本文采用Python壮观的白昼，借助networkx, matplotlib实现三种典型数据中心网络拓扑的绘制与可视化。三种数据中心网络拓扑为Fat-tree, BCube, Vl2。
拓扑生成
import matplotlib.pyplot as pltimport networkx as nx Fat-tree def fat_tree_topo(n=4): """Standard fat tree topology n: number of pods total n^3/4 servers """ topo = nx.Graph() num_of_servers_per_edge_switch = n // 2 num_of_edge_switches = n // 2 num_of_aggregation_switches = num_of_edge_switches num_of_core_switches = int((n / 2) * (n / 2)) # generate topo pod by pod for i in range(n): for j in range(num_of_edge_switches): topo.add_node("Pod {} edge switch {}".format(i, j)) topo.add_node("Pod {} aggregation switch {}".format(i, j)) for k in range(num_of_servers_per_edge_switch): topo.add_node("Pod {} edge switch {} server {}".format( i, j, k)) topo.add_edge( "Pod {} edge switch {}".format(i, j), "Pod {} edge switch {} server {}".format(i, j, k)) # add edge among edge and aggregation switch within pod for i in range(n): for j in range(num_of_aggregation_switches): for k in range(num_of_edge_switches): topo.add_edge("Pod {} aggregation switch {}".format(i, j), "Pod {} edge switch {}".format(i, k)) # add edge among core and aggregation switch num_of_core_switches_connected_to_same_aggregation_switch = num_of_core_switches // num_of_aggregation_switches for i in range(num_of_core_switches): topo.add_node("Core switch {}".format(i)) aggregation_switch_index_in_pod = i // num_of_core_switches_connected_to_same_aggregation_switch for j in range(n): topo.add_edge( "Core switch {}".format(i), "Pod {} aggregation switch {}".format( j, aggregation_switch_index_in_pod)) topo.name = 'fattree' return topo BCube def bcube_topo(k=0, n=4): """Standard Bcube topology k: layers n: num of servers total n ^ (k+1) servers """ topo = nx.Graph() num_of_servers = n**(k + 1) # add server first for i in range(num_of_servers): topo.add_node("Server {}".format(i)) # add switch by layer num_of_switches = int(num_of_servers / n) for i in range(k + 1): index_interval = n**i num_of_one_group_switches = n**i for j in range(num_of_switches): topo.add_node("Layer {} Switch {}".format(i, j)) start_index_server = j % num_of_one_group_switches + ( j // num_of_one_group_switches) * num_of_one_group_switches * n for k in range(n): server_index = start_index_server + k * index_interval topo.add_edge("Server {}".format(server_index), "Layer {} Switch {}".format(i, j)) topo.name = 'Bcube' return topo Vl2 def vl2_topo(port_num_of_aggregation_switch=4, port_num_of_tor_for_server=2): """Standard vl2 topology total port_num_of_aggregation_switch^2 / 4 * port_num_of_tor_for_server servers """ topo = nx.Graph() num_of_aggregation_switches = port_num_of_aggregation_switch num_of_intermediate_switches = num_of_aggregation_switches // 2 num_of_tor_switches = (port_num_of_aggregation_switch // 2) * (port_num_of_aggregation_switch // 2) # create intermediate switch for i in range(num_of_intermediate_switches): topo.add_node("Intermediate switch {}".format(i)) # create aggregation switch for i in range(num_of_aggregation_switches): topo.add_node("Aggregation switch {}".format(i)) for j in range(num_of_intermediate_switches): topo.add_edge("Aggregation switch {}".format(i), "Intermediate switch {}".format(j)) # create ToR switch num_of_tor_switches_per_aggregation_switch_can_connect = num_of_aggregation_switches // 2 for i in range(num_of_tor_switches): topo.add_node("ToR switch {}".format(i)) # every ToR only need to connect 2 aggregation switch aggregation_index = ( i // num_of_tor_switches_per_aggregation_switch_can_connect) * 2 topo.add_edge("ToR switch {}".format(i), "Aggregation switch {}".format(aggregation_index)) aggregation_index += 1 # The second aggregation switch topo.add_edge("ToR switch {}".format(i), "Aggregation switch {}".format(aggregation_index)) # add server to ToR for j in range(port_num_of_tor_for_server): topo.add_node("ToR switch {} server {}".format(i, j)) topo.add_edge("ToR switch {} server {}".format(i, j), "ToR switch {}".format(i)) topo.name = 'VL2' return topo 说明：在以上代码中用了较多很长的变量名，这是为了让读者更容易理解代码的含义。在实际使用中，可适当精简变量的命名。 代码测试 Fat-tree topo = fat_tree_topo()nx.draw(topo, with_labels=True)plt.show() BCube topo = bcube_topo()nx.draw(topo, with_labels=True)plt.show() Vl2 topo = vl2_topo()nx.draw(topo, with_labels=True)plt.show() 参考资料 在本文中没有详细介绍三种网络拓扑的具体结构等内容，详细的介绍可以在以下文章中找到：
A Survey of Data Center Network Architectures（链接：https://pdfs.semanticscholar.org/676b/f0c711107389f0452553ed0c3c59921db4e5.pdf）.