An SDN-based energy-aware traffic management mechanism

Vieira, Alex Borges; Paraizo, Wallace Nascimento; Chaves, Luciano Jerez; Correia, Luiz H. A.; Silva, Edelberto Franco

doi:10.1007/s12243-021-00863-x

Cited by 8 publications

(6 citation statements)

References 17 publications

(28 reference statements)

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“…In Figure 5 below, we compare the proposed approach (SDN orchestration with GNN and multi-arm bandit) with other state-of-the-art traffic management methods, including SDN [ 22 ] and SDM + ML [ 30 ]. The x-axis represents the different traffic management policies used, and the y-axis represents the throughput of the network traffic.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…This work highlights the potential of SDN in addressing complexity and performance costs associated with SAVI while maintaining network security. Finally, in [ 22 ], the authors propose work on DTM (Dynamic mechanism for Traffic Management), a promising energy-aware dynamic mechanism for traffic management. By leveraging the SDN paradigm, DTM effectively adjusts network resources to prevent over-provisioning and reduce power consumption during low-demand periods without compromising the quality of service.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The use of the multi-arm bandit algorithm for optimizing traffic management policies based on the GNN model's predictions also shows promising results in improving network throughput, reducing packet loss, and minimizing delay. In Figure 5 below, we compare the proposed approach (SDN orchestration with GNN and multi-arm bandit) with other state-of-the-art traffic management methods, including SDN [22] and SDM + ML [30]. The x-axis represents the different traffic management policies used, and the y-axis represents the throughput of the network traffic.…”

Section: Training and Validationmentioning

confidence: 99%

See 2 more Smart Citations

Traffic Management in IoT Backbone Networks Using GNN and MAB with SDN Orchestration

Guo¹,

Yu²,

Khan

et al. 2023

Sensors

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Traffic management is a critical task in software-defined IoT networks (SDN-IoTs) to efficiently manage network resources and ensure Quality of Service (QoS) for end-users. However, traditional traffic management approaches based on queuing theory or static policies may not be effective due to the dynamic and unpredictable nature of network traffic. In this paper, we propose a novel approach that leverages Graph Neural Networks (GNNs) and multi-arm bandit algorithms to dynamically optimize traffic management policies based on real-time network traffic patterns. Specifically, our approach uses a GNN model to learn and predict network traffic patterns and a multi-arm bandit algorithm to optimize traffic management policies based on these predictions. We evaluate the proposed approach on three different datasets, including a simulated corporate network (KDD Cup 1999), a collection of network traffic traces (CAIDA), and a simulated network environment with both normal and malicious traffic (NSL-KDD). The results demonstrate that our approach outperforms other state-of-the-art traffic management methods, achieving higher throughput, lower packet loss, and lower delay, while effectively detecting anomalous traffic patterns. The proposed approach offers a promising solution to traffic management in SDNs, enabling efficient resource management and QoS assurance.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Training and Validationmentioning

confidence: 99%

See 1 more Smart Citation

Traffic Management in IoT Backbone Networks Using GNN and MAB with SDN Orchestration

Guo¹,

Yu²,

Khan

et al. 2023

Sensors

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“…Various layers and strategies are employed to address energy-saving aspects within SDN. These strategies encompass enhancements in hardware design, network planning, network operations, component placement, dynamic activation and deactivation of network elements, and resource allocation [11,13]. The allocation of network resources is contingent on meeting demands, and, in certain cases, fulfilling a demand may necessitate the activation of all available network resources [14].…”

Section: The Problemmentioning

confidence: 99%

Energy-and-Blocking-Aware Routing and Device Assignment in Software-Defined Networking—A MILP and Genetic Algorithm Approach

Riveros-Rojas,

Cespedes-Sanchez,

Pinto-Roa

et al. 2024

MCA

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Internet energy consumption has increased rapidly, and energy conservation has become a significant issue that requires focused research efforts. The most promising solution is to identify the minimum power subsets within the network and shut down unnecessary network devices and links to satisfy traffic loads. Due to their distributed network control, implementing a centralized and coordinated strategy in traditional networks is challenging. Software-Defined Networking (SDN) is an emerging technology with dynamic, manageable, cost-effective, and adaptable solutions. SDN decouples network control and forwarding functions, allowing network control to be directly programmable, centralizing control with a global network view to manage power states. Nevertheless, it is crucial to develop efficient algorithms that leverage the centralized control of SDN to achieve maximum energy savings and consider peak traffic times. Traffic demand usually cannot be satisfied, even when all network devices are active. This work jointly addresses the routing of traffic flows and the assignment of SDN devices to these flows, called the Routing and Device Assignment (RDA) problem. It simultaneously seeks to minimize the network’s energy consumption and blocked traffic flows. For this approach, we develop an exact solution based on Mixed-Integer Linear Programming (MILP) as well as a metaheuristic based on a Genetic Algorithm (GA) that seeks to optimize both criteria by routing flows efficiently and suspending devices not used by the flows. Conducted simulations on traffic environment scenarios show up to 34% savings in overall energy consumption for the MILP and 33% savings achieved by the GA. These values are better than those obtained using competitive state-of-the-art strategies.

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“…Uma soluc ¸ão interessante é aplicar os conceitos de Software-Defined Networking (SDN), que isolam naturalmente os planos de controle e dados, a fim de oferecer suporte ao provisionamento rápido de servic ¸os, e minimizar as restric ¸ões de hardware [Fu et al 2018]. Por exemplo, a proposta de balanceamento de carga apresentado por [Vieira et al 2021], determina, com base nas informac ¸ões de carga da rede e topologia, quais roteadores devem ser mantidos ligados ou não, melhorando assim a gestão dos recursos.…”

Section: Trabalhos Relacionadosunclassified

Alocação de Recursos em Redes Sem Fio de Grande Porte por meio de Meta-Heurísticas e Predição da Carga de Usuários

Frank

Carnevale

Galletta

et al. 2022

Anais Do VI Workshop De Computação Urbana (CoUrb 2022)

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Com o aumento significativo de usuários móveis, a busca pelo gerenciamento de recursos tornou-se essencial. Essa gerência deve visar atender à cobertura do sinal, mas, principalmente, manter o Acordo de Nível de Serviço desejado, independentemente do número de usuários conectados. Assim, propomos a utilização de quatro modelos de predição aplicado ao número de usuários conectados em uma rede sem fio. A partir dessas previsões, os recursos de rede podem ser alocados adequadamente. Investigamos o uso de Otimização de Enxame de Partículas e Algoritmo Genético para hiperparametrizar um Perceptron Multicamadas e uma Árvore de Decisão. Avaliamos nossa proposta utilizando dados reais de rede sem fio com mais de 20 mil usuários. Como resultado, obtivemos uma precisão média de 94,80%, melhorando consideravelmente a utilização de recursos da rede e atendendo um nível de acordo de serviço de 95%.

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An SDN-based energy-aware traffic management mechanism

Cited by 8 publications

References 17 publications

Traffic Management in IoT Backbone Networks Using GNN and MAB with SDN Orchestration

Traffic Management in IoT Backbone Networks Using GNN and MAB with SDN Orchestration

Energy-and-Blocking-Aware Routing and Device Assignment in Software-Defined Networking—A MILP and Genetic Algorithm Approach

Alocação de Recursos em Redes Sem Fio de Grande Porte por meio de Meta-Heurísticas e Predição da Carga de Usuários

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