def get_query_list(k, port_range):
node_query_list = []
path_query_list = []
for index in xrange(50):
node_query_where = {}
node_query_return = {}
for _ in xrange(index):
node_query_where[metadata_list[randint(0, index) %
metadata_len]] = {}
node_query_return[metadata_list[randint(0, index) %
metadata_len]] = {}
node_query_list.append(
NodeQuery(randint(0, port_range), randint(0, k), node_query_where,
randint(1, index + 1), node_query_return,
category[randint(0, 1)]))
path_query_where = {}
path_query_return = {}
for _ in xrange(index):
path_query_where[metadata_list[randint(0, index) %
metadata_len]] = {}
path_query_return[metadata_list[randint(0, index) %
metadata_len]] = {}
path_query_list.append(
PathQuery(randint(0, port_range), randint(0, k),
randint(0, port_range), randint(0, k), path_query_where,
randint(1, index + 1), path_query_return,
category[randint(0, 1)]))
query_list = node_query_list + path_query_list
return query_list
def get_query_list_dict(port_range, router_range):
query_list_dict = {100: [], 500: [], 1000: []}
for index in xrange(250):
node_query_where = {}
node_query_return = {}
for _ in xrange(index):
node_query_where[metadata_list[randint(0, index) %
metadata_len]] = {}
node_query_return[metadata_list[randint(0, index) %
metadata_len]] = {}
node = randint(0, router_range)
port = randint(0, port_range)
cat = category[randint(0, 1)]
query_list_dict[100].append(
NodeQuery(node, port, node_query_where, 100, node_query_return,
cat))
query_list_dict[500].append(
NodeQuery(node, port, node_query_where, 500, node_query_return,
cat))
query_list_dict[1000].append(
NodeQuery(node, port, node_query_where, 1000, node_query_return,
cat))
path_query_where = {}
path_query_return = {}
for _ in xrange(index):
path_query_where[metadata_list[randint(0, index) %
metadata_len]] = {}
path_query_return[metadata_list[randint(0, index) %
metadata_len]] = {}
src_node = randint(0, router_range)
src_port = randint(0, port_range)
dst_node = randint(0, router_range)
dst_port = randint(0, port_range)
cat = category[randint(0, 1)]
query_list_dict[100].append(
PathQuery(src_node, src_port, dst_node, dst_port, path_query_where,
100, path_query_return, cat))
query_list_dict[500].append(
PathQuery(src_node, src_port, dst_node, dst_port, path_query_where,
500, path_query_return, cat))
query_list_dict[1000].append(
PathQuery(src_node, src_port, dst_node, dst_port, path_query_where,
1000, path_query_return, cat))
return query_list_dict
node_query_list = []
path_query_list = []
for query_cnt in xrange(10, 1010, 10):
node_query_cnt = query_cnt / 2
for index in xrange(node_query_cnt - 5, node_query_cnt):
node_query_where = {}
node_query_return = {}
for _ in xrange(index):
node_query_where[metadata_list[randint(0, index) %
metadata_len]] = {}
node_query_return[metadata_list[randint(0, index) %
metadata_len]] = {}
node_query_list.append(
NodeQuery(index, randint(0, k), node_query_where,
randint(1, node_query_cnt), node_query_return,
category[randint(0, 1)]))
path_query_cnt = query_cnt / 2
for index in xrange(path_query_cnt - 5, path_query_cnt):
path_query_where = {}
path_query_return = {}
for _ in xrange(index):
path_query_where[metadata_list[randint(0, index) %
metadata_len]] = {}
path_query_return[metadata_list[randint(0, index) %
metadata_len]] = {}
path_query_list.append(
PathQuery(index, randint(0, k), index + 1,
randint(0, k), path_query_where,
randint(1, path_query_cnt), path_query_return,
category = ['performance', 'failure']
f = open('testsdata/fig6a_query_update_time_least.txt', 'w')
node_query_update_list = []
path_query_update_list = []
for query_update_cnt in xrange(10, 1010, 10):
query_add_time = 0.0
node_query_update_cnt = query_update_cnt / 2
for index in xrange(node_query_update_cnt-5, node_query_update_cnt):
node_query_where = {}
node_query_return = {}
for _ in xrange(index):
node_query_where[metadata_list[randint(0, index) % metadata_len]] = {}
node_query_return[metadata_list[randint(0, index) % metadata_len]] = {}
node_query_update_list.append(NodeQuery(index, randint(0, k), node_query_where,
randint(1, node_query_update_cnt),
node_query_return, category[randint(0, 1)]))
path_query_update_cnt = query_update_cnt / 2
for index in xrange(path_query_update_cnt-5, path_query_update_cnt):
path_query_where = {}
path_query_return = {}
for _ in xrange(index):
path_query_where[metadata_list[randint(0, index) % metadata_len]] = {}
path_query_return[metadata_list[randint(0, index) % metadata_len]] = {}
path_query_update_list.append(PathQuery(index, randint(0, k), index + 1, randint(0, k),
path_query_where, randint(1, path_query_update_cnt),
path_query_return, category[randint(0, 1)]))
add_query_list = node_query_update_list + path_query_update_list
for _ in xrange(10):
def get_query_list_dict(k, port_range):
query_list_dict = {100: [], 500: [], 1000: []}
node_query_list = []
path_query_list = []
for index in xrange(50):
node_query_where = {}
node_query_return = {}
for _ in xrange(index):
node_query_where[metadata_list[randint(0, index) %
metadata_len]] = {}
node_query_return[metadata_list[randint(0, index) %
metadata_len]] = {}
node_query_list.append(
NodeQuery(randint(0, port_range), randint(0, k), node_query_where,
randint(1, index + 1), node_query_return,
category[randint(0, 1)]))
path_query_where = {}
path_query_return = {}
for _ in xrange(index):
path_query_where[metadata_list[randint(0, index) %
metadata_len]] = {}
path_query_return[metadata_list[randint(0, index) %
metadata_len]] = {}
path_query_list.append(
PathQuery(randint(0, port_range), randint(0, k),
randint(0, port_range), randint(0, k), path_query_where,
randint(1, index + 1), path_query_return,
category[randint(0, 1)]))
query_list_dict[100] = node_query_list + path_query_list
query_list_dict[500] = deepcopy(query_list_dict[100])
for index in xrange(100, 300):
node_query_where = {}
node_query_return = {}
for _ in xrange(index):
node_query_where[metadata_list[randint(0, index) %
metadata_len]] = {}
node_query_return[metadata_list[randint(0, index) %
metadata_len]] = {}
query_list_dict[500].append(
NodeQuery(randint(0, port_range), randint(0, k), node_query_where,
randint(1, index), node_query_return,
category[randint(0, 1)]))
path_query_where = {}
path_query_return = {}
for _ in xrange(index):
path_query_where[metadata_list[randint(0, index) %
metadata_len]] = {}
path_query_return[metadata_list[randint(0, index) %
metadata_len]] = {}
query_list_dict[500].append(
PathQuery(randint(0, port_range), randint(0, k),
randint(0, port_range), randint(0, k), path_query_where,
randint(1, index), path_query_return,
category[randint(0, 1)]))
query_list_dict[1000] = deepcopy(query_list_dict[500])
for index in xrange(300, 550):
node_query_where = {}
node_query_return = {}
for _ in xrange(index):
node_query_where[metadata_list[randint(0, index) %
metadata_len]] = {}
node_query_return[metadata_list[randint(0, index) %
metadata_len]] = {}
query_list_dict[1000].append(
NodeQuery(randint(0, port_range), randint(0, k), node_query_where,
randint(1, index), node_query_return,
category[randint(0, 1)]))
path_query_where = {}
path_query_return = {}
for _ in xrange(index):
path_query_where[metadata_list[randint(0, index) %
metadata_len]] = {}
path_query_return[metadata_list[randint(0, index) %
metadata_len]] = {}
query_list_dict[1000].append(
PathQuery(randint(0, port_range), randint(0, k),
randint(0, port_range), randint(0, k), path_query_where,
randint(1, index), path_query_return,
category[randint(0, 1)]))
return query_list_dict
# -*- coding: utf-8 -*-
from topology.fat_tree import FatTreeTopology
from optimize_algorithm.generate_probe import generate_probe_set
from query_primitives.query import PathQuery, NodeQuery
from utils.const import metadata_list
from random import randint
if __name__ == '__main__':
fat_tree = FatTreeTopology()
k_fwd = 100
k_tele = 20
metadata_len = len(metadata_list)
for query_cnt in xrange(10, 210, 10):
node_query_list = [
NodeQuery(index, 2,
{metadata_list[randint(0, 1000) % metadata_len]: {}}, 1,
{metadata_list[randint(0, 1000) % metadata_len]: {}},
'failure') for index in xrange(query_cnt / 2)
]
path_query_list = [
PathQuery(index, 1, index + 10, 1,
{metadata_list[randint(0, 2000) % metadata_len]: {}},
0.5,
{metadata_list[randint(0, 2000) % metadata_len]: {}},
'failure') for index in xrange(query_cnt / 2)
]
query_list = node_query_list + path_query_list
probe_pkt_list = generate_probe_set(query_list, fat_tree, k_fwd,
k_tele)
probe_cnt = 0
for category in ['performance', 'failure']:
probe_set_dict = probe_pkt_list[category]