BL‐Hybrid: A graph‐theoretic approach to improving software‐defined networking‐based data center network performance

AlShammari, Walaa M.; Alenazi, Mohammed J. F.

doi:10.1002/ett.4163

Cited by 4 publications

(4 citation statements)

References 40 publications

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“…This approach uses a dynamic threshold to pick the flow path and then saves them using the Ant Colony technique to determine the best path added to the best path list. Lan et al [38] introduced two methods for dynamic path optimization: single-link and multi-link Dynamic Load-Balanced Path Optimization (DLPO) [39] techniques. The flow route comprising the majority of the top 10% heavily loaded links is then modified to a route where the blockage route comprises the most available bandwidth.…”

Section: Related Workmentioning

confidence: 99%

Detection Collision Flows in SDN Based 5G Using Machine Learning Algorithms

Aqdus¹,

Amin²,

Ramzan³

et al. 2023

Computers, Materials &Amp; Continua

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The rapid advancement of wireless communication is forming a hyper-connected 5G network in which billions of linked devices generate massive amounts of data. The traffic control and data forwarding functions are decoupled in software-defined networking (SDN) and allow the network to be programmable. Each switch in SDN keeps track of forwarding information in a flow table. The SDN switches must search the flow table for the flow rules that match the packets to handle the incoming packets. Due to the obvious vast quantity of data in data centres, the capacity of the flow table restricts the data plane's forwarding capabilities. So, the SDN must handle traffic from across the whole network. The flow table depends on Ternary Content Addressable Memorable Memory (TCAM) for storing and a quick search of regulations; it is restricted in capacity owing to its elevated cost and energy consumption. Whenever the flow table is abused and overflowing, the usual regulations cannot be executed quickly. In this case, we consider lowrate flow table overflowing that causes collision flow rules to be installed and consumes excessive existing flow table capacity by delivering packets that don't fit the flow table at a low rate. This study introduces machine learning techniques for detecting and categorizing low-rate collision flows table in SDN, using Feed Forward Neural Network (FFNN), K-Means, and Decision Tree (DT). We generate two network topologies, Fat Tree and Simple Tree Topologies, with the Mininet simulator and coupled to the OpenDayLight (ODL) controller. The efficiency and efficacy of the suggested algorithms are assessed using several assessment indicators such as success rate query, propagation delay, overall dropped packets, energy consumption, bandwidth usage, latency rate, and throughput. The findings showed that the suggested technique to tackle the flow table congestion problem minimizes the number of flows while retaining the statistical consistency of the 5G network. By putting the proposed flow method and checking whether a packet may move 1414

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Section: Related Workmentioning

confidence: 99%

Detection Collision Flows in SDN Based 5G Using Machine Learning Algorithms

Aqdus¹,

Amin²,

Ramzan³

et al. 2023

Computers, Materials &Amp; Continua

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“…To improve the outcomes, some approaches utilize the concept of SDN principles [34]. The authors make use of graph theory to maximize the throughput by identifying the optimum association graph between wireless devices and APs [35]. All APs must work on the same channel to maintain the required throughput.…”

Section: B Throughput Maximizationmentioning

confidence: 99%

TiWA: Achieving Tetra Indicator Wi-Fi Associations in Software Defined Wi-Fi Networks

Manzoor,

Kayani,

Ali

et al. 2023

IEEE Access

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With the rise in smart devices, challenges such as traffic management particularly in the indoor scenarios have also increased. Wireless technologies seek to address these needs by using new radio access technologies that offer faster connectivity and access to the Internet. While 4G and 5G provide faster connectivity outdoor, Wi-Fi still remains the dominant technology in indoor scenarios due to its low cost and easy deployment. However, the IEEE 802.11 standard does not address the fairness of load in user-access point (AP) associations, resulting in unequal user distributions and subpar resource utilization. Densification has been employed in the past to address the aforementioned issue, but it is costly as it requires more hardware. Load balancing is necessary in high-density Wi-Fi networks in order to guarantee quality of service (QoS). To avoid the densification and issues related to load balancing at the APs this paper introduces tetra indicator Wi-Fi associations (TiWA), a combined SDN-based user association and channel assignment solution for Wi-Fi networks. TiWA considers multi-criteria to make better association decisions. The wireless stations from an overloaded OpenFlow enabled AP (OAP) are handed to an underloaded OAP by considering multi-metrics such as the packet loss rate, received signal strength indicator (RSSI), throughput and channel occupancy. Extensive experimentations on 5G EmPOWER platform show that TiWA surpasses previous research methods based on load balancing in terms of aggregated throughput by 39%, offering superior level of load fairness and supporting greater numbers of users and traffic prior to hitting the point at which densification is required.

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“…8,9 In recent years, its use for quantifying resilience has evolved to become an essential component in multiple applications within the field of computer network security. [10][11][12][13][14] One of the main resilience metrics, referred to as robustness, is the ability of a network to keep providing the same level of service in the presence of disruptions. 6,7 In general, resilience is determined by centrality measures that quantify the significance of nodes; robustness is evaluated after the node with the highest centrality measure has been attacked.…”

Section: Introductionmentioning

confidence: 99%

“…Graph theory has long been used for network analysis, including designing, modeling, and optimizing networks 8,9 . In recent years, its use for quantifying resilience has evolved to become an essential component in multiple applications within the field of computer network security 10‐14 . One of the main resilience metrics, referred to as robustness, is the ability of a network to keep providing the same level of service in the presence of disruptions 6,7 .…”

Section: Introductionmentioning

confidence: 99%

Evaluation of industrial network robustness against targeted attacks

Alrumaih,

Alenazi

2023

Concurrency and Computation

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SummaryDeveloping a long‐lasting, secure Industry 4.0 system presents a significant challenge for businesses and other interested parties. Industrial control systems (ICSs) are particularly vulnerable to cybercrime because of the operating systems' excessive availability and high robustness requirements. This research investigates five graph‐theory‐based measures to evaluate the robustness of industrial network topologies against three centrality‐based attacks and one random attack. Experiments are conducted to examine the three levels of the ICS network topology, from the field devices to the controllers and the enterprise devices. The results are twofold. On the one hand, the closeness‐based attack is the most harmful since it has the highest destructive potential and needs to attack only half of the total nodes in the network to reach the lowest robustness level. The betweenness‐based attack follows closely in terms of destruction, whereas the degree‐based attack is less destructive but rapidly degrades the robustness of the network. On the other hand, the flow robustness measure provides the best performance in the presence of any of the studied attacks, showing strong perception of robustness reduction when only one percent of the total nodes in the network are attacked. For this reason, the flow robustness measure is suitable to identify and locate the targeted attacks at their early stages, preventing them from becoming more catastrophic. Finally, the results suggest that the industrial network security system should combine at least two measures to ensure robustness against the most destructive attacks and their early‐stage detection. The research also confirmed the results by implementing attacks and measures on a real gas transmission network.

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BL‐Hybrid: A graph‐theoretic approach to improving software‐defined networking‐based data center network performance

Cited by 4 publications

References 40 publications

Detection Collision Flows in SDN Based 5G Using Machine Learning Algorithms

Detection Collision Flows in SDN Based 5G Using Machine Learning Algorithms

TiWA: Achieving Tetra Indicator Wi-Fi Associations in Software Defined Wi-Fi Networks

Evaluation of industrial network robustness against targeted attacks

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