File size: 7,910 Bytes
517cbd2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 | from utils import *
from simulator import *
from broadcast import BroadCastTopology
from pathlib import Path
import networkx as nx
import subprocess
import argparse
import json
import sys
import os
def N_dijkstra(src, dsts, G, num_partitions):
h = G.copy()
h.remove_edges_from(list(h.in_edges(source_node)) + list(nx.selfloop_edges(h)))
bc_topology = BroadCastTopology(src, dsts, num_partitions)
for dst in dsts:
path = nx.dijkstra_path(h, src, dst, weight="cost")
for i in range(0, len(path) - 1):
s, t = path[i], path[i + 1]
for j in range(bc_topology.num_partitions):
bc_topology.append_dst_partition_path(dst, j, [s, t, G[s][t]])
return bc_topology
def N_direct(src, dsts, G, num_partitions):
bc_topology = BroadCastTopology(src, dsts, num_partitions)
for dst in dsts:
edge = G[src][dst]
for j in range(bc_topology.num_partitions):
bc_topology.set_dst_partition_paths(dst, j, [[src, dst, edge]])
return bc_topology
def MULTI_MDST(src, dsts, G, num_partitions):
# Construct MDST path based on original graph
h = G.copy()
MDST_graphs = []
while len(list(h.edges())) > 0:
_, MDST_graph = MDST(src, dsts, h, 1)
print("MDST graph: ", MDST_graph.edges.data())
MDST_graphs.append(MDST_graph)
h.remove_edges_from(list(MDST_graph.edges()))
print("Number of MDSTs: ", len(MDST_graphs))
def Min_Steiner_Tree(src, dsts, G, num_partitions, hop_limit=3000):
source_v, dest_v = src, dsts
h = G.copy()
h.remove_edges_from(list(h.in_edges(source_v)) + list(nx.selfloop_edges(h)))
nodes, edges = list(h.nodes), list(h.edges)
num_nodes, num_edges = len(nodes), len(edges)
id_to_name = {nodes.index(n) + 1: n for n in nodes}
config_loc = "write.set"
write_loc = "test.stplog"
param_loc = "test.stp"
with open(config_loc, "w") as f:
f.write('stp/logfile = "use_probname"')
f.close()
scipstp_bin = os.environ.get("SCIPSTP_BIN", "scipstp")
command = f" {scipstp_bin}"
command += f" -f {param_loc} -s {config_loc} -l {write_loc}"
def construct_stp():
section_begin = '33D32945 STP File, STP Format Version 1.0\n\nSECTION Comment\nName "Relay: cloud regions"\nCreator "SkyDiscover"\n'
section_begin += f'Remark "Cloud region problem adapted from relay"\nEND\n\nSECTION Graph\n'
section_begin += f"Nodes {num_nodes}\nEdges {num_edges}\nHopLimit {hop_limit}\n"
Edge_info = []
cnt = 0
for edge in edges:
s, d = nodes.index(edge[0]) + 1, nodes.index(edge[1]) + 1
cost = h[edge[0]][edge[1]]["cost"]
cnt += 1
Edge_info.append(f"A {s} {d} {cost}\n")
if cnt == num_edges:
Edge_info.append("END\n")
s = nodes.index(source_v) + 1
v = [nodes.index(i) + 1 for i in dest_v]
terminal_info = [f"T {i}\n" for i in v]
terminal_info.append("END\n\nEOF")
section_terminal = f"""\nSECTION Terminals\nRoot {s}\nTerminals {len(dest_v)}\n"""
with open(param_loc, "w") as f:
f.write(section_begin)
for edge in Edge_info:
f.write(edge.lstrip())
f.write(section_terminal)
for t in terminal_info:
f.write(t)
f.close()
return
def read_result(loc):
di_stree_graph = nx.DiGraph()
with open(loc, "r") as f:
lines = f.readlines()
for line in lines:
if line.startswith("E") and len(line.split()) == 3:
l = line.split()
src_r, dst_r = id_to_name[int(l[1])], id_to_name[int(l[2])]
di_stree_graph.add_edge(src_r, dst_r, **G[src_r][dst_r])
# overlays = [node for node in di_stree_graph.nodes if node not in [source_v]+dest_v]
return di_stree_graph
construct_stp() # construct problem to a file
process = subprocess.Popen(command, shell=True) # run the steiner tree solver
process.wait()
solution_graph = read_result(loc=write_loc)
print(
f"Number of overlays added: {len(solution_graph.nodes) - (1 + len(dsts))}, {[node for node in solution_graph.nodes if node not in [src]+dsts]}"
)
bc_topology = BroadCastTopology(src, dsts, num_partitions)
os.remove(config_loc)
os.remove(write_loc)
os.remove(param_loc)
return append_src_dst_paths(src, dsts, solution_graph, bc_topology)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("jsonfile", help="input json file")
parser.add_argument("-a", "--algo", type=str, nargs="?", const="")
parser.add_argument("-n", "--num-vms", type=int, nargs="?", const="")
args = vars(parser.parse_args())
print("Args:", args)
print(f"\n==============> Baseline generation")
with open(args["jsonfile"], "r") as f:
config_name = args["jsonfile"].split("/")[1].split(".")[0]
config = json.loads(f.read())
# generate default graph with node and edge info
# G = make_nx_graph(throughput_path="profiles/aws_throughput_11_8.csv")
G = make_nx_graph(num_vms=int(args["num_vms"]))
# src, dst
source_node = config["source_node"]
terminal_nodes = config["dest_nodes"]
print(f"source_v = '{source_node}'")
print(f"dest_v = {terminal_nodes}")
# baseline path generations
if args["algo"] is None:
algorithms = [
"Ndirect",
"MDST",
# "HST",
]
else:
algorithms = [args["algo"]]
print(f"Algorithms: {algorithms}\n")
directory = f"paths/{config_name}"
if not os.path.exists(directory):
Path(directory).mkdir(parents=True, exist_ok=True)
num_partitions = config["num_partitions"]
for algo in algorithms:
outf = f"{directory}/{algo}.json"
print(f"Generate {algo} paths into {outf}")
if algo == "Ndirect":
bc_t = N_direct(source_node, terminal_nodes, G, num_partitions)
elif algo == "MDST":
bc_t, mdgraph = MDST(source_node, terminal_nodes, G, num_partitions)
elif algo == "MULTI-MDST":
bc_t = MULTI_MDST(source_node, terminal_nodes, G, num_partitions)
elif algo == "HST":
bc_t = Min_Steiner_Tree(source_node, terminal_nodes, G, num_partitions)
elif algo == "Ndijkstra":
bc_t = N_dijkstra(source_node, terminal_nodes, G, num_partitions)
else:
raise NotImplementedError(algo)
bc_t.set_num_partitions(config["num_partitions"]) # simple baseline, don't care about partitions, simply set it
with open(outf, "w") as outfile:
outfile.write(
json.dumps(
{
"algo": algo,
"source_node": bc_t.src,
"terminal_nodes": bc_t.dsts,
"num_partitions": bc_t.num_partitions,
"generated_path": bc_t.paths,
}
)
)
# put the evaluate logic here
input_dir = "paths" # input paths
output_dir = "evals" # eval results
with open(sys.argv[1], "r") as f:
config_name = sys.argv[1].split("/")[1].split(".")[0]
config = json.loads(f.read())
input_dir += f"/{config_name}"
output_dir += f"/{config_name}"
if not os.path.exists(output_dir):
Path(output_dir).mkdir(parents=True, exist_ok=True)
simulator = BCSimulator(int(args["num_vms"]), output_dir)
for algo in algorithms:
path = f"{input_dir}/{algo}.json"
simulator.evaluate_path(path, config) # path of algorithm output, basic config to evaluate
# nx.draw(mdgraph, with_labels=True)
# plt.show()
# h.render(filename="Ndirect")
|