def r(list_flops):
result = []
res = Resource("res_0")
for flop, i in zip(list_flops, range(len(list_flops))):
node = Node(res.name + "_node_" + str(i), res, [SoftItem.ANY_SOFT])
node.flops = flop
result.append(node)
res.nodes = result
return [res]
def generate_resources(list_flops):
result = []
for nodes_list, j in zip(list_flops, range(len(list_flops))):
blade = []
res = Resource("res_" + str(j))
for flop, i in zip(nodes_list, range(len(nodes_list))):
node = Node(res.name + "_node_" + str(i), res, [SoftItem.ANY_SOFT])
node.flops = flop
blade.append(node)
res.nodes = blade
result.append(res)
return result
def as_schedule(dct):
if '__cls__' in dct and dct['__cls__'] == 'Node':
res = dct['resource']
node = Node(dct['name'], res, dct['soft'])
node.flops = dct['flops']
return node
if '__cls__' in dct and dct['__cls__'] == 'ScheduleItem':
task = task_dict[dct['job']]
scItem = ScheduleItem(task, dct['start_time'], dct['end_time'])
scItem.state = dct['state']
return scItem
if '__cls__' in dct and dct['__cls__'] == 'Schedule':
mapping = {node_values['node']: node_values['value'] for node_values in dct['mapping']}
schedule = Schedule(mapping)
return schedule
if '__cls__' in dct and dct['__cls__'] == 'Resource':
res = Resource(dct['name'])
res.nodes = dct['nodes']
return res
if '__cls__' in dct and dct['__cls__'] == 'SaveBundle':
all_nodes = set()
for res in dct['dedicated_resources']:
for node in res.nodes:
node.resource = res
all_nodes.update(res.nodes)
all_nodes = {node.name: node for node in all_nodes}
dct['ga_schedule'].mapping = {all_nodes[node_name]: values for (node_name, values) in
dct['ga_schedule'].mapping.items()}
bundle = SaveBundle(dct['name'],
dct['dedicated_resources'],
dct['transfer_mx'],
dct['ideal_flops'],
dct['ga_schedule'],
dct['wf_name'])
return bundle
return dct
def generate(self):
random = Random()
resCount = self.rand(random, self.min_res_count, self.max_res_count)
resources = list()
for i in range(0,resCount):
res = Resource("res_" + str(i))
resources.append(res)
nodeCount = self.rand(random, self.min_node_count, self.max_node_count)
for j in range(0, nodeCount):
node = Node(res.name + "_node_" + str(j), res, [SoftItem.ANY_SOFT])
##TODO: repair it later
node.flops = self.rand(random, self.min_flops, self.max_flops)
#if j == 0:
# node.flops = 10
#if j == 1:
# node.flops = 15
#if j == 2:
# node.flops = 25
#if j == 3:
# node.flops = 30
res.nodes.add(node)
return resources
def generate_public_resources(self):
## TODO: remake it later
#(public_resources, generate_reliability, generate_probability_law_for_(task,node)_pair) = generate public_resource
resCount = 3
resources = list()
for i in range(0, resCount):
res = Resource("public_res_" + str(i))
resources.append(res)
nodeCount = None
if i == 0:
nodeCount = 15
elif i == 1:
nodeCount = 12
elif i == 2:
nodeCount = 9
for j in range(0, nodeCount):
node = Node(res.name + "_node_" + str(j), res, [SoftItem.ANY_SOFT])
# if j == 0:
# node.flops = 10 + 5
# if j == 1:
# node.flops = 15 + 10#10*3
# if j == 2:
# node.flops = 25 + 10#25*3
# if j == 3:
# node.flops = 25 + 10#25*3
# if j == 4:
# node.flops = 30 + 10#30*3
# if j == 5:
# node.flops = 10 + 5
# if j == 6:
# node.flops = 15 + 10#10*3
# if j == 7:
# node.flops = 25 + 10#25*3
# if j == 8:
# node.flops = 25 + 10#25*3
# if j == 9:
# node.flops = 30 + 10#30*3
# if j == 10:
# node.flops = 10 + 5
# if j == 11:
# node.flops = 15 + 10#10*3
# if j == 12:
# node.flops = 25 + 10#25*3
# if j == 13:
# node.flops = 25 + 10#25*3
# if j == 14:
# node.flops = 30 + 10#30*3
if j == 0:
node.flops = 10
if j == 1:
node.flops = 15#10*3
if j == 2:
node.flops = 25#25*3
if j == 3:
node.flops = 25#25*3
if j == 4:
node.flops = 30#30*3
if j == 5:
node.flops = 10
if j == 6:
node.flops = 15#10*3
if j == 7:
node.flops = 25#25*3
if j == 8:
node.flops = 25#25*3
if j == 9:
node.flops = 30#30*3
if j == 10:
node.flops = 10
if j == 11:
node.flops = 15#10*3
if j == 12:
node.flops = 25#25*3
if j == 13:
node.flops = 25#25*3
if j == 14:
node.flops = 30#30*3
res.nodes.add(node)
nodes = HeftHelper.to_nodes(resources)
reliability_map = {node.name: 0.9 for node in nodes}
def probability_estimator(dt, comp_estimation, transfer_estimation):
M = comp_estimation + transfer_estimation
sigma = 0.1 * M
result = 0.5 *(1 + math.erf((dt - M)/sigma))
return result
return (resources, reliability_map, probability_estimator)
