def dereplicate_function(self, fname, num_replicas, function_locations):
if num_replicas < 2:
return
while len(function_locations[fname]) > num_replicas:
ip, tid = random.sample(function_locations[fname], 1)[0]
send_message(self.context, fname,
get_executor_unpin_address(ip, tid))
function_locations[fname].discard((ip, tid))
def replicate_function(self, fname, num_replicas, function_locations,
cpu_executors, gpu_executors):
existing_replicas = function_locations[fname]
msg = PinFunction()
msg.name = fname
msg.response_address = self.ip
# TODO: Add proper support for autoscaling GPU instances and for
# checking whether batching is enabled.
if 'gpu' in fname:
candidate_nodes = gpu_executors.difference(existing_replicas)
for key in function_locations:
if 'gpu' in key:
for location in function_locations[key]:
candidate_nodes.discard(location)
else:
candidate_nodes = cpu_executors.difference(existing_replicas)
for _ in range(num_replicas):
if len(candidate_nodes) == 0:
continue
ip, tid = random.sample(candidate_nodes, 1)[0]
send_message(self.context, msg.SerializeToString(),
get_executor_pin_address(ip, tid))
response = GenericResponse()
try:
response.ParseFromString(self.pin_accept_socket.recv())
except zmq.ZMQError:
logging.error('Pin operation to %s:%d timed out for %s.' %
(ip, tid, fname))
candidate_nodes.remove((ip, tid))
continue
if response.success:
logging.info('Pin operation to %s:%d for %s successful.' %
(ip, tid, fname))
function_locations[fname].add((ip, tid))
else:
# The pin operation was rejected, remove node and try again.
logging.error('Node %s:%d rejected pin for %s.' %
(ip, tid, fname))
candidate_nodes.remove((ip, tid))
def replicate_function(self, fname, num_replicas, function_locations,
executors):
if num_replicas < 0:
return
for _ in range(num_replicas):
existing_replicas = function_locations[fname]
candiate_nodes = executors.difference(existing_replicas)
if len(candiate_nodes) == 0:
continue
ip, tid = random.sample(candiate_nodes, 1)[0]
msg = '127.0.0.1:' + fname
send_message(self.context, msg, get_executor_pin_address(ip, tid))
function_locations[fname].add((ip, tid))
def replicate_function(self, fname, num_replicas, function_locations,
executors):
if num_replicas < 0:
return
existing_replicas = function_locations[fname]
candiate_nodes = executors.difference(existing_replicas)
for _ in range(num_replicas):
if len(candiate_nodes) == 0:
continue
ip, tid = random.sample(candiate_nodes, 1)[0]
msg = PinFunction()
msg.name = fname
msg.response_address = self.ip
send_message(self.context, msg.SerializeToString(),
get_executor_pin_address(ip, tid))
response = GenericResponse()
try:
response.ParseFromString(self.pin_accept_socket.recv())
except zmq.ZMQError:
logging.error('Pin operation to %s:%d timed out for %s.' %
(ip, tid, fname))
candiate_nodes.remove((ip, tid))
continue
if response.success:
logging.info('Pin operation to %s:%d for %s successful.' %
(ip, tid, fname))
function_locations[fname].add((ip, tid))
else:
# The pin operation was rejected, remove node and try again.
logging.error('Node %s:%d rejected pin for %s.' %
(ip, tid, fname))
candiate_nodes.remove((ip, tid))
def executor_policy(self, executor_statuses, departing_executors):
# If no executors have joined yet, we don't need to calcuate anything.
if len(executor_statuses) == 0:
return
# We institute a grace period (2 minutes by default) during which no
# elasticity decisions are made. We start the grace period when we
# decide to add or remove a VM and wait until after its over to make
# sure we don't put the system in hysteresis.
if time.time() < (self.grace_start + self.grace_period):
return
utilization_sum = 0.0
pinned_function_count = 0
for status in executor_statuses.values():
utilization_sum += status.utilization
pinned_function_count += len(status.functions)
avg_utilization = utilization_sum / len(executor_statuses)
avg_pinned_count = pinned_function_count / len(executor_statuses)
num_nodes = len(executor_statuses) / NUM_EXEC_THREADS
logging.info(
('There are currently %d executor nodes active in the' +
'system (%d threads).') % (num_nodes, len(executor_statuses)))
logging.info('Average executor utilization: %.4f' % (avg_utilization))
logging.info('Average pinned function count: %.2f' %
(avg_pinned_count))
# We check to see if the average utilization or number of pinned
# functions exceeds the policy's thresholds and add machines to the
# system in both cases.
if (avg_utilization > self.max_utilization
or avg_pinned_count > self.max_pin_count):
logging.info(
('Average utilization is %.4f. Adding %d nodes to' +
' cluster.') % (avg_utilization, self.scale_increase))
if (len(executor_statuses) / NUM_EXEC_THREADS) < 5:
self.scaler.add_vms('function', self.scale_increase)
# start the grace period after adding nodes
self.grace_start = time.time()
# We also look at any individual nodes that might be overloaded. Since
# we currently only pin one function per node, that means that function
# is very expensive, so we proactively replicate it onto two other
# threads.
for status in executor_statuses.values():
if status.utilization > .9:
logging.info(
('Node %s:%d has over 90%% utilization.' +
' Replicating its functions.') % (status.ip, status.tid))
executors = set(executor_statuses.keys())
for fname in status.functions:
self.scaler.replicate_function(fname, 2,
self.function_locations,
executors)
# We only decide to kill nodes if they are underutilized and if there
# are at least 5 executors in the system -- we never scale down past
# that.
if avg_utilization < self.min_utilization and num_nodes > 5:
ip = random.choice(list(executor_statuses.values())).ip
logging.info(('Average utilization is %.4f, and there are %d ' +
'executors. Removing IP %s.') %
(avg_utilization, len(executor_statuses), ip))
for tid in range(NUM_EXEC_THREADS):
send_message(self.scaler.context, '',
get_executor_depart_address(ip, tid))
if (ip, tid) in executor_statuses:
del executor_statuses[(ip, tid)]
departing_executors[ip] = NUM_EXEC_THREADS
def check_hash_ring(client, context):
route_ips = util.get_pod_ips(client, 'role=routing')
# If there are no routing nodes in the system currently, the system is
# still starting, so we do nothing.
if not route_ips:
return
ip = random.choice(route_ips)
# Retrieve a list of all current members of the cluster.
socket = context.socket(zmq.REQ)
socket.connect(get_routing_seed_address(ip, 0))
socket.send_string('')
resp = socket.recv()
cluster = ClusterMembership()
cluster.ParseFromString(resp)
tiers = cluster.tiers
# If there are no tiers, then we don't need to evaluate anything.
if len(tiers) == 0:
return
elif len(tiers) == 1:
# If there is one tier, it will be the memory tier.
mem_tier, ebs_tier = tiers[0], None
else:
# If there are two tiers, we need to make sure that we assign the
# correct tiers as the memory and EBS tiers, respectively.
if tiers[0].tier_id == MEMORY:
mem_tier = tiers[0]
ebs_tier = tiers[1]
else:
mem_tier = tiers[1]
ebs_tier = tiers[0]
# Queries the Kubernetes master for the list of memory nodes its aware of
# -- if any of the nodes in the hash ring aren't currently running, we add
# those the departed list.
mem_ips = util.get_pod_ips(client, 'role=memory')
departed = []
for node in mem_tier.servers:
if node.private_ip not in mem_ips:
departed.append(('0', node))
# Performs the same process for the EBS tier if it exists.
ebs_ips = []
if ebs_tier:
ebs_ips = util.get_pod_ips(client, 'role=ebs')
for node in ebs_tier.servers:
if node.private_ip not in ebs_ips:
departed.append(('1', node))
logging.debug('Found %d departed nodes.' % (len(departed)))
mon_ips = util.get_pod_ips(client, 'role=monitoring')
storage_ips = mem_ips + ebs_ips
# For each departed node the cluster is unaware of, we inform all storage
# nodes, all monitoring nodes, and all routing nodes that it has departed.
for pair in departed:
logging.info('Informing cluster that node %s/%s has departed.' %
(pair[1].public_ip, pair[1].private_ip))
msg = pair[0] + ':' + pair[1].public_ip + ':' + pair[1].private_ip
# NOTE: In this code, we are presuming there are 4 threads per
# storage/routing node. If there are more, this will be buggy; if there
# are fewer, this is fine as the messages will go into the void.
for ip in storage_ips:
for t in range(4):
send_message(context, msg, get_storage_depart_address(ip,
t))
msg = 'depart:' + msg
for ip in route_ips:
for t in range(4):
send_message(context, msg, get_routing_depart_address(ip,
t))
for ip in mon_ips:
send_message(context, msg, get_monitoring_depart_address(ip))
