A simple congestion-aware algorithm for load balancing in datacenter networks
A simple congestion-aware algorithm for load balancing in datacenter networks
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DOI：
10.1109/tnet.2017.2751251
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发表时间：
2016-04
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影响因子：
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通讯作者：
Mehrnoosh Shafiee;Javad Ghaderi
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文献类型：
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作者：
Mehrnoosh Shafiee;Javad Ghaderi
我们研究数据中心网络中的负载平衡问题，即在可用路径之间分配端到端的数据流，以有效地平衡网络中的负载。今天使用的解决方案通常依赖于ECMP（等价多路径）机制，该机制本质上试图通过将流散列到可用的最短路径来平衡网络中的负载。然而，众所周知，当网络拓扑或流大小存在不对称性时，ECMP表现不佳，因此最近人们对解决这些缺点的替代机制产生了很大兴趣。在本文中，我们考虑了一个一般的网络拓扑结构，其中每个链路的成本是一个凸函数的链路利用率。各种源-目的地对之间的流随时间动态生成，每个流具有大小（带宽要求）和持续时间。一旦流被分配到网络中的路径，它就在其持续时间内消耗来自沿其路径沿着的所有链路的等于其大小的带宽。我们提出了一个低复杂度的调度感知算法，分配流的可用路径在一个在线的方式，而不分裂，并证明它渐近最小化的总网络成本。大量的仿真结果验证了我们的算法在各种流量条件下，在不同的数据中心架构的性能。
We study the problem of load balancing in datacenter networks, namely, assigning the end-to-end data flows among the available paths in order to efficiently balance the load in the network. The solutions used today rely typically on ECMP (Equal Cost Multi Path) mechanism which essentially attempts to balance the load in the network by hashing the flows to the available shortest paths. However, it is well known that ECMP performs poorly when there is asymmetry either in the network topology or the flow sizes, and thus there has been much interest recently in alternative mechanisms to address these shortcomings. In this paper, we consider a general network topology where each link has a cost which is a convex function of the link utilization. Flows among the various source-destination pairs are generated dynamically over time, each with a size (bandwidth requirement) and a duration. Once a flow is assigned to a path in the network, it consumes bandwidth equal to its size from all the links along its path for its duration. We propose a low-complexity congestion-aware algorithm that assigns the flows to the available paths in an online fashion and without splitting, and prove that it asymptotically minimizes the total network cost. Extensive simulation results are presented to verify the performance of our algorithm under a wide range of traffic conditions and under different datacenter architectures.