Predicting and proactively resolving service outages
Predicting or forecasting a service outage enables organizations to move from being reactive to being preventive. Being predictive means organizations can contain smaller issues from becoming larger problems, prevent service outages, take timely actions, and focus on key value drivers. Predictive features help organizations to maximize the IT performance and save the time and cost required to manage IT environments.
The AI-based service-centric prediction in BMC Helix AIOpsconsumes service health indicators and metrics from multicloud and hybrid IT environments. It has the capability to predict service outages and helps in identifying and fixing issues before they happen. As a result, it helps reduce an organization's overall mean time to resolve (MTTR).
The Predictions page provides a quick insights on forecasted service failure events. The prediction algorithm is capable of learning from past events to predict failures in the future. The algorithm uses the historical information to calculate a future service failure event based on the defined metric and identifies the threshold point at which the first impact will occur along with the predicted severity.
As time progresses, the algorithm continuously keeps learning and recalculates the status. Based on the recalculations, the availability status can change. This overriding of the previous status occurs at a pre-configured interval set within the algorithm.
Consider the following scenarios where you can use service predictions.
Jordan is a tenant administrator with APEX Global Finance, a financial services provider. The organization provides API services to multiple business consumers. APEX Global has an SLA of 99.9%, and if any of its API services goes down, it costs them a lot. Jordan thinks that if he can get predictive information about an API outage at least 30 minutes before it happens, he might be able to prevent, which in turn, will help his organization to meet the SLA.
Jordan is using BMC Helix AIOps service and situation monitoring features. Now Jordan can use the service prediction feature to list the services that might degrade in the near future. This information helps him maintain the system health more effectively.
Scott is a tenant administrator with APEX Global NMS, a network management service provider. The organization aims to provide the best wi-fi experience at all its client locations. Any degradation or drop in the wi-fi services decreases customer satisfaction and affects their clients' business.
Scott is using BMC Helix Operations Management for his IT operations management tasks. Additionally, he can use the service-centric predictions feature in BMC Helix AIOps, which gives him the ability to predict and prevent sensitive wi-fi service issues in advance and deliver maximum customer satisfaction.
APEX Global is a cell tower service provider. It wants to predict cell tower congestions in advance. In this day and age with billions of smart phone users and wireless broadband services running 24x7, APEX Global has the challenge of keeping the speed and reliability at their best, reducing congestion due to buffering and streaming, and minimizing call drops and other service impacts. Considering all these value drivers, APEX Global IT has decided to use BMC Helix AIOps, which has the capabilities to predict outages in real time and eventually save much needed time and money.
The following table lists the actions you must perform to enable and start monitoring service predictions in BMC Helix AIOps:
| Action | Reference |
|---|---|
Enable the service-centric predictions feature As a tenant administrator, select the AIOps Service Predictions option on the Manage Product Features page. | Enabling the AIOps features |
Monitor service predictions As an operator, monitor service predictions on the Service Predictions page and take timely actions to prevent any potential service outages. | Monitoring service predictions |
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