def main(simulated_time):
random.seed(RANDOM_SEED)
"""
TOPOLOGY from a json
"""
t = Topology()
t_json = create_json_topology()
t.load(t_json)
t.write("network.gexf")
"""
APPLICATION
"""
app = create_application()
"""
PLACEMENT algorithm
"""
placement = CloudPlacement(
"onCloud") # it defines the deployed rules: module-device
placement.scaleService({"ServiceA": 1})
"""
POPULATION algorithm
"""
#In ifogsim, during the creation of the application, the Sensors are assigned to the topology, in this case no. As mentioned, YAFS differentiates the adaptive sensors and their topological assignment.
#In their case, the use a statical assignment.
pop = Statical("Statical")
#For each type of sink modules we set a deployment on some type of devices
#A control sink consists on:
# args:
# model (str): identifies the device or devices where the sink is linked
# number (int): quantity of sinks linked in each device
# module (str): identifies the module from the app who receives the messages
pop.set_sink_control({
"model": "actuator-device",
"number": 1,
"module": app.get_sink_modules()
})
#In addition, a source includes a distribution function:
dDistribution = deterministicDistribution(name="Deterministic", time=100)
pop.set_src_control({
"model": "sensor-device",
"number": 1,
"message": app.get_message("M.A"),
"distribution": dDistribution
})
"""--
SELECTOR algorithm
"""
#Their "selector" is actually the shortest way, there is not type of orchestration algorithm.
#This implementation is already created in selector.class,called: First_ShortestPath
selectorPath = MinimunPath()
"""
SIMULATION ENGINE
"""
stop_time = simulated_time
s = Sim(t, default_results_path="Results")
s.deploy_app(app, placement, pop, selectorPath)
s.run(stop_time, show_progress_monitor=False)
def main(simulated_time):
random.seed(RANDOM_SEED)
np.random.seed(RANDOM_SEED)
"""
TOPOLOGY from a json
"""
t = Topology()
t_json = create_json_topology()
t.load(t_json)
t.write("network.gexf")
"""
APPLICATION
"""
app = create_application()
"""
PLACEMENT algorithm
"""
# MINHA IMPLEMENTACAO DO ALGORITMO PARA PLACEMENT.
# UTILIZA A PROPRIEDADE model:node
placement = UCPlacement(name="UCPlacement")
# Para criar replicas dos servicos
placement.scaleService({"MLTTask": 1, "FLTTask":1, "DLTTask":1})
"""
POPULATION algorithm
"""
#In ifogsim, during the creation of the application, the Sensors are assigned to the topology, in this case no. As mentioned, YAFS differentiates the adaptive sensors and their topological assignment.
#In their case, the use a statical assignment.
pop = Statical("Statical")
#For each type of sink modules we set a deployment on some type of devices
#A control sink consists on:
# args:
# model (str): identifies the device or devices where the sink is linked
# number (int): quantity of sinks linked in each device
# module (str): identifies the module from the app who receives the messages
pop.set_sink_control({"model": "sink","number":0,"module":app.get_sink_modules()})
#In addition, a source includes a distribution function:
dDistribution = deterministicDistribution(name="Deterministic",time=10)
# delayDistribution = deterministicDistributionStartPoint(400, 100, name="DelayDeterministic")
# number:quantidade de replicas
pop.set_src_control({"model": "camera", "number":1,"message": app.get_message("M.Cam"), "distribution": dDistribution})
# pop.set_src_control({"type": "mlt", "number":1,"message": app.get_message("M.MLT"), "distribution": dDistribution})
"""
SELECTOR algorithm
"""
#Their "selector" is actually the shortest way, there is not type of orchestration algorithm.
#This implementation is already created in selector.class,called: First_ShortestPath
selectorPath = MinimunPath()
# selectorPath = MinPath_RoundRobin()
"""
SIMULATION ENGINE
"""
stop_time = simulated_time
s = Sim(t, default_results_path="Results")
s.deploy_app(app, placement, pop, selectorPath)
s.run(stop_time,show_progress_monitor=False)
s.draw_allocated_topology() # for debugging
def main():
random.seed(RANDOM_SEED)
np.random.seed(RANDOM_SEED)
"""
Detalles
"""
topologies = {
"Grid": "data/grid200.graphml",
"BarabasiAlbert": "data/BarabasiAlbert2.graphml",
"RandomEuclidean": "data/RandomEuclidean.graphml",
"Lobster": "data/Lobster.graphml",
}
community_size = [50, 100, 150, 200, 300]
threshold_population_edge = 0.010
ltopo = []
lnodes = []
ledges = []
lavepath = []
leddev = []
try:
for key_topo in topologies.keys():
"""
TOPOLOGY from a json
"""
t = Topology()
# t_json = create_json_topology()
# t.load(t_json)
t.load_graphml(topologies[key_topo])
print "TOPOLOGY: %s" % key_topo
print "Total Nodes: %i" % len(t.G.nodes())
print "Total Vertexs: %i" % len(t.G.edges())
print "Average shortest path: %s" % nx.average_shortest_path_length(
t.G)
# t.write("network_ex2.gexf")
ltopo.append(key_topo)
lnodes.append(len(t.G.nodes()))
ledges.append(len(t.G.edges()))
lavepath.append(nx.average_shortest_path_length(t.G))
### COMPUTING Node degrees
### Stablish the Edge devices (acc. with Algoritm.nodedegree >... 1)
### Identify Cloud Device (max. degree)
deg = {}
edge = {}
cloud_device = 0
bestDeg = -1
for n in t.G.nodes():
d = t.G.degree(n)
if d > bestDeg:
bestDeg = d
cloud_device = int(n)
deg[n] = {"degree": d}
#### MUST BE IMPROVED
if key_topo in ["BarabasiAlbert", "Lobster"]:
if d == 1:
edge[n] = -1
elif key_topo in ["Grid"]:
if d <= 3:
edge[n] = -1
elif key_topo in ["RandomEuclidean"]:
if d > 14:
edge[n] = -1
print "Total Edge-Devices: %i" % len(edge.keys())
leddev.append(len(edge.keys()))
"""
OJO
"""
#TODO
if key_topo in ["Grid"]:
cloud_device = 320
print "ID of Cloud-Device: %i" % cloud_device
continue
#print "Total edges devices: %i" % len(edge.keys())
#nx.set_node_attributes(t.G, values=deg)
## COMPUTING MATRIX SHORTEST PATH among EDGE-devices
minPath = {}
minDis = {}
for d in edge.keys():
minDis[d] = []
for d1 in edge.keys():
if d == d1: continue
path = list(nx.shortest_path(t.G, source=d, target=d1))
minPath[(d, d1)] = path
minDis[d].append(len(path))
"""
WORKLOAD DEFINITION & REGION SENSORS (=Communities)
"""
for size in community_size:
print "SIZE Of Community: %i" % size
communities, com = generate_communities(
size, edge, minPath, minDis, threshold_population_edge,
cloud_device)
#print "Computed Communities"
# print communities
#for idx,c in enumerate(communities):
# print "\t %i - size: %i -: %s" %(idx,len(c),c)
### WRITING NETWORK
nx.set_node_attributes(t.G, values=com)
t.write("tmp/network-communities-%s-%i.gexf" %
(key_topo, size))
# exit()
#TODO suposicion cada comunidad es un tipo de carga
sensor_workload_types = communities
lambdas_wl = np.random.randint(low=5,
high=10,
size=len(sensor_workload_types))
id_lambdas = zip(range(0, len(lambdas_wl)), lambdas_wl)
id_lambdas.sort(key=lambda tup: tup[1],
reverse=True) # sorts in place
#print id_lambdas # [(1, 9), (0, 8)]
"""
topology.Centricity
"""
# Both next ids be extracted from topology.entities
all_nodes_dev = t.G.nodes()
edge_dev = edge.keys()
weights = computingWeights(t, all_nodes_dev, edge_dev,
sensor_workload_types)
print "Computed weights"
"""
APPLICATION
"""
apps = []
pops = []
for idx in range(0, len(sensor_workload_types)):
apps.append(create_application(idx))
pops.append(Statical("Statical-%i" % idx))
"""
PLACEMENT algorithm
"""
#There is not modules thus placement.functions are empty
placement = NoPlacementOfModules("empty")
"""--
SELECTOR algorithm
"""
# This implementation is already created in selector.class,called: First_ShortestPath
selectorPath = First_ShortestPath()
#In this point, the study analizes the behaviour of the deployment of (sink, src) modules (population) in different set of centroide algorithms
#functions = {"Cluster":"Cluster","Eigenvector":nx.eigenvector_centrality,"Current_flow_betweenness_centrality":nx.current_flow_betweenness_centrality,
# "Betweenness_centrality":nx.betweenness_centrality,"load_centrality":nx.load_centrality} #"katz_centrality":nx.katz_centrality,
#functions = {"Current_flow_betweenness_centrality":nx.current_flow_betweenness_centrality}
functions = {
"Cluster": "Cluster",
"Eigenvector": nx.eigenvector_centrality,
"Current_flow_betweenness_centrality":
nx.current_flow_betweenness_centrality,
"Betweenness_centrality": nx.betweenness_centrality,
#"Load_centrality": nx.load_centrality
} # "katz_centrality":nx.katz_centrality,
for f in functions:
print "Analysing network with algorithm: %s " % f
"""
POPULATION algorithm
"""
pops = []
for idx in range(0, len(sensor_workload_types)):
pops.append(Statical("Statical-%i" % idx))
# print "-"*20
# print "Function: %s" %f
# print "-"*20
if "Cluster" in f:
for idWL in id_lambdas:
idx = idWL[0]
# print idx
## idWL[0] #index WL
## idWL[1] #lambda
### ALL SINKS goes to Cluster ID: NODE 0
# print "\t", "SINK"
pops[idx].set_sink_control({
"id": [cloud_device],
"number":
1,
"module":
apps[idx].get_sink_modules()
})
# print "\t", "SOURCE"
# In addition, a source includes a distribution function:
dDistribution = deterministicDistribution(
name="Deterministic", time=idWL[1])
pops[idx].set_src_control({
"id":
sensor_workload_types[idx],
"number":
1,
"message":
apps[idx].get_message("m-st"),
"distribution":
dDistribution
})
else:
#Computing best device for each WL-type and each centrality function
print "\t Computando centralidad "
centralWL = range(0, len(weights))
for idx, v in enumerate(sensor_workload_types):
if idx % 20 == 0:
print "\t\t%i%%", idx
nx.set_edge_attributes(t.G,
values=weights[idx],
name="weight")
centrality = functions[f](t.G, weight="weight")
# print(['%s %0.2f' % (node, centrality[node]) for node in centrality])
centralWL[idx] = centrality
print "\t Generando SINK/SRCs centralidad "
previous_deploy = {} ## DEV -> ID:load
for idWL in id_lambdas:
idx = idWL[0]
## idWL[0] #index WL
## idWL[1] #lambda
for dev, value in sorted(
centralWL[idx].iteritems(),
key=lambda (k, v): (v, k),
reverse=True):
# print "%s: %s" % (dev, value)
#TODO CONTTOLAR LA CAPACIDAD DEL DISPOSITO HERE
if not dev in previous_deploy.values():
previous_deploy[idx] = dev
break
#TODO chequear que dEV es un device
# print "\t Previous deploy: ",previous_deploy
# print "\t NameApp: ",apps[idx].name
# print "\t Device:",dev
#Each application have a correspondence SRC/SINK among APPS
pops[idx].set_sink_control({
"id": [dev],
"number":
1,
"module":
apps[idx].get_sink_modules()
})
dDistribution = deterministicDistribution(
name="Deterministic", time=idWL[1])
#In addition, a source includes a distribution function:
pops[idx].set_src_control({
"id":
sensor_workload_types[idx],
"number":
1,
"message":
apps[idx].get_message("m-st"),
"distribution":
dDistribution
})
"""
SIMULATION ENGINE
"""
stop_time = 100 #CHECK
s = Sim(t)
for idx, app in enumerate(apps):
s.deploy_app(app, placement, pops[idx], selectorPath)
s.run(stop_time,
test_initial_deploy=False,
show_progress_monitor=False)
#end for algorithms
#end for communities size
#end for topology change
print ltopo
print lnodes
print ledges
print lavepath
print leddev
#end try
except:
print "Some error??"
def main(simulated_time, depth, police):
random.seed(RANDOM_SEED)
"""
TOPOLOGY from a json
"""
numOfDepts = depth
numOfMobilesPerDept = 4 # Thus, this variable is used in the population algorithm
# In YAFS simulator, entities representing mobiles devices (sensors or actuactors) are not necessary because they are simple "abstract" links to the access points
# in any case, they can be implemented with node entities with no capacity to execute services.
#
t = Topology()
t_json = create_json_topology(numOfDepts, numOfMobilesPerDept)
t.load(t_json)
t.write("network_%s.gexf" % depth)
"""
APPLICATION
"""
app = create_application()
"""
PLACEMENT algorithm
"""
#In this case: it will deploy all app.modules in the cloud
if police == "cloud":
print "cloud"
placement = CloudPlacement("onCloud")
placement.scaleService({
"Calculator": numOfDepts * numOfMobilesPerDept,
"Coordinator": 1
})
else:
print "EDGE"
placement = FogPlacement("onProxies")
placement.scaleService({
"Calculator": numOfMobilesPerDept,
"Coordinator": 1
})
# placement = ClusterPlacement("onCluster", activation_dist=next_time_periodic, time_shift=600)
"""
POPULATION algorithm
"""
#In ifogsim, during the creation of the application, the Sensors are assigned to the topology, in this case no. As mentioned, YAFS differentiates the adaptive sensors and their topological assignment.
#In their case, the use a statical assignment.
pop = Statical("Statical")
#For each type of sink modules we set a deployment on some type of devices
#A control sink consists on:
# args:
# model (str): identifies the device or devices where the sink is linked
# number (int): quantity of sinks linked in each device
# module (str): identifies the module from the app who receives the messages
pop.set_sink_control({
"model": "a",
"number": 1,
"module": app.get_sink_modules()
})
#In addition, a source includes a distribution function:
dDistribution = deterministicDistribution(name="Deterministic", time=100)
pop.set_src_control({
"model": "s",
"number": 1,
"message": app.get_message("M.EGG"),
"distribution": dDistribution
})
"""--
SELECTOR algorithm
"""
#Their "selector" is actually the shortest way, there is not type of orchestration algorithm.
#This implementation is already created in selector.class,called: First_ShortestPath
if police == "cloud":
selectorPath = CloudPath_RR()
else:
selectorPath = BroadPath(numOfMobilesPerDept)
"""
SIMULATION ENGINE
"""
stop_time = simulated_time
s = Sim(t,
default_results_path="Results_%s_%i_%i" %
(police, stop_time, depth))
s.deploy_app(app, placement, pop, selectorPath)
s.run(stop_time, test_initial_deploy=False, show_progress_monitor=False)
input_directory = temporal_folder + "normalized_trajectories.csv" #
logging.info("Loading trajectories from (cached file): %s" %
input_directory)
tracks = trackanimation.read_track(input_directory)
else:
input_directory = trajectories_path # can load csv files
logging.info("Loading trajectories from (raw files): %s" %
input_directory)
tracks = trackanimation.read_track(input_directory)
tracks = tracks.time_video_normalize(
time=number_simulation_steps, framerate=1) # framerate must be one
tracks.export(temporal_folder + "normalized_trajectories")
# 1.2 Network infrastructure
# Endpoint entities must have three attributes: level(=0) and lat/lng coordinates
t = Topology()
dataNetwork = json.load(open(experiment_path + 'networkDefinition.json'))
t.load_all_node_attr(dataNetwork)
# Performing multiple simulations
for i in range(nSimulations):
random.seed(i)
np.random.seed(i)
logging.info("Running Mobility Case - %s" % experiment_path)
start_time = time.time()
main(
path=experiment_path,
path_results=temporal_folder,
number_simulation_steps=number_simulation_steps,
tracks=tracks,
def main(simulated_time,experimento,file,study,it):
random.seed(it)
np.random.seed(it)
"""
TOPOLOGY from a json
"""
t = Topology()
dataNetwork = json.load(open(experimento+file+'-network.json'))
t.load(dataNetwork)
attNodes = {}
for k in t.G.nodes():
attNodes[k] = {"IPT": 1}
nx.set_node_attributes(t.G, values=attNodes)
# t.write("network.gexf")
"""
APPLICATION
"""
studyApp = study
if study=="FstrRep":
studyApp="Replica"
elif study == "Cloud":
studyApp="Single"
dataApp = json.load(open(experimento+file+'-app%s.json'%studyApp))
apps = create_applications_from_json(dataApp)
#for app in apps:
# print apps[app]
"""
PLACEMENT algorithm
"""
placementJson = json.load(open(experimento+file+'-alloc%s.json'%study))
placement = JSONPlacement(name="Placement",json=placementJson)
### Placement histogram
# listDevices =[]
# for item in placementJson["initialAllocation"]:
# listDevices.append(item["id_resource"])
# import matplotlib.pyplot as plt
# print listDevices
# print np.histogram(listDevices,bins=range(101))
# plt.hist(listDevices, bins=100) # arguments are passed to np.histogram
# plt.title("Placement Histogram")
# plt.show()
## exit()
"""
POPULATION algorithm
"""
studyUser = study
if study == "FstrRep":
studyUser = "******"
elif study == "Cloud":
studyUser = "******"
dataPopulation = json.load(open(experimento+file+'-users%s.json'%studyUser))
pop = JSONPopulation(name="Statical",json=dataPopulation,it=it)
"""
SELECTOR algorithm
"""
selectorPath = DeviceSpeedAwareRouting()
"""
SIMULATION ENGINE
"""
stop_time = simulated_time
s = Sim(t, default_results_path=experimento + "Results_%i_%s_%s_%i" % (it,file,study,stop_time))
"""
Failure process
"""
# time_shift = 10000
# distribution = deterministicDistributionStartPoint(name="Deterministic", time=time_shift,start=10000)
# failurefilelog = open(experimento+"Failure_%s_%i.csv" % (ilpPath,stop_time),"w")
# failurefilelog.write("node, module, time\n")
# idCloud = t.find_IDs({"type": "CLOUD"})[0] #[0] -> In this study there is only one CLOUD DEVICE
# centrality = np.load(pathExperimento+"centrality.npy")
# randomValues = np.load(pathExperimento+"random.npy")
# # s.deploy_monitor("Failure Generation", failureControl, distribution,sim=s,filelog=failurefilelog,ids=centrality)
# s.deploy_monitor("Failure Generation", failureControl, distribution,sim=s,filelog=failurefilelog,ids=randomValues)
#For each deployment the user - population have to contain only its specific sources
for aName in apps.keys():
#print "Deploying app: ",aName
pop_app = JSONPopulation(name="Statical_%s"%aName,json={},it=it)
data = []
for element in pop.data["sources"]:
if element['app'] == aName:
data.append(element)
pop_app.data["sources"]=data
s.deploy_app(apps[aName], placement, pop_app, selectorPath)
s.run(stop_time, test_initial_deploy=False, show_progress_monitor=False) #TEST to TRUE
sim.remove_node(node_to_remove)
for key in keys_DES:
sim.stop_process(key)
except IndexError:
None
else:
sim.stop = True ## We stop the simulation
def main(simulated_time, path, pathResults, case, it):
"""
TOPOLOGY from a json
"""
t = Topology()
dataNetwork = json.load(open(path + 'networkDefinition.json'))
t.load(dataNetwork)
t.write(path + "network.gexf")
# t = loadTopology(path + 'test_GLP.gml')
"""
APPLICATION
"""
dataApp = json.load(open(path + 'appDefinition.json'))
apps = create_applications_from_json(dataApp)
# for app in apps:
# print apps[app]
"""
PLACEMENT algorithm
"""
# In our model only initial cloud placements are enabled
def main(stop_time, it):
'''
TOPOLOGY DEFINITION (from JSON file)
'''
THIS_FOLDER = os.path.dirname(os.path.abspath(__file__))
topology_json = json.load(
open(THIS_FOLDER + '/data/topology_definition.json'))
t = Topology()
t.load_all_node_attr(topology_json)
nx.write_gexf(t.G, THIS_FOLDER + "/results/topology.gexf"
) # exported .gexf file for visualizing it with Gephi
print(t.G.nodes())
'''
APPLICATION DEFINITION (from JSON file)
'''
THIS_FOLDER = os.path.dirname(os.path.abspath(__file__))
app_json = json.load(open(THIS_FOLDER + '/data/app_definition.json'))
apps = create_applications_from_json(
app_json) # this array will consist of only one app
'''
MODULE PLACEMENT (from JSON file)
'''
placement_json = json.load(
open(THIS_FOLDER + '/data/alloc_definition.json'))
placement = JSONPlacement(name="Placement", json=placement_json)
'''
ROUTING ALGORITHM (of messages along the topology)
'''
selector_path = DeviceSpeedAwareRouting()
'''
SIMULATION ENGINE
'''
THIS_FOLDER = os.path.dirname(os.path.abspath(__file__))
s = Sim(t, default_results_path=THIS_FOLDER + "/results/s_trace")
'''
DEPLOY OF THE APP'S MODULES (from JSON file)
'''
for app in apps.keys():
s.deploy_app(apps[app], placement, selector_path)
print(apps["water_pipe_control"])
'''
DEPLOY INITIAL WORKLOAD (from JSON file)
'''
population_json = json.load(
open(THIS_FOLDER + '/data/population_definition.json'))
for source in population_json["sources"]:
app_name = source["app"]
app = s.apps[app_name]
msg = app.get_message(source["message"])
node = source["id_resource"]
dist = deterministic_distribution(100, name="Deterministic")
idDES = s.deploy_source(app_name,
id_node=node,
msg=msg,
distribution=dist)
'''
RUNNING - last step
'''
s.run(stop_time) # To test deployments put test_initial_deploy a TRUE
def main(simulated_time):
random.seed(RANDOM_SEED)
"""
TOPOLOGY from a json
"""
t = Topology()
t.G = nx.read_graphml("Euclidean.graphml")
t.G = nx.convert_node_labels_to_integers(t.G, first_label=0, ordering='default', label_attribute=None)
print "Nodes: %i" %len(t.G.nodes())
print "Edges: %i" %len(t.G.edges())
#MANDATORY fields of a link
# Default values = {"BW": 1, "PR": 1}
valuesOne = dict(itertools.izip(t.G.edges(),np.ones(len(t.G.edges()))))
nx.set_edge_attributes(t.G, name='BW', values=valuesOne)
nx.set_edge_attributes(t.G, name='PR', values=valuesOne)
centrality = nx.betweenness_centrality(t.G)
nx.set_node_attributes(t.G, name="centrality", values=centrality)
sorted_clustMeasure = sorted(centrality.items(), key=operator.itemgetter(1), reverse=True)
top20_devices = sorted_clustMeasure[:20]
main_fog_device = copy.copy(top20_devices[0][0])
print "-" * 20
print "Top 20 centralised nodes:"
for item in top20_devices:
print item
print "-"*20
"""
APPLICATION
"""
app1 = create_application("app1")
"""
PLACEMENT algorithm
"""
#There are not modules to place.
placement = NoPlacementOfModules("NoPlacement")
"""
POPULATION algorithm
"""
number_generators = int(len(t.G)*0.1)
print number_generators
#you can use whatever funciton to change the topology
dStart = deterministicDistributionStartPoint(0, 100, name="Deterministic")
dStart2 = exponentialDistributionStartPoint(500, 100.0, name="Deterministic")
pop = Pop_and_Failures(name="mttf-nodes",srcs = number_generators,activation_dist=dStart2 )
pop.set_sink_control({"ids": top20_devices, "number": 1, "module": app1.get_sink_modules()})
dDistribution = deterministicDistribution(name="Deterministic", time=10)
pop.set_src_control(
{"number": 1, "message": app1.get_message("M.Action"), "distribution": dDistribution})
#In addition, a source includes a distribution function:
"""--
SELECTOR algorithm
"""
selectorPath = BroadPath()
"""
SIMULATION ENGINE
"""
s = Sim(t, default_results_path="Results_%s_exp" % (simulated_time))
s.deploy_app(app1, placement, pop, selectorPath)
s.run(simulated_time,test_initial_deploy=False,show_progress_monitor=False)
# s.draw_allocated_topology() # for debugging
print "Total nodes available in the toopology %i" %len(s.topology.G.nodes())
print "Total edges available in the toopology %i" %len(s.topology.G.edges())
print pop.nodes_removed
def main(simulated_time):
random.seed(RANDOM_SEED)
"""
TOPOLOGY from a json
"""
t = Topology()
data = json.load(open('egg_infrastructure.json'))
pprint(data["entity"])
t.load(data)
t.write("network.gexf")
"""
APPLICATION
"""
app = create_application()
"""
PLACEMENT algorithm
"""
#This adaptation can be done manually or automatically.
# We make a simple manual allocation from the FogTorch output
#8 - [client_0_0->sp_0_0][client_0_1->sp_0_1][client_0_2->sp_0_2][client_0_3->sp_0_3][client_1_0->sp_1_0][client_1_1->sp_1_1][client_1_2->sp_1_2]
# [client_1_3->sp_1_3]
# [concentrator->gw_0][coordinator->gw_1]; 0.105; 2.5; 6.01
placementJson = {
"initialAllocation": [
{"app": "EGG_GAME",
"module_name": "Calculator",
"id_resource": 3},
{"app": "EGG_GAME",
"module_name": "Coordinator",
"id_resource": 8},
{"app": "EGG_GAME",
"module_name": "Client",
"id_resource": 4},
{"app": "EGG_GAME",
"module_name": "Client",
"id_resource": 5},
{"app": "EGG_GAME",
"module_name": "Client",
"id_resource": 6},
{"app": "EGG_GAME",
"module_name": "Client",
"id_resource": 7},
{"app": "EGG_GAME",
"module_name": "Client",
"id_resource": 9},
{"app": "EGG_GAME",
"module_name": "Client",
"id_resource": 10},
{"app": "EGG_GAME",
"module_name": "Client",
"id_resource": 11},
{"app": "EGG_GAME",
"module_name": "Client",
"id_resource": 12},
]
}
placement = JSONPlacement(name="Places",json=placementJson)
"""
POPULATION algorithm
"""
populationJSON = {
"sinks": [
{"app": "EGG_GAME", "module_name": "Display", "id_resource": 4},
{"app": "EGG_GAME", "module_name": "Display", "id_resource": 5},
{"app": "EGG_GAME", "module_name": "Display", "id_resource": 6},
{"app": "EGG_GAME", "module_name": "Display", "id_resource": 7},
{"app": "EGG_GAME", "module_name": "Display", "id_resource": 9},
{"app": "EGG_GAME", "module_name": "Display", "id_resource": 10},
{"app": "EGG_GAME", "module_name": "Display", "id_resource": 11},
{"app": "EGG_GAME", "module_name": "Display", "id_resource": 12}
],
"sources":[
{"app": "EGG_GAME", "message":"M.EGG", "lambda":100,"id_resource":4},
{"app": "EGG_GAME", "message":"M.EGG", "lambda":100,"id_resource":5},
{"app": "EGG_GAME", "message":"M.EGG", "lambda":100,"id_resource":6},
{"app": "EGG_GAME", "message":"M.EGG", "lambda":100,"id_resource":7},
{"app": "EGG_GAME", "message":"M.EGG", "lambda":100,"id_resource":9},
{"app": "EGG_GAME", "message":"M.EGG", "lambda":100,"id_resource":10},
{"app": "EGG_GAME", "message":"M.EGG", "lambda":100,"id_resource":11},
{"app": "EGG_GAME", "message":"M.EGG", "lambda":100,"id_resource":12},
]
}
pop = JSONPopulation(name="Statical",json=populationJSON,iteration=0)
"""
SELECTOR algorithm
"""
selectorPath = MinShortPath()
"""
SIMULATION ENGINE
"""
stop_time = simulated_time
s = Sim(t, default_results_path="Results_%i" % (stop_time))
s.deploy_app(app, placement, pop, selectorPath)
s.run(stop_time, test_initial_deploy=False, show_progress_monitor=False)
s.print_debug_assignaments()
def main(simulated_time):
random.seed(RANDOM_SEED)
"""
TOPOLOGY from a json
"""
t = Topology()
t.G = nx.read_graphml("Euclidean.graphml")
t.G = nx.convert_node_labels_to_integers(t.G,
first_label=0,
ordering='default',
label_attribute=None)
print "Nodes: %i" % len(t.G.nodes())
print "Edges: %i" % len(t.G.edges())
#MANDATORY fields of a link
# Default values = {"BW": 1, "PR": 1}
valuesOne = dict(itertools.izip(t.G.edges(), np.ones(len(t.G.edges()))))
nx.set_edge_attributes(t.G, name='BW', values=valuesOne)
nx.set_edge_attributes(t.G, name='PR', values=valuesOne)
centrality = nx.betweenness_centrality(t.G)
nx.set_node_attributes(t.G, name="centrality", values=centrality)
sorted_clustMeasure = sorted(centrality.items(),
key=operator.itemgetter(1),
reverse=True)
top20_devices = sorted_clustMeasure[:20]
main_fog_device = copy.copy(top20_devices[0][0])
print "-" * 20
print "Top 20 centralised nodes:"
for item in top20_devices:
print item
print "-" * 20
"""
APPLICATION
"""
app1 = create_application("app1")
app2 = create_application("app2")
"""
PLACEMENT algorithm
"""
#There are not modules to place.
placement = NoPlacementOfModules("NoPlacement")
"""
POPULATION algorithm
"""
number_generators = int(len(t.G) * 0.1)
print number_generators
dDistribution = deterministicDistributionStartPoint(3000,
300,
name="Deterministic")
dDistributionSrc = deterministicDistribution(name="Deterministic", time=10)
pop1 = Evolutive(top20_devices,
number_generators,
name="top",
activation_dist=dDistribution)
pop1.set_sink_control({
"app": app1.name,
"number": 1,
"module": app1.get_sink_modules()
})
pop1.set_src_control({
"number": 1,
"message": app1.get_message("M.Action"),
"distribution": dDistributionSrc
})
pop2 = Statical(number_generators, name="Statical")
pop2.set_sink_control({
"id": main_fog_device,
"number": number_generators,
"module": app2.get_sink_modules()
})
pop2.set_src_control({
"number": 1,
"message": app2.get_message("M.Action"),
"distribution": dDistributionSrc
})
#In addition, a source includes a distribution function:
"""--
SELECTOR algorithm
"""
selectorPath1 = BroadPath()
selectorPath2 = CloudPath_RR()
"""
SIMULATION ENGINE
"""
s = Sim(t, default_results_path="Results_%s_singleApp1" % (simulated_time))
s.deploy_app(app1, placement, pop1, selectorPath1)
# s.deploy_app(app2, placement, pop2, selectorPath2)
s.run(simulated_time,
test_initial_deploy=False,
show_progress_monitor=False)
"COST": 3,
"WATT": 20.0
}
sensor_dev = {
"id": 1,
"model": "sensor-device",
"IPT": 100 * 10 ^ 6,
"RAM": 4000,
"COST": 3,
"WATT": 40.0
}
actuator_dev = {
"id": 2,
"model": "actuator-device",
"IPT": 100 * 10 ^ 6,
"RAM": 4000,
"COST": 3,
"WATT": 40.0
}
link1 = {"s": 0, "d": 1, "BW": 1, "PR": 10}
link2 = {"s": 0, "d": 2, "BW": 1, "PR": 1}
topology_json["entity"].append(cloud_dev)
topology_json["entity"].append(sensor_dev)
topology_json["entity"].append(actuator_dev)
topology_json["link"].append(link1)
topology_json["link"].append(link2)
t = Topology()
t.load(topology_json)
def main(simulated_time):
random.seed(RANDOM_SEED)
np.random.seed(RANDOM_SEED)
"""
TOPOLOGY from a json
"""
t = Topology()
t.G = nx.read_graphml("Euclidean.graphml")
ls = list(t.G.nodes)
li = {x: int(x) for x in ls}
nx.relabel_nodes(t.G, li, False) #Transform str-labels to int-labels
print "Nodes: %i" % len(t.G.nodes())
print "Edges: %i" % len(t.G.edges())
#MANDATORY fields of a link
# Default values = {"BW": 1, "PR": 1}
valuesOne = dict(itertools.izip(t.G.edges(), np.ones(len(t.G.edges()))))
nx.set_edge_attributes(t.G, name='BW', values=valuesOne)
nx.set_edge_attributes(t.G, name='PR', values=valuesOne)
centrality = nx.betweenness_centrality(t.G)
nx.set_node_attributes(t.G, name="centrality", values=centrality)
sorted_clustMeasure = sorted(centrality.items(),
key=operator.itemgetter(1),
reverse=True)
top20_devices = sorted_clustMeasure[0:20]
main_fog_device = copy.copy(top20_devices[0][0])
# df = pd.read_csv("pos_network.csv")
# pos = {}
# for r in df.iterrows():
# lat = r[1].x
# lng = r[1].y
# pos[r[0]] = (lat, lng)
# fig = plt.figure(figsize=(10, 8), dpi=100)
# nx.draw(t.G, with_labels=True,pos=pos,node_size=60,node_color="orange", font_size=8)
# plt.savefig('labels.png')
# exit()
print "-" * 20
print "Best top centralized device: ", main_fog_device
print "-" * 20
"""
APPLICATION
"""
app1 = create_application("app1")
"""
PLACEMENT algorithm
"""
#There are not modules to place.
placement = NoPlacementOfModules("NoPlacement")
"""
POPULATION algorithm
"""
number_generators = int(len(t.G) * 0.1)
print "Number of generators %i" % number_generators
#you can use whatever funciton to change the topology
dStart = deterministicDistributionStartPoint(500,
400,
name="Deterministic")
pop = Population_Move(name="mttf-nodes",
srcs=number_generators,
node_dst=main_fog_device,
activation_dist=dStart)
pop.set_sink_control({
"id": main_fog_device,
"number": number_generators,
"module": app1.get_sink_modules()
})
dDistribution = deterministicDistribution(name="Deterministic", time=100)
pop.set_src_control({
"number": 1,
"message": app1.get_message("M.Action"),
"distribution": dDistribution
})
#In addition, a source includes a distribution function:
"""--
SELECTOR algorithm
"""
selectorPath = CloudPath_RR()
"""
SIMULATION ENGINE
"""
s = Sim(t, default_results_path="Results_%s" % (simulated_time))
s.deploy_app(app1, placement, pop, selectorPath)
s.run(simulated_time,
test_initial_deploy=False,
show_progress_monitor=False)
# s.draw_allocated_topology() # for debugging
s.print_debug_assignaments()
def main(simulated_time):
random.seed(RANDOM_SEED)
np.random.seed(RANDOM_SEED)
"""
TOPOLOGY from a json
"""
topology = Topology()
t_json = create_json_topology(numMLO=3, numBroker=1, numFLO=2, numDLO=1)
topology.load(t_json)
# t.write("network.gexf")
"""
APPLICATIONS
"""
app = create_application(name="Workflow1",
params={
"max_latency": 100,
"FPS_total": 120
})
app2 = create_application(name="Workflow2",
params={
"max_latency": 100,
"FPS_total": 120
})
"""
PLACEMENT algorithm
"""
# MELINDA's placement algorithm
# It uses model:node
placement = UCPlacement(name="UCPlacement")
# Which services will be necessary
placement.scaleService({"MLO": 3, "Broker": 1, "FLO": 2, "DLO": 1})
"""
POPULATION algorithm
"""
# In ifogsim, during the creation of the application, the Sensors are assigned to the topology,
# in this case no. As mentioned, YAFS differentiates the adaptive sensors and their topological assignment.
# In their case, the use a statical assignment.
pop = Statical("Statical")
# For each type of sink modules we set a deployment on some type of devices
# A control sink consists on:
# args:
# model (str): identifies the device or devices where the sink is linked
# number (int): quantity of sinks linked in each device
# module (str): identifies the module from the app who receives the messages
pop.set_sink_control({
"model": "sink",
"number": 1,
"module": app.get_sink_modules()
})
# In addition, a source includes a distribution function:
dDistribution = deterministicDistribution(name="Deterministic", time=5)
# delayDistribution = deterministicDistributionStartPoint(400, 100, name="DelayDeterministic")
# number:quantidade de replicas
pop.set_src_control({
"model": "camera",
"number": 1,
"message": app.get_message("RawVideo"),
"distribution": dDistribution
})
# pop.set_src_control({"type": "mlt", "number":1,"message": app.get_message("M.MLT"),
# "distribution": dDistribution})
"""
SELECTOR algorithm
"""
# Their "selector" is actually the shortest way, there is not type of orchestration algorithm.
# This implementation is already created in selector.class,called: First_ShortestPath
# selectorPath = MinimunPath()
selectorPath = MinPath_RoundRobin()
"""
SIMULATION ENGINE
"""
stop_time = simulated_time
s = Sim(topology, default_results_path="Results")
s.deploy_app(app, placement, pop, selectorPath)
s.run(stop_time, show_progress_monitor=False)
s.draw_allocated_topology() # for debugging
def main(simulated_time,experimento,ilpPath):
random.seed(RANDOM_SEED)
np.random.seed(RANDOM_SEED)
"""
TOPOLOGY from a json
"""
t = Topology()
dataNetwork = json.load(open(experimento+'networkDefinition.json'))
t.load(dataNetwork)
t.write("network.gexf")
"""
APPLICATION
"""
dataApp = json.load(open(experimento+'appDefinition.json'))
apps = create_applications_from_json(dataApp)
#for app in apps:
# print apps[app]
"""
PLACEMENT algorithm
"""
placementJson = json.load(open(experimento+'allocDefinition%s.json'%ilpPath))
placement = JSONPlacement(name="Placement",json=placementJson)
### Placement histogram
# listDevices =[]
# for item in placementJson["initialAllocation"]:
# listDevices.append(item["id_resource"])
# import matplotlib.pyplot as plt
# print listDevices
# print np.histogram(listDevices,bins=range(101))
# plt.hist(listDevices, bins=100) # arguments are passed to np.histogram
# plt.title("Placement Histogram")
# plt.show()
## exit()
"""
POPULATION algorithm
"""
dataPopulation = json.load(open(experimento+'usersDefinition.json'))
pop = JSONPopulation(name="Statical",json=dataPopulation)
"""
SELECTOR algorithm
"""
selectorPath = DeviceSpeedAwareRouting()
"""
SIMULATION ENGINE
"""
stop_time = simulated_time
s = Sim(t, default_results_path=experimento + "Results_%s_%i" % (ilpPath, stop_time))
#For each deployment the user - population have to contain only its specific sources
for aName in apps.keys():
print "Deploying app: ",aName
pop_app = JSONPopulation(name="Statical_%s"%aName,json={})
data = []
for element in pop.data["sources"]:
if element['app'] == aName:
data.append(element)
pop_app.data["sources"]=data
s.deploy_app(apps[aName], placement, pop_app, selectorPath)
s.run(stop_time, test_initial_deploy=False, show_progress_monitor=False) #TEST to TRUE