AIOps for Cloud Intelligence Systems

Simply put, today’s customers want—and need—AI-driven multi-cloud operations for the vital purposes of monitoring, detecting, and preventing costly service disruptions. The purpose and value of AI for IT operations (AIOps) in cloud systems are to gather reliable operational insights, enhance and fortify human judgments with AI-enriched actionable information, and all in all, support positive business outcomes.

The AIOps mission is to prevent disruption and catastrophe of countless types and to avert their wasteful, discouraging consequences.

Fortunately, today’s enterprise CIOs now have the innovation and resources to leverage multi-cloud and DevOps trends with AI/ML to automate operations and provide real-time visibility. At last, IT teams can take preemptive action to anticipate and solve Cloud/DevOps and IT issues before they occur.

Introduction to AIOps for Cloud Computing

Forrester defines AIOps as “A practice that combines human and technological application of AI/ML, advanced analytics, and operational practices to business and operations data.”

AIOps supports and sharpens human judgment, proactively alerts on known scenarios and challenges, predicts likely events, recommends corrective actions, and gives the team the instruments to take advantage of automated remediation. Sensory data is transferred into AI-enriched actionable information, driving better governance, foundational improvements, and trust.

The Significance of AIOps in Modern Cloud Environments

Today’s AIOps leverages machine learning and artificial intelligence algorithms to automate and streamline IT operations, operating cloud services, and ease monitoring, management, and optimization. AIOps reduces outages and downtime conserves revenue, and supports the brand through customer loyalty, trust, and satisfaction.

What makes AIOps so ideally suited for digital transformation in cloud environments? For one, scale. Today’s IT infrastructures are often distributed across multiple cloud providers and data centers. They encompass thousands—or millions—of devices and applications. With AIOps, IT gains the ability to process and analyze massive volumes of data in real time, identifying patterns and anomalies that humans using manual techniques would miss.

Another advantage of AIOps in the cloud is the ability to respond rapidly to meet customer expectations and proactively prevent downtime, preventing loss before the threat is even visible.  Additionally, AIOps can reduce costs by automating repetitive tasks, improving efficiency, and shrinking the risk of downtime.

Challenges Addressed by AIOps in Cloud Systems

Complexity is accelerating as organizations adopt cloud-native technologies. Companies entering a multicloud model introduce more data and systems to track and a complex span of systems to manage. Nevertheless, many organizations still continue to rely on statistical correlation to detect performance or security problems.

This approach demands a lot of manual work to identify exactly where and why a problem arises. It can’t stay apace with cloud-native environments and automate DevOps processes. According to Dynatrace, 63% of CIOs say their cloud environments have already surpassed a human’s ability to manage.

How AIOps Offers Solutions to These Challenges

Microsoft points out that using AIOps makes cloud systems more autonomous and minimizes human operations and rule-based decisions. Automating DevOps reduces the impact of system issues on users, helps teams make better decisions earlier in the development process, and reduces maintenance costs across build, deployment, monitoring, and diagnosis.

Knowing future status enables teams to proactively dodge negative system impacts, such as migrating services from an unhealthy computing node to a healthy one.  Most encouraging, AI/ML technology can enable systems to learn dynamically the best decisions to make.

AIOps makes cloud systems more manageable by introducing the notion of tiered autonomy. Each tier represents a set of operations that require a certain level of human expertise and human intervention—from the top tier of autonomous routine operations to the bottom tier, which requires deep human expertise.

Additional Resources on AIOps

AIOps Strategy for Cloud Systems

Vision for Enhancing Cloud System Autonomy

One challenge in reaching an autonomous cloud lies in the heterogeneity of cloud data. Cloud platforms deploy a huge number of monitors to collect highly diverse data in various formats, and are subjected to changes over time. To ensure that adopted AIOps solutions can function autonomously in this environment, the operations management system needs robust, extendable AI/ML models capable of learning useful information from heterogeneous data sources and drawing accurate conclusions.

Developing Proactive Management Tools

In an era of big data and complex IT environments, an AIOps platform can efficiently process and correlate vast datasets automatically, so organizations can navigate intricate networks and diverse data sources for a more streamlined and effective approach to IT management. Choosing the right AIOps tools involves measuring complex IT environments, and ensuring your AIOps tool aligns with systems, processes, and overall operational structure, network size, and complexity. Assess the tool’s ability to integrate with diverse data sources for accurate insights and predictions. And look for robust ML capabilities for advanced analysis, anomaly detection, and predictive insights. Ensure the tool aligns with your budget, and evaluate its collaboration and communication features to enable seamless interaction and coordination among IT teams.

 Advancing System Manageability

If your organization is exploring how to leverage machine learning and AIOps, here are capabilities to consider:

  1. Dependency mapping across numerous domains to take information about systems, applications, and services;
  2. Event and incident management to take advantage of AIOps’ ability to ingest, analyze, and manage monitoring data from different sources with depth and speed-to-insight;
  3. Predictive maintenance and capacity management from helping IT organizations take preemptive action;
  4. Automated remediation uses AIOps self-healing capabilities to drive the resolution of both current incidents and predicted problems;
  5. Managing IoT devices: David Linthicum, chief cloud strategy officer for Deloitte Consulting, says the complexity and volume of these devices make management nearly impossible without AIOps.

AIOps in cloud systems

Comprehensive AIOps Integration Across the Cloud Stack

Security Enhancements Through AI

AIOps has a myriad of use cases in the cloud to enhance operations and security. These include Threat Intelligence, Incident Response and Management, Behavioral Analysis, Fraud Detection and Malware Detection, and Data Classification and Monitoring. AIOps analyzes and monitors structured and unstructured data stored in all cloud environments—public, private, or hybrid cloud.

Compliance and Regulatory Considerations

Most compliance violations are a matter of human error—but AIOps simplifies regulations for ITOps teams with no legal background. Advanced Natural Language Processing enables breaking down complex regulations into easily understandable guidelines that can be used to train IT and operations teams at all levels. An AIOps-powered automation engine can enforce region-specific compliance rules when connected with log and trace analytics. This surpasses traditional tools relying on access controls or approvals.

Finally, AIOps brings a new approach to regulatory compliance through core components that enable the capture of data flow between all microservices and independent components.

Innovations and Advanced Techniques in AIOps

Utilizing Predictive Analytics for Preventive Maintenance

AIOps platforms analyze patterns and trends in the data set to find abnormalities and outliers, as well as offering best practices on handling a situation. This ability to identify and address anomalies quickly lessens the likelihood of IT outages.

By predicting resource usage, predictive analytics enables superior performance monitoring, assisting in predicting ticket volumes, spikes, and resource usage levels. AIOps helps teams be proactive in preventing IT downtime—estimating issue volume and usage patterns.

AIOps automates and speeds anomaly detection, predicts network outages, and helps resolve capacity problems by examining previous consumption trends of infrastructure resources, such as CPU, memory, etc.

Eventually, AI-powered predictive analytics will identify potential hazards and weaknesses in the IT infrastructure. Machine learning examines data across numerous dimensions in real time to determine typical application behavior and notifies IT staff of unexpected patterns.

Machine Learning Models for Real-Time Decision Making

Machine learning models must be able to execute requests on-the-fly and perform to expectations. Machine learning models must also be constantly monitored and updated for accuracy and reliability because data changes—new patterns emerge, and models must be adapted and fine-tuned to retain their accuracy and reflect changing dynamics.

Artificial Intelligence Tools for Incident Response and Resource Optimization

Besides machine learning for threat detection, incident response embraces event correlation analysis, improved capacity planning and optimization, proactive and intelligence for IT operations health checks, and predictive analytics for IT operations. AIOps assists in prioritizing alerts and events based on their potential impact on IT operations.

To minimize alert fatigue, AIOps can consider context and dependencies to identify the most critical and immediate issues.

AIOps analyzes root causes by tracing cause-and-effect relationships between events to streamline troubleshooting and speed incident resolution by identifying underlying issues. This supports IT operations efficiency and system reliability by automating event correlation and providing actionable insights.

Future Trends and Developments in AIOps Technology

AIOps is vital—not just for IT optimization but to ensure the integrity, stability and transparency of Cloud and IT operations themselves. Extended to service management, performance management, data analytics, and automation, AIOps can revolutionize Cloud and IT operations across infrastructure systems, storage, networks, and services/applications.

As the way forward, AIOps combines algorithmic and human intelligence to provide full visibility into the state and performance of the IT systems that businesses rely on. Cloud adoption, rapid change, and the implementation of new technologies require a shift in focus to applications and developers, an increased pace of innovation and deployment, and the acquisition of new digital users.

AIOps can help overcome the challenges of managed cloud, unmanaged cloud, third-party services, SaaS integrations, mobile, and more. Manually tracking and managing this complex, dynamic, and elastic environment is not even possible let alone efficient. Plus, this will only intensify in years to come.

Conclusion

AIOps uses advanced analytics and machine learning algorithms to analyze vast amounts of data generated by IT systems in real-time—data from logs, performance, metrics, events, and alerts—to indicate potential issues before they occur.

This helps IT teams proactively address issues before they impact system availability or performance.

One of the key benefits of AIOps is that it helps IT teams better manage the complexity of modern IT systems. With so many different systems, applications, and services running in today’s IT environments, it can be difficult to keep track of everything.

AIOps provides a unified view of all the data generated by these systems, making it easier for IT teams to identify and resolve issues quickly. Advantages include:

  • AIOps reduces the amount of time it takes to respond
  • AIOps never stops learning
  • AIOps allows teams to focus on strategic areas
  • AIOps tools can set dynamic thresholds
  • AIOps tools can continuously monitor IT infrastructure and applications
  • AIOps technologies can combine data from several sources

To experience the power of Aisera’s AIOps platform for AI observability and dynamic CMDB book a custom AI demo today!