comments added!

Author: PooriaNamyar

metric/tub.py

@@ -6,6 +6,16 @@
 
 
 def get_throughput_upper_bound(topo, tor_list, demand_dict):
+    """ Computes the throughput upper bound (tub)
+
+    Args:
+        topo: a networkx graph representing switch connections
+        tor_list: contains list of switches with directly connected servers
+        demand_dict: a mapping from each ToR to its number of directly connected servers
+
+    Returns:
+        tub: throughput upper bound
+    """
     print("** Computing maximal permutation traffic matrix...")
     _, sum_weight_matching = near_wc_tm.get_longest_matching_traffic_matrix(topo, tor_list, demand_dict)
     print("** Computing TUB...")

topo_repo/topology.py

@@ -41,9 +41,9 @@ def get_avg_path_between_src_dst(self, tor_list=None):
         return shortest_path.all_pair_shortest_path_length_adjacency_matrix(self.get_topology(), tor_list)
 
     def get_near_worst_case_traffic_matrix(self):
-        return near_wc_tm.get_longest_matching_traffic_matrix_igraph_implementation(self.get_topology(),
-                                                                                    self.get_tor_list(),
-                                                                                    self.get_demand_dict())
+        return near_wc_tm.get_longest_matching_traffic_matrix(self.get_topology(),
+                                                              self.get_tor_list(),
+                                                              self.get_demand_dict())
 
     def get_tub(self):
         return tub.get_throughput_upper_bound(self.get_topology(), self.get_tor_list(), self.get_demand_dict())

utils/near_wc_tm.py

@@ -13,19 +13,20 @@
 def get_longest_matching_traffic_matrix(topology, tor_list, demand_dict):
     """ Generates maximal permutation traffic matrix that results in near worst-case throughput
 
-            This is a generalization to: Measuring and Understanding Throughput of Network Topologies
-            (Sangeetha Abdu Jyothi, Ankit Singla, P. Brighten Godfrey, Alexandra Kolla), 2016
+    This is a generalization to: Measuring and Understanding Throughput of Network Topologies
+    (Sangeetha Abdu Jyothi, Ankit Singla, P. Brighten Godfrey, Alexandra Kolla), 2016
 
-            Args:
-                topology: a networkx graph representing switch connections
-                tor_list: contains list of switches with directly connected servers
-                demand_dict: a mapping from each ToR to its number of directly connected servers
-            Returns:
-                traffic_matrix: a dictionary (src, dst) --> amount of traffic
-                sum_weight_matching: sum shortest_path_length(src, dst) * traffic_matrix(src, dst) over all pairs
-                                     traffic_matrix
+    Args:
+        topology: a networkx graph representing switch connections
+        tor_list: contains list of switches with directly connected servers
+        demand_dict: a mapping from each ToR to its number of directly connected servers
+    Returns:
+        traffic_matrix: a dictionary (src, dst) --> amount of traffic
+        sum_weight_matching: sum shortest_path_length(src, dst) * traffic_matrix(src, dst) over all pairs
+                             traffic_matrix
     """
 
+    # Line 30-36 makes sure the order of ToRs in the tor_list is the same as their order in the topology.nodes().
     initial_tor_list = list(tor_list)
     num_nodes = len(tor_list)
     tor_list = list()
@@ -49,7 +50,6 @@ def get_longest_matching_traffic_matrix(topology, tor_list, demand_dict):
     del coefficient
     del minimum
     gc.collect()
-    # bi_graph.es["weights"] = weights
 
     print("**** Computing maximum weighted matching...")
     maximal_matching = bi_graph.maximum_bipartite_matching(weights=weights, eps=0.001)

utils/shortest_path.py

@@ -24,7 +24,6 @@ def sum_all_pair_shortest_path_length_adjacency_matrix(g):
     C = np.zeros(np.shape(A), order='F', dtype=np.float32)
     for k in range(num_node - 1):
         B = scipy.linalg.blas.sgemm(1, B, A)
-        # B = np.matmul(B, A)
         C = np.add(C, B)
         num = np.count_nonzero(C == 0)
         if num == 0:

utils/utilities.py

@@ -8,7 +8,7 @@
 
 
 def get_model(filename):
-    print("** Loading Topology...")
+    print("** Loading Model...")
     with open(filename, "rb") as file:
         if sys.version_info >= (3, 0):
             model = pickle.load(file, encoding="latin1")
@@ -23,6 +23,7 @@ def delete_file(file_path):
 
 
 def store_model(model, file_path):
+    print("** Storing Model...")
     delete_file(file_path)
     with open(file_path, "wb") as f:
         pickle.dump(model, f)