based on Gambling Theory and LSTM Neural Networks. 2023 IEEE Global Communications Conference (GLOBECOM), pp. 413-418, https://doi.org/10.1109/GLOBECOM54140.2023.10437727

Multi Service-Oriented Routing Mechanism for Heterogeneous Multi-Domain Software-Defined Networking

SDN has some challenges with scalability and quality of services (QoS) in distributed multi-domain scenarios due to the unprecedented growth of heterogeneous characteristics services. There is a current gap in a standard routing mechanism for satisfying various service requirements in distributed SDN. Most existing works design a homogeneous routing strategy for heterogeneous services, which might need to be more scalable and efficient for the future of rising heterogeneous online services. This study proposes a multi service-oriented routing mechanism for multi-domain SDN, which aims to help Internet service providers (ISPs) achieve high QoS and service-level agreements (SLAs). The mechanism utilizes a service classification (through a deep learning model) and optimizes network routing (using a new cost function containing both QoS and the server load). The mechanism has been integrated into the Knowledge-defined heterogeneous network architecture and tested on four prevalent considered services: E-commerce, Interactive Data, Video On-demand, and Bulk Data Transfer. The experimental results indicate that the proposed service-oriented routing mechanism outperforms the benchmark in terms of faster server response time while reducing up to 25% of the network congestion.

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• Nguyen Tuan, Ngo Hoang, Pham Trung, Hoang Nam-Thang, Tong Van, Tran Hai-Anh, Nguyen Giang, Mellouk Abdelhamid, Tran Truong (2023). Multi Service-Oriented Routing Mechanism for Heterogeneous Multi-Domain Software-Defined Networking. 2023 IEEE Global Communications Conference (GLOBECOM) , pp. 1271-1276, https://doi.org/10.1109/GLOBECOM54140.2023.10437532

A New Transfer Learning-Based Traffic Classification Algorithm for a Multi-Domain SDN Network

To enhance the efficiency and resource utilization of a computer network, it is imperative to classify network traffic and implement distinct priority policies. Network traffic classification plays a pivotal role across various domains, including network administration, cybersecurity, and network resource optimization. As encrypted network data undergoes diverse evolution, as evident in datasets from tech giants like Google, Facebook, and YouTube, traditional traffic classification methods have given way to machine learning-based approaches. Given that computer networks are primarily deployed as distributed multi-domain systems, employing machine learning for traffic classification becomes challenging when a new network domain appears with a limited dataset. One potential remedy is to employ transfer learning, allowing knowledge transfer from a pre-trained model in an established domain to a new one. In this paper, we present two contributions. First, a novel algorithm called Multi-class TrAdaBoost-CNN is introduced to tackle the challenge of cross-domain classification in encrypted network services. This algorithm extends the Multi-class TrAdaBoost approach by incorporating a Convolutional Neural Network (CNN) as a weak learner. Secondly, extensive experiments are conducted on two distinct domains characterized by imbalanced data distributions to assess the efficacy of our proposed method. The experimental results clearly demonstrate that our algorithm outperforms the traditional CNN model, achieving remarkable accuracy improvements of up to 16%, even when dealing with extremely limited data.

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• Nam-Thang Hoang, Cong-Son Duong, Minh-Ngoc Vu, Huy-Hieu Nguyen, Xuan-Truong Tran, Van Tong, and Hai Anh Tran (2023). A New Transfer Learning-Based Traffic Classification Algorithm for a Multi-Domain SDN Network. In Proceedings of the 12th International Symposium on Information and Communication Technology (SOICT ’23), Association for Computing Machinery (ACM) 235–242. https://doi.org/10.1145/3628797.3628804

Enhancing Encrypted Traffic Classification with Deep Adaptation Networks. 2023 IEEE 48th Conference on Local Computer Networks (LCN)

Classifying network traffic is the foundation for enhancing the quality of management mechanisms. However, traditional traffic classification methods, such as port-based, deep packet inspection, and statistic-based, are limited in identifying new encrypted traffic characteristics. Deep Learning-based classification approaches that consider packet-based features have been explored to address this challenge. Along with other deep learning methods, Transfer Learning, where a new model can inherit knowledge previously learned by a base model, is commonly used to increase classification performance in low data resources. Unfortunately, feature transferability may decline in transfer learning. This paper proposes an encrypted traffic classification mechanism that leverages the Deep Adaptation Network architecture with Mean Embedding Test to overcome this limitation. Our experimental results show that the proposed mechanism surpasses existing benchmarks’ accuracy and can classify encrypted traffic in real-time.

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• Dao C, Tong V, Hoang N, Tran H, Tran Truong (2023). Enhancing Encrypted Traffic Classification with Deep Adaptation Networks. 2023 IEEE 48th Conference on Local Computer Networks (LCN). 2023 IEEE 48th Conference on Local Computer Networks (LCN); Daytona Beach, FL, USA. IEEE; https://doi.org/10.1109/LCN58197.2023.10223333

V2V Communications Using Blockchain-Enabled 6G Technology and Federated Learning

This study proposes an interesting approach for vehicle-to-vehicle (V2V) communication, which integrates blockchain technology, federated learning (FL), and allocation optimization of latency and resources. The research evaluates the proposed system using various performance metrics such as packet delivery ratio (PDR), model accuracy, and latency and demonstrates its superiority over existing techniques. Furthermore, the system provides enhanced security through consensus optimization and k-anonymity for data privacy. Overall, the proposed system is a promising solution for efficient and secure V2V communication in the era of connected and autonomous vehicles. Moreover, the proposed approach achieves higher reliability, lower latency, and better resource utilization compared to traditional 5G.

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• Ahmed Tahir, Tiang Jun Jiat, Mahmud Azwan, Do Dinh-Thuan, Tran Truong, Mumtaz Shahid (2023). V2V Communications Using Blockchain-Enabled 6G Technology and Federated Learning. 2023 IEEE Global Communications Conference (GLOBECOM), pp. 1302-1307, https://doi.org/10.1109/GLOBECOM54140.2023.10437406