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
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[randint(0, 1)]))
query_list = node_query_list + path_query_list
naive_probe_pkt_list = naive_generate_probe_set(
query_list, fat_tree, k_fwd, k_tele)
naive_probe_cnt = 0
for probe_set in naive_probe_pkt_list:
naive_probe_cnt += len(probe_set)
optimize_probe_pkt_list = optimize_generate_probe_set(
query_list, fat_tree, k_fwd, k_tele)
optimize_probe_cnt = 0
# print optimize_probe_pkt_list
for probe_type in ['performance', 'failure']:
probe_set_dict = optimize_probe_pkt_list[probe_type]
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):
time_start = time()
all_query_cluster_list = naive_generate_probe_set(add_query_list, fat_tree, k_fwd, k_tele)
time_end = time()
query_add_time += int((time_end - time_start) * 1000000)
query_add_time /= 10
query_add_time = int(query_add_time)
print query_update_cnt, query_add_time, 0
f.write('{} {} {} \n'.format(query_update_cnt, query_add_time, 0))
f.close()
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
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]
for freq, probe_set in probe_set_dict.iteritems():
probe_cnt += len(probe_set)
print query_cnt, probe_cnt