Comparison of Routing Algorithms with Static and Dynamic Link Cost in SDN

Akin, Erdal; Korkmaz, Turgay

doi:10.1109/ccnc.2019.8651815

Cited by 9 publications

(3 citation statements)

References 21 publications

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“…To achieve such a separation, three types of components are needed: (i) a (logically) centralized SDN controller, (ii) SDN-capable switches, and (iii) a management protocol, such as OpenFlow [30]. An advantage of SDN is the capability of centrally updating routes of existing traffic on demand by the controller [31]- [38] in a consistent manner [16].…”

Section: Background and Related Work A Software-defined Networkimentioning

confidence: 99%

QR-SDN: Towards Reinforcement Learning States, Actions, and Rewards for Direct Flow Routing in Software-Defined Networks

et al. 2020

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Flow routing can achieve fine-grained network performance optimizations by routing distinct packet traffic flows over different network paths. While the centralized control of Software-Defined Networking (SDN) provides a control framework for implementing centralized network optimizations, e.g., optimized flow routing, the implementation of flow routing that is adaptive to varying traffic loads requires complex models. The goal of this study is to pursue a model-free approach that is based on reinforcement learning. We design and evaluate QR-SDN, a classical tabular reinforcement learning approach that directly represents the routing paths of individual flows in its state-action space. Due to the direct representation of flow routes in the QR-SDN state-action space, QR-SDN is the first reinforcement learning SDN routing approach to enable multiple routing paths between a given source (ingress) switch-destination (egress) switch pair while preserving the flow integrity. That is, in QR-SDN, packets of a given flow take the same routing path, while different flows with the same source-destination switch pair may take different routes (in contrast, the recent DRL-TE approach splits a given flow on a per-packet basis incurring high complexity and out-of-order packets). We implemented QR-SDN in a Software-Defined Network (SDN) emulation testbed. Our evaluations demonstrate that the flow-preserving multi-path routing of QR-SDN achieves substantially lower flow latencies than prior routing approaches that determine only a single source-destination route. A limitation of QR-SDN is that the state-action space grows exponentially with the number of network nodes. Addressing the scalability of direct flow routing, e.g., through routing only high-rate flows, is an important direction for future research. The QR-SDN code is made publicly available to support this future research.

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Section: Background and Related Work A Software-defined Networkimentioning

confidence: 99%

QR-SDN: Towards Reinforcement Learning States, Actions, and Rewards for Direct Flow Routing in Software-Defined Networks

et al. 2020

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“…Although dynamic routing can perform rerouting under certain pre-defined conditions by periodically monitoring the status of each data flow, problems concerning a suitable monitoring period duration and the lack of learning ability from past experiences to avoid similar but ineffective path decisions remain unsolved. The authors of [31] emphasized that the monitoring period duration has a significant impact on network performance. A monitoring period that is too short may cause excessive communication burden on the southbound interface.…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence Enabled Routing in Software Defined Networking

Hwang²,

Hwang³

et al. 2020

Applied Sciences

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Software defined networking (SDN) is an emerging networking architecture that separates the control plane from the data plane and moves network management to a central point, called the controller. The controller is responsible for preparing the flow tables of each switch in the data plane. Although dynamic routing can perform rerouting in case of congestion by periodically monitoring the status of each data flow, problems concerning a suitable monitoring period duration and lack of learning ability from past experiences to avoid similar but ineffective route decisions remain unsolved. This paper presents an artificial intelligence enabled routing (AIER) mechanism with congestion avoidance in SDN, which can not only alleviate the impact of monitoring periods with dynamic routing, but also provide learning ability and superior route decisions by introducing artificial intelligence (AI) technology. We evaluate the performance of the proposed AIER mechanism on the Mininet simulator by installing three additional modules, namely, topology discovery, monitoring period, and an artificial neural network, in the control plane. The effectiveness and superiority of our proposed AIER mechanism are demonstrated by performance metrics, including average throughput, packet loss ratio, and packet delay versus data rate for different monitoring periods in the system.

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“…As network size increases and the enormous number of sensor nodes become a part of WSN, the network scalability with efficient data routing gain a lot of popularity between the research communities. The routing protocols can be categorized for data dissemination either in static or dynamic methods [11][12][13]. In the static method, the routing tables of gateway nodes and routers are manually formed and maintained.…”

Section: Iintroductionmentioning

confidence: 99%

Secure and Energy-Aware Heuristic Routing Protocol for Wireless Sensor Network

et al. 2020

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Wireless Sensor Networks (WSNs) achieve much attention from various domains because of its easy maintenance, self-configuration, and scalability characteristics. It is comprised of small-sized sensors that interact with the Internet of Things (IoT) for observing and recording the physical conditions. The sensor nodes are autonomous and construct inter-communication topology with each other in an ad-hoc manner. However, the main restrictions of sensor nodes are their finite resources for energy management, data storage, transmission, and processing power. Different solutions have been addressed by researchers to overcome network performance due to bounded limitations of such battery-powered nodes, however, equalize the energy consumption and maintain the network throughput are the main research problems. Furthermore, due to the compromised nodes, the data is more prone to security vulnerabilities. Therefore, their security over the unpredictable network is other research concerns. Thus, the aim of this research article to propose a secure and energy-aware heuristic-based routing (SEHR) protocol for WSN to detect and prevent compromising data with efficient performance. Firstly, the proposed protocol makes use of an artificial intelligence-based heuristic analysis to accomplish a reliable, and intellectual learning scheme. Secondly, it protects the transmissions against adversary groups to attain security with the least complexity. Moreover, the route maintenance strategy is also achieved by using traffic exploration to reduce link failures and network dis-connectivity. The simulation results demonstrated the SEHR protocol improves the efficacy for network throughput by an average of 18%, packet drop ratio by 42%, end-to-end delay by 26%, energy consumption by 36%, faulty routes by 38%, network overhead by 44%, and computational overhead by 43% in dynamic scenarios as compared to existing work.

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Comparison of Routing Algorithms with Static and Dynamic Link Cost in SDN

Cited by 9 publications

References 21 publications

QR-SDN: Towards Reinforcement Learning States, Actions, and Rewards for Direct Flow Routing in Software-Defined Networks

QR-SDN: Towards Reinforcement Learning States, Actions, and Rewards for Direct Flow Routing in Software-Defined Networks

Artificial Intelligence Enabled Routing in Software Defined Networking

Secure and Energy-Aware Heuristic Routing Protocol for Wireless Sensor Network

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