Front-loading with OSS auto-discovery

Yesterday’s post discussed the merits of front-loading effort on knowledge transfer of new starters and automated testing, whilst acknowledging the challenges that often prevent that from happening.

Today we look at the front-loading benefits of building OSS / network auto-discovery tools.

We all know that OSS are only as good as the data we seed them with. As the old saying goes, garbage in, garbage out.

Assurance / network-health data is generally collected directly from the network in near real time, typically using SNMP traps, syslog events and similar. The network is generally the data master for this assurance-style data, so it makes sense to pull data from the network wherever possible (ie bottom-up data flows).

Fulfilment data, in the form of customer orders, network designs, etc are often captured in external systems first (ie as master) and pushed into the network as configurations (ie top-down data flows).
Bottom-up and top-down OSS data flows

These two flows meet in the middle, as they both tend to rely on network inventory and/or resources. Bottom-up – Network faults to be tied to inventory / resource / topology information (eg fibre cuts, port failure, device failure, etc) which are important for fault identification (Root Cause Analysis [RCA]). Similarly for top-down – customer services / circuits / contracts tend to consume inventory, capacity and/or other resources.

Looking end-to-end and correlating network health (assurance) to customer service health (fulfilment) (eg as Service Level Agreement [SLA] analysis, Service Impact Analysis [SIA]) tends to only be possible due to reconciliation via inventory / resource data sets as linking keys.

Seeding an OSS with inventory / resource data can be done via three methods:

  1. Data migration (eg script-loading from external sources such as spreadsheet or CSV files)
  2. Manual data creation
  3. Auto-discovery (ie collection of data directly from the network)
  4. (or a combination of the above)

Options 1 and 2 are probably the more traditional method of initial seeding of OSS databases, mainly because they tend to be faster to demonstrate progress.

Option 3 is the front-loading option that can be challenging in the short-to-medium term but will hopefully prove beneficial in the longer term (just like knowledge transfer and automated testing).

It might seem easy to just suck data directly out of the network, but the devil is definitely in the detail, details such as:

  • Choosing optimal timings to poll the network without saturating it (if notification aren’t being pushed by the network), not to mention session handling of these long-running data transfers
  • Building the mediation layer to perform protocol conversion and data mappings
  • Field translation / mapping to common naming standards. This can be much more difficult than it sounds and is key to allow the assurance and fulfilment meet-in-the-middle correlations to occur (as described above)
  • Reconciliation between the data being presented by the network and what’s already in the OSS database. In theory, the data presented by the network should always be right, but there are scenarios where it’s not (eg flapping ports giving the appearance of assets being present / absent from network audit data, assets in test / maintenance mode that aren’t intended be accepted into the OSS inventory pool, lifecycle transitions from planned to built, etc)
  • Discrepancy and exception handling rules
  • All of the above make it challenging for “siloed” data sets, but perhaps even more challenging is in the discovery and auto-stitching of cross-domain data sets (eg cross-domain circuits / services / resource chains)

The vexing question arises – do you front-load and seed via auto-discovery or perform a creation / migration that requires more manual intervention? In some cases, it could even be a combination of both as some domains are easier to auto-discover than others.

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