def runstuff():
NUM_SWITCHES = 20 # total number of switches in the complete graph of switch-port connectivity
NUM_CLIENTS = 2 # number of clients on every switch. large numbers will decrease the size of sccs, because
# forwarding to clients is always safe; not part of a loop.
RULES_PER_ROUND = 350 # number of rule installs to do between gathering time statistics.
MAX_TOTAL_RULES_INSTALLED = 450 # number of total rules to maintain in the network model, so
# that as more than these come in, we evict the oldest ones.
TOTAL_RULE_INSTALLATIONS_BEFORE_HALT = 350 # halt after this many total rule installations.
SHOW_TRACE = False # should we show the trace when loops are detected? This can get hectic.
MIN_IN = 1 # minininum number of in_ports to be used in random rules.
MAX_IN = 3 # etc.
MIN_OUT = 1
MAX_OUT = 2
# the network that we'kll use to generate random valid rules
net = TestNetwork1(NUM_SWITCHES, NUM_CLIENTS, minswitch=5555, minport=100001)
# our model
n = NetworkFlowruleModel()
# track the flow install rate in this
timestats = StatsBuddy()
total_installed = 0
# track the rules in place in our NetworkFlowruleModel so we can evict them
q = Queue.Queue()
while True:
#start a timer
t1 = time.time()
# do some rule installations
for r in xrange(RULES_PER_ROUND):
if q.qsize() > MAX_TOTAL_RULES_INSTALLED:
droprule = q.get() # get the number of the rule that's over the limit
n.drop_flow_rule(droprule) # kill the rule
rule = net.get_random_rule(MIN_IN, MAX_IN, MIN_OUT, MAX_OUT)
(rnum, trace) = n.install_flow_rule(rule)
if SHOW_TRACE and trace: NetworkFlowruleModel.explain(trace)
q.put(rnum)
total_installed += 1
t2 = time.time()
# NOTE: linux might report this in ms instead of seconds, I think,
# so this might give weird results
timestats.add(RULES_PER_ROUND/(t2-t1))
# how we eventually halt
if total_installed >= TOTAL_RULE_INSTALLATIONS_BEFORE_HALT: break
print timestats, 'are stats for the number of rule installs and evictions per second.'
print n.sccsizestats, 'are stats for the average scc size of installed rules.'
return
def RunTest(num_switches, num_clients, steady_state_avg_rules_per_switch,
total_rule_installations_before_halt,
min_in, max_in, min_out, max_out,
min_match_bits=0, max_match_bits=0, min_rewrite_bits=0, max_rewrite_bits=0,
show_trace = False,
port_density = 1.0,
rewrite_rule_probability = 1.0):
"""
num_switches:
total number of switches in the complete graph of switch-port connectivity
num_clients:
number of clients on every switch. large numbers will decrease the size of sccs,
because forwarding to clients is always safe; not part of a loop.
steady_state_avg_rules_per_switch:
The average number of rules to keep in the network per switch before rules are evicted
The network will gradually be built up to have steady_state_avg_rules_per_switch * num_switches
and then exist with that steady state number of rules until the total number of rule
installations occurs
total_rule_installations_before_halt:
Halt after this many total rule installations. (Will be rounded to a multiple of 20,
to make progress reporting cleaner)
min_in:
Minininum number of in_ports to be used in random rules
max_in: etc.
min_out: etc.
max_out: etc.
min_match_bits:
The minimum number of bits to fix as non-'x' in random rewrite rule matchs
max_match_bits:
Max of above
min_rewrite_bits:
The minimum number of bits to explicitly fix randomly as one of 0/1 in output packets
max_rewrite_bits:
Max of above
show_trace:
Should we show the trace when loops are detected? This can get hectic and start spinning the terminal.
port_density:
Default of 1.0, indicating the complete graph as the underlying network topology
For less than that, the network is a ring with _this_ proportion of the pairs of
switches connected by a pair of 1-way ports.
rewrite_rule_probability:
The proportion of the random rules that should be random rewrite rules rather than random forwarding rules
"""
# some basic info for our run
steady_state_rules_in_network = num_switches * steady_state_avg_rules_per_switch
rules_per_round = total_rule_installations_before_halt / 20
total_rule_installations_before_halt = rules_per_round * 20
# eject if params are silly
if total_rule_installations_before_halt <= steady_state_rules_in_network:
raise Exception('The given parameters will not permit the network to reach a steady state, so no statistics would be collected. Try decreasing "steady_state_avg_rules_per_switch".')
# the network that we'll use to generate random valid rules
net = TestNetwork(num_switches, num_clients, port_density)
# our model
n = NetworkFlowruleModel()
# annotate the parameters
print 'Parameters for this run are:', (num_switches, num_clients, steady_state_avg_rules_per_switch,
total_rule_installations_before_halt,
min_in, max_in, min_out, max_out,
min_match_bits, max_match_bits, min_rewrite_bits, max_rewrite_bits,
show_trace, port_density, rewrite_rule_probability)
print 'Steady state will have {} rules installed.'.format(steady_state_rules_in_network)
#start a timer
test_start_time = time.time()
steady_state_start_time = None # we'll set this when we reach maximum flow capacity for the network
total_installed = 0
# track the rules in place in our NetworkFlowruleModel so we can evict them
q = Queue.Queue()
while total_installed < total_rule_installations_before_halt:
# do some rule installations
for r in xrange(rules_per_round):
# drop rules so we don't exceed steady_state_rules_in_network
if q.qsize() > steady_state_rules_in_network:
droprule = q.get() # get the number of the rule that's over the limit
n.drop_flow_rule(droprule) # kill the rule
if steady_state_start_time is None:
n.collect_stats(True)
steady_state_start_time = time.time()
######################################
if rand.random() <= rewrite_rule_probability:
rule = net.get_random_rewrite_rule(min_in, max_in, min_out, max_out, min_match_bits, max_match_bits, min_rewrite_bits, max_rewrite_bits)
else:
rule = net.get_random_rule(min_in, max_in, min_out, max_out)
######################################
# put the random flow into our model
(rnum, trace) = n.install_flow_rule(rule)
# show detected loop details if desired
if show_trace and trace: NetworkFlowruleModel.explain(trace)
q.put(rnum)
total_installed += 1
amount_done = 1.0 * total_installed / total_rule_installations_before_halt
if steady_state_start_time is not None:
time_in_steady_state = time.time() - steady_state_start_time
amount_of_steady_state_done = 1.0 * (total_installed - steady_state_rules_in_network) / (total_rule_installations_before_halt - steady_state_rules_in_network)
expected_steady_state_time_total = time_in_steady_state / amount_of_steady_state_done
expected_remaining_time = expected_steady_state_time_total * (1.0 - amount_of_steady_state_done)
print '{:.0%} done. Expected {:.4g}s remaining'.format(amount_done, expected_remaining_time)
else:
print '{:.0%} done. Steady state not yet entered...'.format(amount_done)
test_end_time = time.time()
total_test_time = test_end_time - test_start_time
steady_state_total_time = test_end_time - steady_state_start_time
# show heaps of info about the run
print
print 'Overall, {:.5g} rules were installed per second on average.'.format(1.0 * total_rule_installations_before_halt / total_test_time)
print 'While in steady state, {:.5g} rules were installed per second on average.'.format(1.0 * (total_rule_installations_before_halt - steady_state_rules_in_network) / steady_state_total_time)
print n.scc_size_stats, 'are the stats for the average SCC size of installed rules.'
print n.edges_stats, 'are the stats for the average number of graph edges added per installed rule.'
print 'Loop detection was called for {:.4%} of rule installs'.format(1.0 * n.loop_detection_calls / total_rule_installations_before_halt)
print 'Loops were detected for {:.4%} of rule installs'.format(1.0 * n.loops_detected / total_rule_installations_before_halt)
print 'Loops were present for {:.4%} of calls to loop detection'.format(1.0 * n.loops_detected / n.loop_detection_calls)
print 'SCC sizes are distributed like this:', n.scc_buckets.graph()
def get_network_model(self):
n = NetworkFlowruleModel()
for rule in self.rules():
(rnum, trace) = n.install_flow_rule(rule)
if trace: print NetworkFlowruleModel.explain(trace)
return n
