def update_jobs_fleet_instances(optimal_cnts, autoscaling_group, max_inst, min_inst, cnts_per_inst):
current_instances = autoscaling.get_capacity(autoscaling_group)
optimal_instances = calculate_optimal_job_instance_capacity(optimal_cnts, cnts_per_inst)
tasks_log.info("JobsInstances - Current:%s Optimal:%s" % (str(current_instances), str(optimal_instances)))
if optimal_instances > current_instances and current_instances < max_inst:
tasks_log.info("Jobs - Increasing Instance Capacity")
autoscaling.increase_capacity(autoscaling_group)
return current_instances+1
elif optimal_instances < current_instances and current_instances > min_inst:
tasks_log.info("Jobs - Reducing Instance Capacity")
autoscaling.decrease_capacity(autoscaling_group)
return current_instances-1
else:
tasks_log.info("Jobs - Leaving Instance Capacity Unchanged")
return current_instances
def update_jobs_fleet_instances(optimal_cnts, autoscaling_group, max_inst,
min_inst, cnts_per_inst):
current_instances = autoscaling.get_capacity(autoscaling_group)
optimal_instances = calculate_optimal_job_instance_capacity(
optimal_cnts, cnts_per_inst)
tasks_log.info("JobsInstances - Current:%s Optimal:%s" %
(str(current_instances), str(optimal_instances)))
if optimal_instances > current_instances and current_instances < max_inst:
tasks_log.info("Jobs - Increasing Instance Capacity")
autoscaling.increase_capacity(autoscaling_group)
return current_instances + 1
elif optimal_instances < current_instances and current_instances > min_inst:
tasks_log.info("Jobs - Reducing Instance Capacity")
autoscaling.decrease_capacity(autoscaling_group)
return current_instances - 1
else:
tasks_log.info("Jobs - Leaving Instance Capacity Unchanged")
return current_instances
def update_job_items_fleet_instances(autoscaling_grp, max_insts, min_insts, cntrs_per_inst, cntrs):
"""
Fast scale up. Slow scale down.
"""
current_instances = autoscaling.get_capacity(autoscaling_grp)
optimal_instances = calculate_optimal_job_item_instance_capacity(cntrs_per_inst, cntrs)
tasks_log.info("JobItemsInstances: Current:%s Optimal:%s" % (str(current_instances),
str(optimal_instances)))
if optimal_instances > current_instances and current_instances < max_insts:
tasks_log.info("JobItems - Increasing Instance Capacity")
autoscaling.increase_capacity(autoscaling_grp)
return current_instances+1
elif optimal_instances < current_instances and current_instances > min_insts:
tasks_log.info("JobItems - Reducing Instance Capacity")
autoscaling.decrease_capacity(autoscaling_grp)
return current_instances-1
else:
tasks_log.info("JobItems - Leaving Instance Capacity Unchanged")
return current_instances
def update_job_items_fleet_instances(autoscaling_grp, max_insts, min_insts,
cntrs_per_inst, cntrs):
"""
Fast scale up. Slow scale down.
"""
current_instances = autoscaling.get_capacity(autoscaling_grp)
optimal_instances = calculate_optimal_job_item_instance_capacity(
cntrs_per_inst, cntrs)
tasks_log.info("JobItemsInstances: Current:%s Optimal:%s" %
(str(current_instances), str(optimal_instances)))
if optimal_instances > current_instances and current_instances < max_insts:
tasks_log.info("JobItems - Increasing Instance Capacity")
autoscaling.increase_capacity(autoscaling_grp)
return current_instances + 1
elif optimal_instances < current_instances and current_instances > min_insts:
tasks_log.info("JobItems - Reducing Instance Capacity")
autoscaling.decrease_capacity(autoscaling_grp)
return current_instances - 1
else:
tasks_log.info("JobItems - Leaving Instance Capacity Unchanged")
return current_instances
def test_decrease_capacity(group):
initial_capacity = autoscaling.get_capacity(group)
autoscaling.decrease_capacity(group)
result = wait_for_capacity_change(autoscaling, group, initial_capacity - 1)
assert result is True
