By use case

Solutions for AIOPs

Nastel’s i2M platform delivers AIOPs

 

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AIOPs

Nastel’s i2M (Integration Infrastructure Management) platform delivers a complete AIOPs solution that capitalizes on your existing messaging middleware environment.

 

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Ingestion

Getting the data into single place where it can be analyzed.
Source
  • Messaging Middleware Platforms
  • Log Files
  • Data Streams
  • Any form of Machine Data
Solution

Nastel provides a broad and deep range of methods of ingesting data.

We maintain a GitHub repository of open-sourced, supported connectors that use many methods of connecting to machine data sources. We make use of both agents and agentless methods to fit all needs.

 

Infrastructure, platforms and applications across datacenters and clouds can be ingested.

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Topology

Presenting a model of how the data flows through the entire system, how it all connects.

Each form of messaging middleware provides a map of the relationship between the different components of the application stack. By following the message pathway each “transaction” takes through the environment, Nastel AIOPs can present the users experience as a topology.

Correlation

Identifying signals in the data and understanding what they mean in the context of the business.

Nastel AIOPs makes use of Machine Learning Artificial Intelligence (ML AI) to compare the topology of a users experience to the historical record of similar requests, to identify the subtle, early indicators of a performance anomaly.

Nastel® Technologies

Machine Learning

Nastel AIOPs makes use of Machine Learning Artificial Intelligence (ML AI) to compare the topology of a users experience to the historical record of similar requests, to identify the subtle, early indicators of a performance anomaly.

Remediation

Understanding what actions must be taken to fix issues, and how these can be automated.

Using a mix of various machine learning artificial intelligence (ML AL) algorithms, Nastel AIOPs can identify the signals associated with complex interactions that indicate a deviance from normal behavior, while also identifying and ignoring false positives. These signals can then be used to alert operators or initiate automated processes to mitigate the potential impact of an event.

 

Actions taken once to remediate a predicted issue, can then be included in future automations driven by ML AI, as the system learns and improves.

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