1. 信息工程大学,河南,郑州,450002
2. 广东省新一代通信与网络创新研究院,广东,广州,510670
3. 信息工程大学,河南,郑州,450002
4. 广东省新一代通信与网络创新研究院,广东,广州,510670
网络出版:2021-03-25,
纸质出版:2021
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崔子熙, 胡宇翔, 兰巨龙, 等. 基于流分类的数据中心网络负载均衡机制[J]. 电子学报, 2021,49(3):559-565.
CUI Zi-xi, HU Yu-xiang, LAN Ju-long, et al. Load Balancing Based on Flow Classification for Datacenter Network[J]. Acta Electronica Sinica, 2021, 49(3): 559-565.
崔子熙, 胡宇翔, 兰巨龙, 等. 基于流分类的数据中心网络负载均衡机制[J]. 电子学报, 2021,49(3):559-565. DOI: 10.12263/DZXB.20200199.
CUI Zi-xi, HU Yu-xiang, LAN Ju-long, et al. Load Balancing Based on Flow Classification for Datacenter Network[J]. Acta Electronica Sinica, 2021, 49(3): 559-565. DOI: 10.12263/DZXB.20200199.
为充分利用数据中心网络的多路径带宽,现有研究多采用基于链路感知的负载均衡算法,在动态获取全局链路拥塞信息后选取最优路径对流量进行转发.然而这些研究未考虑数据中心网络流量大小分布不均匀的特性,难以在选路成本和转发效率上取得平衡.为此,设计一种基于流分类的数据中心网络负载均衡机制(ULFC,Utilization-aware Load balancing based on Flow Classification),在实现拥塞感知的基础上进行流量特征分析,采用不同的策略为大、小流分配路径,实现网络流量特征与选路方法优势的最佳匹配.实验结果表明,相比于现有方案,ULFC的平均流处理效率提高了1.3倍至1.6倍,路由成本降低了50%以上.
In order to fully utilize the bandwidth of multi-paths of the datacenter network (DCN)
existing studies mostly adopt the congestion-aware load-balancing scheme
which forwards traffic along the optimal path after dynamically obtaining global congestion information. However
these works do not consider the non-uniform distribution of flow size and are difficult to strike a balance between the routing cost and the forwarding efficiency. This paper proposes ULFC
a utilization-aware load-balancing mechanism based on flow classification. By analyzing the characteristics of traffic
ULFC classifies the flows based on their sizes and assigns paths to them using different strategies
realizing the best matching between the characteristics of traffic and the advantages of the routing method. We evaluate ULFC with simulation and the results show that it outperforms the existing schemes in average flow-completion time (1.3~1.6×)
while the routing cost has been reduced by more than 50%.
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