def generate_iot(args):
print("generate iot ", args.output)
# Constraints and variables with a power-law constraint graph:
variables, constraints, domain = generate_powerlaw_var_constraints(
args.num, args.domain, args.range
)
# Build a dcop and computation graph with no agents, just to be able to
# compute the footprint of computations:
dcop = DCOP(
"graph coloring",
"min",
domains={"d": domain},
variables=variables,
agents={},
constraints=constraints,
)
graph_module = import_module("pydcop.computations_graph.factor_graph")
cg = graph_module.build_computation_graph(dcop)
algo_module = load_algorithm_module("maxsum")
footprints = {c.name: algo_module.computation_memory(c) for c in cg.nodes}
# Generate an agent for each variable computation and assign the
# computation to that agent.
agents = {} # type: Dict[str, AgentDef]
mapping = defaultdict(lambda: []) # type: Dict[str, List[str]]
for comp in cg.nodes:
if isinstance(comp, VariableComputationNode):
a_name = agt_name(comp.name)
agt = AgentDef(
a_name,
capacity=footprints[comp.name] * 100,
default_hosting_cost=10,
hosting_costs=agt_hosting_costs(comp, cg),
default_route=1,
routes=agt_route_costs(comp, cg),
)
logger.debug(
"Create agent %s for computation %s with capacity %s",
agt.name,
comp.name,
agt.capacity,
)
agents[agt.name] = agt
mapping[agt.name].append(comp.name)
# Now, we have created all the agents and distributed all the variables
# let's distribute the factor computations.
msg_load = msg_load_func(cg, algo_module.communication_load)
factor_mapping = distribute_factors(agents, cg, footprints, mapping, msg_load)
for a in mapping:
mapping[a].extend(factor_mapping[a])
dcop = DCOP(
"graph coloring",
"min",
domains={"d": domain},
variables=variables,
agents=agents,
constraints=constraints,
)
distribution = Distribution(mapping)
if args.output:
outputfile = args.output
write_in_file(outputfile, dcop_yaml(dcop))
dist = distribution.mapping()
cost = ilp_compref.distribution_cost(
distribution,
cg,
dcop.agents.values(),
computation_memory=algo_module.computation_memory,
communication_load=algo_module.communication_load,
)
result = {
"inputs": {
"dist_algo": "io_problem",
"dcop": args.output,
"graph": "factor_graph",
"algo": "maxsum",
},
"distribution": dist,
"cost": cost,
}
outputfile = "dist_" + args.output
write_in_file(outputfile, yaml.dump(result))
else:
print(dcop_yaml(dcop))
def generate(args):
slots, events, resources = generate_problem_definition(
args.slots_count,
args.resources_count,
args.max_resource_value,
args.events_count,
args.max_length_event,
args.max_resources_event,
)
penalty = args.max_resource_value * args.slots_count * args.resources_count
variables, constraints, agents = peav_model(slots, events, resources,
penalty)
domains = {
variable.domain.name: variable.domain
for variable in variables.values()
}
variables = {variable.name: variable for variable in variables.values()}
# agents_defs = {agent.name: agent for agent, _ in agents.values()}
# Generate agents hosting and route costs
agents_defs = {}
if not args.no_agents:
for agent, agt_variables in agents.items():
kw = {}
kw["hosting_costs"] = {v.name: 0 for v in agt_variables}
if args.hosting_default:
kw["default_hosting_cost"] = args.hosting_default
if args.capacity:
kw["capacity"] = args.capacity
if args.routes_default:
kw["default_route"] = args.routes_default
agents_defs[agent] = AgentDef(agent, **kw)
dcop = DCOP(
"MeetingSceduling",
objective="max",
domains=domains,
variables=variables,
constraints=constraints,
agents=agents_defs,
)
if not args.no_agents:
distribution = Distribution({
agent.name: [v.name for v in agents[agent.name]]
for agent in agents_defs.values()
})
if args.output:
output_file = args.output
with open(output_file, encoding="utf-8", mode="w") as fo:
fo.write(dcop_yaml(dcop))
if not args.no_agents:
dist_result = {
"inputs": {
"dist_algo": "peav",
"dcop": output_file,
"graph": "constraints_graph",
"algo": "NA",
},
"distribution": distribution.mapping(),
"cost": None,
}
path, ext = splitext(output_file)
dist_output_file = f"{path}_dist{ext}"
with open(dist_output_file, encoding="utf-8", mode="w") as fo:
fo.write(yaml.dump(dist_result))
else:
print(dcop_yaml(dcop))
if not args.no_agents:
dist_result = {
"inputs": {
"dist_algo": "peav",
"dcop": "NA",
"graph": "constraints_graph",
"algo": "NA",
},
"distribution": distribution.mapping(),
"cost": None,
}
# FIXME proper serialization of the distribution:
print(yaml.dump(dist_result))