vnfr["vim-account-id"] = pla_vnf["vimAccountId"]
return modified
+ def _gather_vnfr_healing_alerts(self, vnfr, vnfd):
+ alerts = []
+ nsr_id = vnfr["nsr-id-ref"]
+ df = vnfd.get("df", [{}])[0]
+ # Checking for auto-healing configuration
+ if "healing-aspect" in df:
+ healing_aspects = df["healing-aspect"]
+ for healing in healing_aspects:
+ for healing_policy in healing.get("healing-policy", ()):
+ vdu_id = healing_policy["vdu-id"]
+ vdur = next(
+ (vdur for vdur in vnfr["vdur"] if vdu_id == vdur["vdu-id-ref"]),
+ {},
+ )
+ if not vdur:
+ continue
+ metric_name = "vm_status"
+ vdu_name = vdur.get("name")
+ vnf_member_index = vnfr["member-vnf-index-ref"]
+ uuid = str(uuid4())
+ name = f"healing_{uuid}"
+ action = healing_policy
+ # action_on_recovery = healing.get("action-on-recovery")
+ # cooldown_time = healing.get("cooldown-time")
+ # day1 = healing.get("day1")
+ alert = {
+ "uuid": uuid,
+ "name": name,
+ "metric": metric_name,
+ "tags": {
+ "ns_id": nsr_id,
+ "vnf_member_index": vnf_member_index,
+ "vdu_name": vdu_name,
+ },
+ "alarm_status": "ok",
+ "action_type": "healing",
+ "action": action,
+ }
+ alerts.append(alert)
+ return alerts
+
+ def _gather_vnfr_scaling_alerts(self, vnfr, vnfd):
+ alerts = []
+ nsr_id = vnfr["nsr-id-ref"]
+ df = vnfd.get("df", [{}])[0]
+ # Checking for auto-scaling configuration
+ if "scaling-aspect" in df:
+ rel_operation_types = {
+ "GE": ">=",
+ "LE": "<=",
+ "GT": ">",
+ "LT": "<",
+ "EQ": "==",
+ "NE": "!=",
+ }
+ scaling_aspects = df["scaling-aspect"]
+ all_vnfd_monitoring_params = {}
+ for ivld in vnfd.get("int-virtual-link-desc", ()):
+ for mp in ivld.get("monitoring-parameters", ()):
+ all_vnfd_monitoring_params[mp.get("id")] = mp
+ for vdu in vnfd.get("vdu", ()):
+ for mp in vdu.get("monitoring-parameter", ()):
+ all_vnfd_monitoring_params[mp.get("id")] = mp
+ for df in vnfd.get("df", ()):
+ for mp in df.get("monitoring-parameter", ()):
+ all_vnfd_monitoring_params[mp.get("id")] = mp
+ for scaling_aspect in scaling_aspects:
+ scaling_group_name = scaling_aspect.get("name", "")
+ # Get monitored VDUs
+ all_monitored_vdus = set()
+ for delta in scaling_aspect.get("aspect-delta-details", {}).get(
+ "deltas", ()
+ ):
+ for vdu_delta in delta.get("vdu-delta", ()):
+ all_monitored_vdus.add(vdu_delta.get("id"))
+ monitored_vdurs = list(
+ filter(
+ lambda vdur: vdur["vdu-id-ref"] in all_monitored_vdus,
+ vnfr["vdur"],
+ )
+ )
+ if not monitored_vdurs:
+ self.logger.error(
+ "Scaling criteria is referring to a vnf-monitoring-param that does not contain a reference to a vdu or vnf metric"
+ )
+ continue
+ for scaling_policy in scaling_aspect.get("scaling-policy", ()):
+ if scaling_policy["scaling-type"] != "automatic":
+ continue
+ threshold_time = scaling_policy.get("threshold-time", "1")
+ cooldown_time = scaling_policy.get("cooldown-time", "0")
+ for scaling_criteria in scaling_policy["scaling-criteria"]:
+ monitoring_param_ref = scaling_criteria.get(
+ "vnf-monitoring-param-ref"
+ )
+ vnf_monitoring_param = all_vnfd_monitoring_params[
+ monitoring_param_ref
+ ]
+ for vdur in monitored_vdurs:
+ vdu_id = vdur["vdu-id-ref"]
+ metric_name = vnf_monitoring_param.get("performance-metric")
+ vnf_member_index = vnfr["member-vnf-index-ref"]
+ scalein_threshold = scaling_criteria.get(
+ "scale-in-threshold"
+ )
+ scaleout_threshold = scaling_criteria.get(
+ "scale-out-threshold"
+ )
+ # Looking for min/max-number-of-instances
+ instances_min_number = 1
+ instances_max_number = 1
+ vdu_profile = df["vdu-profile"]
+ if vdu_profile:
+ profile = next(
+ item for item in vdu_profile if item["id"] == vdu_id
+ )
+ instances_min_number = profile.get(
+ "min-number-of-instances", 1
+ )
+ instances_max_number = profile.get(
+ "max-number-of-instances", 1
+ )
+
+ if scalein_threshold:
+ uuid = str(uuid4())
+ name = f"scalein_{uuid}"
+ operation = scaling_criteria[
+ "scale-in-relational-operation"
+ ]
+ rel_operator = rel_operation_types.get(operation, "<=")
+ metric_selector = f'{metric_name}{{ns_id="{nsr_id}", vnf_member_index="{vnf_member_index}", vdu_id="{vdu_id}"}}'
+ expression = f"(count ({metric_selector}) > {instances_min_number}) and (avg({metric_selector}) {rel_operator} {scalein_threshold})"
+ labels = {
+ "ns_id": nsr_id,
+ "vnf_member_index": vnf_member_index,
+ "vdu_id": vdu_id,
+ }
+ prom_cfg = {
+ "alert": name,
+ "expr": expression,
+ "for": str(threshold_time) + "m",
+ "labels": labels,
+ }
+ action = scaling_policy
+ action = {
+ "scaling-group": scaling_group_name,
+ "cooldown-time": cooldown_time,
+ }
+ alert = {
+ "uuid": uuid,
+ "name": name,
+ "metric": metric_name,
+ "tags": {
+ "ns_id": nsr_id,
+ "vnf_member_index": vnf_member_index,
+ "vdu_id": vdu_id,
+ },
+ "alarm_status": "ok",
+ "action_type": "scale_in",
+ "action": action,
+ "prometheus_config": prom_cfg,
+ }
+ alerts.append(alert)
+
+ if scaleout_threshold:
+ uuid = str(uuid4())
+ name = f"scaleout_{uuid}"
+ operation = scaling_criteria[
+ "scale-out-relational-operation"
+ ]
+ rel_operator = rel_operation_types.get(operation, "<=")
+ metric_selector = f'{metric_name}{{ns_id="{nsr_id}", vnf_member_index="{vnf_member_index}", vdu_id="{vdu_id}"}}'
+ expression = f"(count ({metric_selector}) < {instances_max_number}) and (avg({metric_selector}) {rel_operator} {scaleout_threshold})"
+ labels = {
+ "ns_id": nsr_id,
+ "vnf_member_index": vnf_member_index,
+ "vdu_id": vdu_id,
+ }
+ prom_cfg = {
+ "alert": name,
+ "expr": expression,
+ "for": str(threshold_time) + "m",
+ "labels": labels,
+ }
+ action = scaling_policy
+ action = {
+ "scaling-group": scaling_group_name,
+ "cooldown-time": cooldown_time,
+ }
+ alert = {
+ "uuid": uuid,
+ "name": name,
+ "metric": metric_name,
+ "tags": {
+ "ns_id": nsr_id,
+ "vnf_member_index": vnf_member_index,
+ "vdu_id": vdu_id,
+ },
+ "alarm_status": "ok",
+ "action_type": "scale_out",
+ "action": action,
+ "prometheus_config": prom_cfg,
+ }
+ alerts.append(alert)
+ return alerts
+
def update_nsrs_with_pla_result(self, params):
try:
nslcmop_id = deep_get(params, ("placement", "nslcmopId"))
db_nsr_update["detailed-status"] = "Done"
db_nslcmop_update["detailed-status"] = "Done"
nslcmop_operation_state = "COMPLETED"
+ # Gather auto-healing and auto-scaling alerts for each vnfr
+ healing_alerts = []
+ scaling_alerts = []
+ for vnfr in self.db.get_list("vnfrs", {"nsr-id-ref": nsr_id}):
+ vnfd = next(
+ (sub for sub in db_vnfds if sub["_id"] == vnfr["vnfd-id"]), None
+ )
+ healing_alerts = self._gather_vnfr_healing_alerts(vnfr, vnfd)
+ for alert in healing_alerts:
+ self.logger.info(f"Storing healing alert in MongoDB: {alert}")
+ self.db.create("alerts", alert)
+
+ scaling_alerts = self._gather_vnfr_scaling_alerts(vnfr, vnfd)
+ for alert in scaling_alerts:
+ self.logger.info(f"Storing scaling alert in MongoDB: {alert}")
+ self.db.create("alerts", alert)
if db_nsr:
self._write_ns_status(
self.logger.error(
logging_text + "kafka_write notification Exception {}".format(e)
)
+ self.logger.debug(f"Deleting alerts: ns_id={nsr_id}")
+ self.db.del_list("alerts", {"tags.ns_id": nsr_id})
self.logger.debug(logging_text + "Exit")
self.lcm_tasks.remove("ns", nsr_id, nslcmop_id, "ns_terminate")
projects / osm / LCM.git / blobdiff