def aws_underutilized_resources_reduced_cost(accounts):
now = datetime.utcnow()
date_from = now.replace(hour=0, minute=0, second=0, microsecond=0) - relativedelta(months=6)
date_to = now.replace(hour=23, minute=59, second=59, microsecond=999999)
resources = AWSMetric.underutilized_resources(account.key for account in accounts)
resource_ids = set(r['id'] for r in resources['resources'])
months = AWSDetailedLineitem.get_monthly_cost_by_resource(resource_ids, date_from=date_from, date_to=date_to)
res = { # Simply multiply every cost by 20% as all instances usage is
k: v * 0.2 # less than 20%. TODO: intelligently find the best type
for k, v in months.iteritems()
}
return jsonify(res)
def compute_reservation_forecast(keys):
if isinstance(keys, models.AWSKey):
keys = [keys]
elif not isinstance(keys, list):
keys = list(keys)
if not all(isinstance(k, models.AWSKey) for k in keys):
raise TypeError('All keys must be AWSKey.')
now = datetime.utcnow().replace(hour=0, minute=0, second=0, microsecond=0)
range_end = now.replace(hour=0, minute=0, second=0, microsecond=0)
range_end -= timedelta(days=1)
range_start = range_end - timedelta(days=120)
range_start = range_start.replace(day=1)
s = AWSDetailedLineitem.get_instance_type(
[k.get_aws_user_id() for k in keys],
date_from=range_start,
date_to=range_end)
instance_type_hours = defaultdict(list)
first_hour = datetime(2099, 1, 1)
for r in s:
rhour = datetime.strptime(r['hour'], "%Y-%m-%dT%H:%M:%S")
if r['region'] != 'unknown': # Some EC2 instances have no region, sometimes...
instance_type_hours[(r['region'], r['instance'])].append(
(rhour, r['ridCount']))
first_hour = min(first_hour, rhour)
hours_ahead = 120 * 24
total_hours = (range_end -
first_hour).total_seconds() / 3600 - 1 + hours_ahead
instance_types = []
lookup = get_instance_lookup()
for (region, instance_type), hours in instance_type_hours.iteritems():
hours = count_forecast(hours, range_start, now, hours_ahead)
prices = lookup[region, instance_type]
price_results = get_monthly_prices(
total_hours, hours, [p['amortized'] for p in prices['reserved']],
prices['ondemand']['amortized'])
ps = []
for pricing, (_, count,
months) in zip(prices['reserved'] + [prices['ondemand']],
price_results):
pricing = dict(pricing)
if count is not None:
pricing['count'] = count
pricing['months'] = [
dict(month=m.strftime('%Y-%m'), cost=c) for m, c in months[:-1]
]
ps.append(pricing)
instance_types.append(
dict(region=region, type=instance_type, pricing_options=ps))
available_volumes = AWSStat.latest_available_volumes([k.key for k in keys])
now = datetime.utcnow()
date_from = now.replace(hour=0, minute=0, second=0,
microsecond=0) - relativedelta(months=6)
date_to = now.replace(hour=23, minute=59, second=59, microsecond=999999)
volume_monthly_costs = AWSDetailedLineitem.get_monthly_cost_by_resource(
available_volumes['volumes'] if 'volumes' in available_volumes else (),
date_from=date_from,
date_to=date_to)
resources = AWSMetric.underutilized_resources([k.key for k in keys])
rids = set(r['id'] for r in resources['resources'])
months = AWSDetailedLineitem.get_monthly_cost_by_resource(
rids, date_from=date_from, date_to=date_to)
reduced_instance_costs = {k: v * 0.2 for k, v in months.iteritems()}
return dict(
instances=instance_types,
volume_monthly_costs=volume_monthly_costs,
reduced_instance_costs=reduced_instance_costs,
)
