Netka AIOps Director
The Ultimate AIOps Solution
overview

Netka AIOps Director

What is Netka AIOps Director?

Netka AIOps Director or N-AIOps is AIOps Platform which provides data ingestion, data analytics by using AI technologies and intelligently drive automation

Overview

Netka AIOps Director or N-AIOps is AIOps Platform which provides data ingestion, data analytics by using AI technologies and intelligently drive automation. N-AIOps have workflow designer which is tool for creating automation process that can flexibly design workflow with complex conditions. N-AIOps is platform which require data from IT management systems e.g. ITIM, ITSM, NPMD, SIEM, APM, DEM for cross-domain analysis and drive automation. N-AIOps supports data for processing as follow:

  1. Log data e.g. Syslog, SNMP Trap, Windows event
  2. Telemetry data e.g. metrics, traces
  3. Network data e.g. packet analysis data, flow analysis data, topology, inventory
  4. ITSM data e.g. incidents, changes, problems, Cis
  5. IoT data or sensor values g. temperature, humidity, AC/DC voltage, current, watt, relay, contact, access door’s status
overview
usecase
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Netka AIOps Director, the ultimate AIOps solution

N-AIOps can work with 3rd party application which send data with Syslog, SNMP Trap or JSON format and work seamlessly with Netka products including

  1. NetkaView Network Manager or NNM support the following data
    • Log data: Syslog, SNMP Trap, Windows event
    • Network data: flow analysis, topology, inventory
    • Metrics data: cpu utilization, memory utilization, disk usage, traffic utilization, error rate, discard rate, crc, rtt, packet loss, jitter, latency, MOS, ICPIF, power loss
  2. NetkaQuartz Service Desk or NSD support ITSM data e.g. incidents, changes, problems, CIs
  3. NetkaView Logger or NLG support the following data
    • Log data: Syslog, SNMP Trap, Windows event, application log, firewall/IPS log, motion detection log & video
    • Network data: packet analysis data, flow analysis data, topology, inventory
    • Metrics data: cpu utilization, memory utilization, disk usage, traffic utilization, error rate, discard rate, crc, rtt, packet loss, Quality of Experience, hop-by-hop latency, end-to-end latency
    • Traces data: span data (operation name, start/finish timestamp) which NLG will support Application Performance Monitoring for distributed tracing in 2Q21
  4. NetkaView IoT or NIoT support IoT data e.g. temperature, humidity, AC/DC voltage, current, watt, relay, contact, access door’s status

The Netka AIOps’s life cycle

When N-AIOps works with NNM, NSD, NLG, NIoT, this will be “The ultimate AIOps Solution” which provides cross-domain analysis for IT Infrastructure Management, IT Service Management, Network Performance Monitoring and Diagnostics, Security Information and Event Management, Application Performance Monitoring and Digital Experience Monitoring. The Netka AIOps’s life cycle consists of 5A as follow:

  1. Acquire is stage for collecting data from IT components by NNM, NSD, NLG, NIoT or 3rd party application with polling, push, discovery, manually input for logs, metrics, traces, network, ITSM and IoT data.
  2. Aggregate is stage for ingesting data from multiple sources into the platform. Then the platform will normalize data and store in data lake.
  3. Analyze is stage for analyzing by using technologies including correlation, pattern recognition, root cause analysis, anomaly detection, threat detection, incident detection, predictive analytics, topological data analysis, facial recognition.
  4. Advise is stage for suggesting prescriptive including enrichment (incident category/impact/urgency/ priority, recommended engineer/team), suggestion (which event must be act or not, probable cause, and recommended actions to resolve)
  5. Act is stage for driving automation by accessing to CIs including network device, server OS, application and run playbook for Self-Diagnostic, Self-Healing, Self-Recovery and Self-Prevention which is enhanced with workflow designer that user can create process to drive automated tasks according to predefined conditions.
solutions

Sample use cases of N-AIOps

N-AIOps can start workflow and run playbook into CIs when incoming log matched predefined rule. Also, N-AIOps can detect anomaly and finding root cause from cross-domain analysis. For example, when user have bad experience from using an application because of slowness and disconnections. This kind of problem requires cross-domain analysis based on variety of data e.g.  traffic utilization, cpu utilization, memory utilization, disk usage, network quality (latency, jitter, packet loss), hop-by-hop latency, end-to-end latency, trace/span data.

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