Skip to content
DataMiner Dojo

More results...

Generic selectors
Exact matches only
Search in title
Search in content
Post Type Selectors
Search in posts
Search in pages
Search in posts
Search in pages
Log in
Menu
  • Updates & Insights
  • Questions
  • Learning
    • E-learning Courses
    • Tutorials
    • Open Classroom Training
    • Certification
      • DataMiner Fundamentals
      • DataMiner Configurator
      • DataMiner Automation
      • Scripts & Connectors Developer: HTTP Basics
      • Scripts & Connectors Developer: SNMP Basics
      • Visual Overview – Level 1
      • Verify a certificate
    • YouTube Videos
    • Solutions & Use Cases
      • Solutions
      • Use Case Library
    • Agility
      • Learn more about Agile
        • Agile Webspace
        • Everything Agile
          • The Agile Manifesto
          • Best Practices
          • Retro Recipes
        • Methodologies
          • The Scrum Framework
          • Kanban
          • Extreme Programming
        • Roles
          • The Product Owner
          • The Agile Coach
          • The Quality & UX Coach (QX)
      • Book your Agile Fundamentals training
      • Book your Kanban workshop
    • >> Go to DataMiner Docs
  • DevOps
    • About the DevOps Program
    • Sign up for the DevOps Program
    • DataMiner DevOps Support
    • Feature Suggestions
  • Downloads
  • Swag Shop
  • PARTNERS
    • Business Partners
    • Technology Partners
  • Contact
    • Sales, Training & Certification
    • DataMiner Support
    • Global Feedback Survey
  • >> Go to dataminer.services

Correlating Generic Ping Alarms Based on Recurring 24-Minute Intervals

40 views6 hours agoCorelation genericping
2
Bina Raiker74 17 hours ago 0 Comments

Hi ,

We would appreciate your guidance on correlating Ping alarms generated by a Generic Ping element.

Scenario

The Generic Ping driver probes each endpoint during every polling cycle and generates an alarm when the probe reports 100% packet loss.

We are interested in detecting a recurring alarm pattern where, for a given IP address, Ping alarms occur at a characteristic cadence, with alarm start times approximately 24 minutes apart.

Requirement

For each monitored IP address, trigger a correlation event when:

  • There are at least two occurrences of Ping alarm pairs whose start times are approximately 24 minutes apart.
  • The alarms are generated for the same IP endpoint.
  • All matching alarms occur within a 60-minute rolling window.

Example

If Ping alarms for the same IP start at:

  • 10:00
  • 10:24
  • 10:48

then there are two occurrences of alarms approximately 24 minutes apart (10:00 → 10:24 and 10:24 → 10:48), and the correlation condition should be met.

Could you please advise whether this can be achieved:

  1. Directly through the Generic Ping connector configuration, or
  2. Using Event Correlation in DataMiner?

If Event Correlation is the recommended approach, could you provide guidance on how to configure a rule that evaluates alarm start times and identifies this recurring 24-minute cadence for the same IP address within a 60-minute period?

Thanks in advance

Bina Raiker

Paulo Henriques [SLC] [DevOps Advocate] Answered question 6 hours ago

1 Answer

  • Active
  • Voted
  • Newest
  • Oldest
0
Paulo Henriques [SLC] [DevOps Advocate]1.95K Posted 6 hours ago 0 Comments

Hi Bina,

1. Via the Generic Ping connector: no.

The connector has no cross-cycle timing logic. Each polling cycle it calculates Cycle Packet Loss (PID 1028) per row and the alarm is raised purely by the alarm template threshold on that parameter. There is no notion of "previous alarm start time" or inter-alarm spacing in the protocol, and adding it would mean a custom fork of the connector. So this is not something to solve at connector level.

2. Event Correlation is the right place, with one caveat.

Correlation gives you the two building blocks you need natively:

  • Alarm filter: limit to the Generic Ping element(s) and the Cycle Packet Loss / Ping Status parameter, severity Critical (or whatever severity the 100% loss maps to). Important: also enable "Trigger on single events. Don't maintain active tree status", otherwise repeated occurrences are treated as updates of the same alarm tree and are not counted separately.
  • Alarm grouping: add grouping "by table index". Each row in the ping table is one destination IP, so this gives you one independent evaluation per IP endpoint, which is exactly your "same IP" requirement.
  • Rule conditions: select "Require situation to occur 3 times in 60 minutes before acting".

That rule already covers your example (10:00, 10:24, 10:48 = 3 alarms for the same IP inside a 60-minute rolling window) and it is fully configurable in Cube, no code.

The caveat: correlation counts occurrences in a sliding window. It does not evaluate the delta between individual alarm start times, so it cannot on its own assert that the gaps are approximately 24 minutes rather than, say, 2 and 55 minutes. In practice 3 ping alarms per hour on one IP is usually already the signal you care about, so it is worth checking whether the plain count is good enough for you.

3. If the exact 24-minute cadence really matters

Extend the rule above with an Automation script action instead of (or in addition to) a correlated alarm. The correlation rule then acts as a cheap pre-filter, and the script does the precise check:

  1. The script receives the element ID, parameter ID and table index of the triggering alarm.
  2. It retrieves the alarm history for that element/parameter/index over the last 60 minutes.
  3. It computes the deltas between consecutive alarm start times and counts how many fall within 24 minutes plus/minus a tolerance (2-3 minutes is realistic, since the alarm start time is bound to the polling cycle, not to the actual event).
  4. If at least two such pairs are found, it raises an information event or an external alarm, sends a notification, sets a property, etc.

This keeps all the heavy lifting inside a script you fully control, while correlation handles the filtering, grouping per IP and the 60-minute window.

One suggestion before you build this: a 24-minute cadence that is that regular usually points at a deterministic cause rather than random loss, for example a route flap, a DHCP lease renewal, a firewall session timeout, or a scheduled job on the path. It may be worth correlating the ping alarm start times with the polling interval configured on the element and with logs on the network side. If the cadence turns out to be a fixed multiple of the polling interval, the pattern may partly be an artefact of how the element polls rather than of the endpoint itself.

For the actual implementation, in particular the custom Automation script in option 3, we would advise you to reach out to your Technical Account Manager or to the deployment squad responsible for your project. They have the context on your setup and can pick this up as part of the project scope.

Paulo Henriques [SLC] [DevOps Advocate] Answered question 6 hours ago
Please login to be able to comment or post an answer.

My DevOps rank

DevOps Members get more insights on their profile page.

My user earnings

0 Dojo credits

Spend your credits in our swag shop.

0 Reputation points

Boost your reputation, climb the leaderboard.

Promo banner DataMiner DevOps Professiona Program
DataMiner Integration Studio (DIS)
Empower Katas
Privacy Policy • Terms & Conditions • Contact

© 2026 Skyline Communications. All rights reserved.

DOJO Q&A widget

Can't find what you need?

? Explore the Q&A DataMiner Docs

[ Placeholder content for popup link ] WordPress Download Manager - Best Download Management Plugin