def __init__(self,
size=100,
load=1.0,
regions=(4,4),
coords=(100,100),
run_times=6,
arrival_dist = dists.expon,
resource_sizes = dists.gamma(100, 10),
job_sizes = dists.gamma(20, 4),
job_durations = dists.gamma(100, 10),
service_means = dists.normal(0.1),
service_dist = dists.shaped_gamma(0.1),
latency_means = dists.normal(0.1),
latency_dist = dists.shaped_gamma(0.1),
regional_latency = dists.gamma(0.2, 0.1),
global_latency = dists.gamma(0.4, 0.1)
):
# calculated results
self.runtime = run_times * job_durations.mean
total_capacity = size * resource_sizes.mean;
max_jobs = total_capacity / float(job_sizes.mean);
arrival_mean = job_durations.mean / float(max_jobs);
self.inter_arrival_time = arrival_dist(arrival_mean / load)
self.job_sizes = job_sizes
self.job_durations = job_durations
# store for stats
self.size = size
self.load = load
# generate network
self.graph = network.Network(
coords,
regions,
latency_means,
regional_latency,
global_latency,
latency_dist)
# generate nodes, but no topology
network.generate_nodes(self.graph, size)
self.service_dist = service_dist
self.service_means = service_means
for node in self.graph.nodes_iter():
node.server = Server(node, service_dist(service_means()))
node.resource = GridResource(node, int(resource_sizes()))
self.mons = OrderedDict()
self.mons["grid_util"] = Monitor("grid_util")
self.mons["server_util"] = Monitor("server_util")
self.mons["server_queue"] = Monitor("server_queue")
def __init__(self,
registry_type=Registry,
broker_means=dists.normal(0.05),
**kw):
super(MdsModel, self).__init__(**kw)
regions = self.graph.regions
self.regions = [i for i in range(regions[0]*regions[1])]
self.brokers = dict()
for r in self.regions:
node = self.random_region_node(r)
node.server.service_time = self.service_dist(broker_means())
broker = Broker(
r,
node,
registry_type(),
60,
self.brokers)
# give them an intitial picture of the grid
for node in self.graph.nodes_in_region(r):
state = ResourceState(node.resource_agent, node.resource.free)
broker.registry.update_state(state)
self.brokers[r] = broker
for node in self.graph.nodes_iter():
node.broker = self.brokers[node.region]
# this nodes resource agent
node.resource_agent = MdsResourceAgent(node, 30)
# mapping of jobagents at this node
# make a link to fast comms to the broker
self.graph.make_link(node, node.broker.node)
# ensure brokers have fast links to each other
for i, broker in enumerate(self.brokers.itervalues()):
for other in self.brokers.values()[i:]:
self.graph.make_link(broker.node, other.node)
self.mons["broker_util"] = Monitor("broker_util")
self.mons["broker_queue"] = Monitor("broker_queue")
def __init__(self,
registry_type=Registry,
broker_means=dists.normal(0.05),
**kw):
super(MdsModel, self).__init__(**kw)
regions = self.graph.regions
self.regions = [i for i in range(regions[0] * regions[1])]
self.brokers = dict()
for r in self.regions:
node = self.random_region_node(r)
node.server.service_time = self.service_dist(broker_means())
broker = Broker(r, node, registry_type(), 60, self.brokers)
# give them an intitial picture of the grid
for node in self.graph.nodes_in_region(r):
state = ResourceState(node.resource_agent, node.resource.free)
broker.registry.update_state(state)
self.brokers[r] = broker
for node in self.graph.nodes_iter():
node.broker = self.brokers[node.region]
# this nodes resource agent
node.resource_agent = MdsResourceAgent(node, 30)
# mapping of jobagents at this node
# make a link to fast comms to the broker
self.graph.make_link(node, node.broker.node)
# ensure brokers have fast links to each other
for i, broker in enumerate(self.brokers.itervalues()):
for other in self.brokers.values()[i:]:
self.graph.make_link(broker.node, other.node)
self.mons["broker_util"] = Monitor("broker_util")
self.mons["broker_queue"] = Monitor("broker_queue")