Integration with sdk

Data that are generated from the tng-sdk-benchmark can be of a Type:

• container specific such as: cpu_usage_total_usage (common in all experiments)
• system under test specific such as: cpu_cores, mem_max (is this common in all experiments ??)
• network service specific such as: TO BE COMPLETED (can we have an example here?)

Metrics naming

Taking under consideration Prometheus best practices for naming:
https://prometheus.io/docs/practices/naming/

container name	metric name	dimensions
cname	metricname	ns_id & experiment_id

eg. mn_mp_output_vdu01_cpu_stats__online_cpus_int {ns_id:"ns-1vnf-ids-suricata",experiment_id:"suricata_performance"}

note: if we will use Prometheus putting the ns_id and experiment_id as a dimension helps to easily fetching all timeseries data for a specific network services and/or experiment. Otherwise can be part of the the name
eg. mn_mp_output_vdu01_cpu_stats__online_cpus_int_ns-1vnf-ids-suricata_suricata_performance

Profiling

End user just has to provide the time period (start_timestap/end_timestap) and the metrics list he wants to be further analyzed.

• Metrics can be specific (JSONArray):
  [mn_mp_output_vdu01_cpu_stats__online_cpus_int {ns_id:"ns-1vnf-ids-suricata",experiment_id:"suricata_performance", mn_mp_output_vdu01_networks__eth0__rx_bytes {ns_id:"ns-1vnf-ids-suricata",experiment_id:"suricata_performance"]}
• Metrics can be fetched per network service and/or experiment_id:
  • [{ns_id:"ns-1vnf-ids-suricata"}] gets all metrics for the specific network (and all experiments it has participate during the given time period)
  • [{ns_id:"ns-1vnf-ids-suricata",experiment_id:"suricata_performance"}] gets all metrics for the specific network and only for the specific experiment
  • [{ns_id:"ns-1vnf-ids-suricata",experiment_id:"suricata_performance000"},{ns_id:"ns-1vnf-ids-suricata",experiment_id:"suricata_performance001"}] gets all metrics for the specific network and for two different experiments

Per experiment

Profiling can be done in an experiment basis:

timestamp	m1	m2	m3
t1	value1	value2	value3

A possible tabular data format example could be: ADD CSV EXAMPLE

Per experiments

In case we have to run a profiling analysis upon data that come from different experiments we can only get metrics that are common in all experiments

Experiment 1:

timestamp	m1	m2	m3
t1	value11	value12	value13
t2	value21	value22	value23
t3	value31	value32	value33

Experiment 2:

timestamp	m1	m3	m4
t4	value41	value43	value44
t5	value51	value53	value54
t6	value61	value63	value64

Result Dataset to be analyzed:

timestamp	m1	m3
t1	value11	value13
t2	value21	value23
t3	value31	value33
t4	value41	value43
t5	value51	value53
t6	value61	value63