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from logging import BASIC_FORMAT
from utils import *
from simulator import *
from networkx.algorithms.tree.branchings import Edmonds
from broadcast import BroadCastTopology
from pathlib import Path
import graphviz as gv
import networkx as nx
import subprocess
import argparse
import json
import sys
import os
def networkx_to_graphviz(g, src, dsts, label="partitions"):
"""Convert `networkx` graph `g` to `graphviz.Digraph`.
@type g: `networkx.Graph` or `networkx.DiGraph`
@rtype: `graphviz.Digraph`
"""
if g.is_directed():
h = gv.Digraph()
else:
h = gv.Graph()
for u, d in g.nodes(data=True):
# u = u.split(",")[0]
if u.split(",")[0] == src:
h.node(str(u.replace(":", " ")), fillcolor="red", style="filled")
elif u.split(",")[0] in dsts:
h.node(str(u.replace(":", " ")), fillcolor="green", style="filled")
h.node(str(u.replace(":", " ")))
for u, v, d in g.edges(data=True):
# print('edge', u, v, d)
h.edge(str(u.replace(":", " ")), str(v.replace(":", " ")), label=str(d[label]))
return h
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 MDST(src, dsts, G, num_partitions):
# Construct MDST path
h = G.copy()
h.remove_edges_from(list(h.in_edges(src)) + list(nx.selfloop_edges(h)))
DST_graph = Edmonds(h.subgraph([src] + dsts))
opt_DST = DST_graph.find_optimum(attr="cost", kind="min", preserve_attrs=True, style="arborescence")
bc_topology = BroadCastTopology(src, dsts, num_partitions)
# Construct MDST graph
MDST_graph = nx.DiGraph()
for edge in list(opt_DST.edges()):
s, d = edge[0], edge[1]
MDST_graph.add_edge(s, d, **G[s][d])
return append_src_dst_paths(src, dsts, MDST_graph, bc_topology), MDST_graph
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):
# TODO: need to download the dependencies first
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()
command = " ~/Documents/Packages/scipoptsuite-8.0.2/build/bin/applications/scipstp " # TODO: then replace this with where scipstp is installed
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 "S. Liu"\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 = networkx_to_graphviz(mdgraph, source_node, terminal_nodes)
# h.render(filename="Ndirect")
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