Luopan: Sampling-Based Load Balancing in Data Center Networks

Wang, Peng; Trimponias, George; Xu, Hong; Geng, Yanhui

doi:10.1109/tpds.2018.2858815

Cited by 41 publications

(15 citation statements)

References 29 publications

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“…Author in [23] used Luopan, a congestion-aware loadbalancing method, to distribute flowcells (sub-flows after breaking an inserted flow) into a few available paths based on sampling. The idea is to take a few paths and distribute flowcells, relying on the least congested paths.…”

Section: Related Workmentioning

confidence: 99%

“…To briefly summarize the above-mentioned related works, we noticed that the procedure described in [24], selects the least congested path based on the shortest-path algorithm, while that in [26], considers the least-congested path and packet loss rate metric to select an optimum path. 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%

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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: Related Workmentioning

confidence: 99%

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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“…Wang et al 35 used a congestion‐aware load‐balancing scheme called Luopan. This scheme distributes the flowcells (subflows after breaking the inserted flow) into a small number of available paths based on sampling.…”

Section: Background and Related Workmentioning

confidence: 99%

“…To summarize the related studies discussed above, we observed that Zakia and Ben Yedder 19 selected the least‐loaded path based on the shortest path algorithm, whereas Fizi and Askar 32 also considered the least congested path and packet loss rate metric to select the proper path. 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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“…A data center load-balancer named Beamer is developed to guarantee a stateless mux operation [32]. Moreover, in [33], the authors introduced a load-balancing protocol that was based on sampling named Luopan in order to overcome the shortcomings of the existing protocols.…”

Section: Related Workmentioning

confidence: 99%

Novel Multi-Level Dynamic Traffic Load-Balancing Protocol for Data Center

et al. 2019

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Typically, the production data centers function with various risk factors, such as for instance the network dynamicity, topological asymmetry, and switch failures. Hence, the load-balancing schemes should consider the sensing accurate path circumstances as well as the reduction of failures. However, under dynamic traffic, current load-balancing schemes use the fixed parameter setting, resulting in suboptimal performances. Therefore, we propose a multi-level dynamic traffic load-balancing (MDTLB) protocol, which uses an adaptive approach of parameter setting. The simulation results show that the MDTLB outperforms the state-of-the-art schemes in terms of both the flow completion time and throughput in typical data center applications.

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Luopan: Sampling-Based Load Balancing in Data Center Networks

Cited by 41 publications

References 29 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

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

Novel Multi-Level Dynamic Traffic Load-Balancing Protocol for Data Center

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