Digital Operational Intelligence: A Fresh Take on Autonomic Computing

Digital Operational Intelligence: A Fresh Take on Autonomic Computing

Digital Operational Intelligence combines the analysis of application, infrastructure and network operations data to optimize user experience, accelerate

Published By - WisdomPlexus

Autonomic computing is a self-managing computing model named after, and patterned on, the human body’s autonomic nervous system. Digital Operational Intelligence combines the analysis of application, infrastructure and network operations data to optimize user experience, accelerate root cause analysis, prevent downtime, and optimize operational efficiency.

The solution leverages CA’s expertise at all levels of the application stack to provide operational transparency based on the analysis of performance metrics, logs, alarms, and topology mappings. Bringing together these different types of operation data from across the entire stack constitutes the optimal basis for applying machine learning and ultimately artificial intelligence algorithms to plan, prioritize, and automate IT operations tasks.

AI and Machine Learning for Autonomic Computing
Based on its comprehensive foundation of application and infrastructure data, the solution automatically creates topology maps of the corporate IT infrastructure and business services by analyzing inventory and relationships from multiple domain-specific monitoring tools. It then applies machine learning (ML) algorithms to these topology maps to provide the customer-specific context and conduct its intelligent trend and root cause analysis. The resulting understanding of infrastructure dependencies is the basis for risk evaluation, issue prioritization, and root cause analytics.

The ability to analyze any type of operations data from across the entire application stack, including hybrid cloud services, enables the solution to radically reduce complexity for IT operators. Like any standard big data challenge, machine learning algorithms dramatically benefit from the availability of large bodies of diverse contextual data. The availability of server, storage, network, OS, middleware, and application performance and reliability data, all from one data lake, creates a great foundation for contextual prioritization, consolidation, and routing of issues, based on their root cause and importance to the business.

The CA solution automatically selects the appropriate machine learning algorithms through continuous data preprocessing, based on ongoing operational feedback across the stack. This combination of feedback learning and topology knowledge helps the CA solution efficiently provide specific and actionable insights and recommendations, as the precondition for autonomic issue remediation and resolution.

You may also like to Read:
The Definitive Guide to AIOps

Download the complete Resource:

* All information that you provide is protected by our privacy policy.