Dynamic load balancing in SDN-based data center networks

Zakia, Umme; Yedder, Hanene Ben

doi:10.1109/iemcon.2017.8117206

Cited by 40 publications

(11 citation statements)

References 8 publications

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“…In communication networks, load balancing Load balancing represents a major traffic engineering (TE) method whose purpose is to improve the delivery of data traffic loads over various resources driven by a particular performance measure [43] [47]. With regard to hybrid SDN/OSPF, the TE goal of attaining maximum link utilization (MLU) can be made achievable using data traffic flow load balancing to ensure a congestion free network.…”

Section: A Load Balancing Studiesmentioning

confidence: 99%

AI-Assisted Framework for Green-Routing and Load Balancing in Hybrid Software-Defined Networking: Proposal, Challenges and Future Perspective

et al. 2020

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The explosive growth of IP networks, the advent of cloud computing, and the rapid progress in wireless communications witnessed today reflect significant progress towards meeting the explosive data traffic demands. Consequently, communications service providers should deploy efficient and intelligent network solutions to accommodate the huge traffic demands and to ease the capacity pressure on their network infrastructure. Besides, vendors should develop novel energy-efficient networks to reduce network utility costs and carbon footprint. Software-defined networking (SDN) provides a suitable solution, however, complete SDN deployment is currently unachievable in the short-term. An alternative is the hybrid SDN/ open shortest path forwarding (OSPF) network, which allows the deployment of SDN in legacy networks. Nevertheless, hybrid SDN/OSPF also faces several technical, economic and organizational challenges. Although many energyefficiency routing solutions exist in hybrid SDN/OSPF networks, they are generic and reactive by design. Moreover, these solutions are characterized by manual control plane forwarding configurations, leading to sub-optimal performance. The recent promising combination of SDN and artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) in traffic management and control provides tremendous opportunities. In this paper, we first provide a review of the most recent optimization approaches for global energy-efficient routing and load balancing. Next, we investigate a scalable and intelligent integrated architectural framework that leverages deep reinforcement learning (DRL) techniques to realize predictive and rate adaptive energy-efficient routing with guaranteed quality of service (QoS), in transitional hybrid SDN/OSPF networks. Based on the need to minimize global network energy consumption and improve link performance, this paper provides key research insights into the current progress in hybrid SDN/OSPF, ML and AI in the hope of stimulating more research.

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Section: A Load Balancing Studiesmentioning

confidence: 99%

AI-Assisted Framework for Green-Routing and Load Balancing in Hybrid Software-Defined Networking: Proposal, Challenges and Future Perspective

et al. 2020

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“…Zakia et al [24] proposed a dynamic SDN-based path selection method to improve DCN load balancing. They used the Dijkstra algorithm for calculating all the shortest paths between two nodes.…”

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.…”

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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“…Zakia and Bedder 19 proposed an SDN‐based dynamic path optimization for load‐balanced DCNs. Their proposed algorithm works as follows: first, it calculates all the shortest paths from one node to the others using the Dijkstra algorithm.…”

Section: Background and Related Workmentioning

confidence: 99%

“…This paper proposes an efficient load‐balancing method by scheduling and managing the flow between the DCN paths. This approach will ensure that the network is protected against link congestion, and it will be the most appropriate solution to the aforementioned issues 19,20 …”

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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Dynamic load balancing in SDN-based data center networks

Cited by 40 publications

References 8 publications

AI-Assisted Framework for Green-Routing and Load Balancing in Hybrid Software-Defined Networking: Proposal, Challenges and Future Perspective

AI-Assisted Framework for Green-Routing and Load Balancing in Hybrid Software-Defined Networking: Proposal, Challenges and Future Perspective

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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