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Status Not under consideration
Workspace IBM Cloud Pak for AIOps
Created by Guest
Created on Apr 7, 2017

RELATED IDEAS

Improving of prediction taking into account the hierarchy of the network

See this idea on ideas.ibm.com

It is necessary for PI to take into account the hierarchy of the network in order to provide more accurate information about relationships between metrics.

Currently, PI prediction is based only on mathematical model and doesn't use topological relationships which cause that PI gives some metrics as related to each other, which can't be related, as they are from different Cells, that have different parent BSCs, from different cities, different domains or even technologies...

Some of the suggested method of supplying relationship information could be:
1. Manual entry of rules to force a relation to form or to NOT form and thus avoid linking metrics from a variety of resources that are not related to each other.
2.Manually add rules that will calculate the distance between entities by having information about entity's position and thus prevent the linking of metrics that originate from entities that are very distant and not related.
3.Import a network topology and then only allow causal relationships between items that have the same parent, grand parent, grand grand parent etc.

Idea priority High
RFE ID 103529
RFE URL
RFE Product IBM Operations Analytics - Predictive Insights
  • Admin
    STEPHEN Hussey
    Reply
    |     May 16, 2022

    Although valid, it is not anticipated that this enhancement will be deliverd in product in the next year of development.

  • Guest
    Reply
    |     Apr 12, 2019

    Due to processing by IBM, this request was reassigned to have the following updated attributes:
    Brand - Cloud
    Product family - Operations Management
    Product - IBM Operations Analytics - Predictive Insights

    For recording keeping, the previous attributes were:
    Brand - WebSphere
    Product family - IT Service Management
    Product - IBM Operations Analytics - Predictive Insights

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