CentFlow: Centrality-Based Flow Balancing and Traffic Distribution for Higher Network Utilization

Challa, Rajesh; Jeon, Seil; Kim, Dongsoo S.; Choo, Hyunseung

doi:10.1109/access.2017.2743697

Cited by 5 publications

(5 citation statements)

References 42 publications

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“…Despite an uncongested network, traffic balancing is still desirable to prevent future congestion. Challa et al [33] proposed a CentFlow routing algorithm to enhance the node and link utilization depending on the centrality measures and temporal node degree. Tseng et al [34] integrated the hypervisor technique with container virtualization and constructed an integrated virtualization (IV) fog platform for deploying industrial applications based on the virtual network function.…”

Section: Bandwidth Utilization Offloading and Balancingmentioning

confidence: 99%

Reinforcement Learning-Based Service-Oriented Dynamic Multipath Routing in SDN

Chiu

Liu

Chou

2022

Wireless Communications and Mobile Computing

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The increasing quality and various requirements of network services are guaranteed because of the advancement of the emerging network paradigm, software-defined networking (SDN), and benefits from the centralized and software-defined architecture. The SDN not only facilitates the configuration of the network policies for traffic engineering but also brings convenience for network state obtainment. The traffic of numerous services is transmitted within a network, whereas each service may demand different network metrics, such as low latency or low packet loss rate. Corresponding quality of service policies must be enforced to meet the requirements of different services, and the balance of link utilization is also indispensable. In this research, Reinforcement Discrete Learning-Based Service-Oriented Multipath Routing (RED-STAR) has been proposed to understand the policy of distributing an optimal path for each service. The RED-STAR takes the network state and service type as input values to dynamically select the path a service must be forwarded. Custom protocols are designed for network state obtainment, and a deep learning-based traffic classification model is also integrated to identify network services. With the differentiated reward scheme for every service type, the reinforcement learning model in RED-STAR gradually achieves high reward values in various scenarios. The experimental results show that RED-STAR can adopt the dynamic network environment, obtaining the highest average reward value of 1.8579 and the lowest average maximum bandwidth utilization of 0.3601 among all path distribution schemes in a real-case scenario.

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Section: Bandwidth Utilization Offloading and Balancingmentioning

confidence: 99%

Reinforcement Learning-Based Service-Oriented Dynamic Multipath Routing in SDN

Chiu

Liu

Chou

2022

Wireless Communications and Mobile Computing

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“…Challa et al [28] introduced a routing method based on software-defined networking (SDN), called CentFlow. Their method utilizes the degree centrality metric to detect the node or link with the highest utilization value and avoids using that node (link) to minimize cost, since the degree of utilization affects a node's cost in a network.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, [27], selected a path based on the least-congested metric, while [23] followed the same approach but with partial flow into multiple flowcells, which were then distributed to the least congested paths. Furthermore, [25], uses the C B metric as an indicator to increase the number of links and network robustness against attacks, while [28], utilizes the C B and the degree of centrality to select the optimum paths from the available shortest paths.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of a Graph-Theoretic Load Balancing Method for Data Centers

AlShammari¹,

Mohammed²

2020

IJACSA

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Modern data centers can process a massive amount of data in a short time with minimal errors. Data center networks (DCNs) use equal-cost, multi-path topologies to deliver split flows across alternative paths between the core layer and hosted servers, which could lead to significant overload if path scheduling is inefficient. Thus, distributing incoming requests among these paths is crucial for providing higher throughput and protection against link or switch failures. Several approaches have been proposed for path selection, mainly relying on round-robin and least-congested methods. In this paper, we propose a loadbalancing method based on betweenness centrality to improve the overall performance of a data center in terms of throughput, delay, and energy consumption. For evaluation, we compare our method with baseline methods of different DCN topologies: fattree, DCell, and BCube. On average, the evaluation results show that our method outperforms the others. It increases throughput by 202% and 33% while reducing delay by 20% and 22%, and energy consumption by 40% and 41% compared to the roundrobin and least-congested methods, respectively.

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“…Challa et al 30 proposed and evaluated CentFlow, which is an SDN‐based routing scheme. It uses the centrality metric to achieve smart traffic distribution and increase usage.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Moreover, Lan et al 33 and Shafiee and Ghaderi 34 selected the path based on the least‐congested metric, whereas Wang et al 35 used the same approach, but with only partial flow going into multiple flowcells, which were then distributed to the least‐loaded path. Moreover, we observed that Alenazi and Sterbenz 31 used the betweenness centrality metric as an indicator to increase the number of links and the network robustness against attacks, whereas Challa et al 30 used the same metric among the degree‐centrality metrics to select the proper paths from the available shortest paths.…”

Section: Background and Related Workmentioning

confidence: 99%

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

AlShammari

Alenazi

2020

Trans Emerging Tel Tech

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Existing data centers can handle a massive amount of data transmitted in a short time with minimum errors. Data center networks (DCNs) use cost‐effective, multipath topologies to distribute flows through alternative paths between core layers and hosting servers. Software‐defined networking (SDN) separates the control plane from the data plane to ease network management. To perform active load balancing among different paths, we address the challenges of load balancing in multipath DCNs. However, most DCNs' load balancing systems rely on a static approach that distributes flows among different paths sequentially, regardless of the path status. Consequently, some paths become congested, degrading the overall performance of the DCN in terms of throughput and delay. Increasing the number of requests worsens the congestion problem, causing some connections to become overwhelmed, thereby decreasing DCN efficiency. In this paper, we propose and evaluate the performance of a novel dynamic load‐balancing system named BL‐Hybrid, which uses the graph‐theoretical centrality of betweenness as a base metric for data forwarding. We perform a comprehensive comparative analysis of our proposed method relative to the round‐robin and least‐congested algorithms in different traffic scenarios, using the most popular SDN‐based DCNs, namely, fat tree, BCube, and DCell. Our evaluation results show that BL‐Hybrid outperforms the round‐robin and least‐congested schemes. It maximizes the average throughput by 4% and 11% and the average data transferred by 7% and 13%, respectively. Also, it reduces the average round‐trip time latency by 14% and 41%, respectively.

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CentFlow: Centrality-Based Flow Balancing and Traffic Distribution for Higher Network Utilization

Cited by 5 publications

References 42 publications

Reinforcement Learning-Based Service-Oriented Dynamic Multipath Routing in SDN

Reinforcement Learning-Based Service-Oriented Dynamic Multipath Routing in SDN

Performance Analysis of a Graph-Theoretic Load Balancing Method for Data Centers

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

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