Utilizing SDN to Deliver Maximum TCP Flow for Data Centers

Saeed, Norah S. Bin; Alenazi, Mohammed J. F.

doi:10.1145/3388176.3388216

Cited by 5 publications

(3 citation statements)

References 25 publications

(27 reference statements)

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“…In contrast, dynamic scheduling algorithms dynamically distribute flows through optimum links by considering the current state of a network. The 'least congested' algorithm is an example of a dynamic scheduling algorithm, which forwards a new request to the least loaded or congested link [11], [20], [21], [22].…”

Section: Path Selection Methodsmentioning

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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Section: Path Selection Methodsmentioning

confidence: 99%

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

AlShammari¹,

Mohammed²

2020

IJACSA

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“…The goal of TCP congestion control has always been to allocate the resources of the network in a fair manner. Modifications to legacy TCP congestion control based on Software Defined Networks (SDNs), Artificial Intelligence (AI) and Machine Learning (ML) algorithms were proposed to achieve this goal-see for example [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Revisiting legacy high-speed TCP congestion control variants: An optimisation-theoretic analysis of multi-mode TCP

Edwan

Phillips

Lin

et al. 2022

Simulation Modelling Practice and Theory

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“…Thereafter, the controller transfers the requests with forwarding instructions back to the core switch, which will then forward the flow to the right servers attached to the edge layer. There are multiple routes between the center layer (core layer) and the edge layer 9‐11 . Therefore, the effective distribution of massive flows among DCN paths can greatly improve the DCN performance by enhancing network throughput and reducing end‐to‐end delay.…”

Section: Introductionmentioning

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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Utilizing SDN to Deliver Maximum TCP Flow for Data Centers

Cited by 5 publications

References 25 publications

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

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

Revisiting legacy high-speed TCP congestion control variants: An optimisation-theoretic analysis of multi-mode TCP

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

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