AIOps-Powered Monitoring and Observability

Data analytics, automation and correlation technologies embedded in eG Enterprise ensure that it is easy to use and is yet effective in proactively detecting and alerting on problems and provides accurate diagnosis.

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Manual analysis of alerts or setting up of if-then-rules for IT operations no longer works for today's dynamic IT infrastructures. AIOps technologies are required to power modern monitoring tools and make them simple, automated and effective.

eG Enterprise leverages modern industry standard machine learning technologies to ensure full observability of IT application and infrastructure stacks (on-prem, cloud, SaaS) at scale. Adopting a modern AIOps powered monitoring platform can help your business scale and automate processes to ensure quality and cost-efficiency whilst allowing you to adopt industry wide criteria and standards for anomaly detection and customer SLAs.

Why traditional IT monitoring is insufficient at scale?

  • Numerous false alerts
  • Tedious threshold setting
  • Doesn't hand time-varying norms
  • Reactive, not proactive

What is AIOps?

AIOps (Artificial Intelligence for IT Operations) is a term coined by Gartner as an industry category for machine learning analytics technologies that enhances IT operations analytics covering operational tasks include automation, performance monitoring and event correlations, among others.

Continual intelligent monitoring at scale allows IT admins:

  • Get actionable alerts from millions of metrics collected in real-time
  • Proactively learn about issues that may affect service delivery down the line
  • Remediate application and infrastructure issues quickly and accurately
  • Drive continuous improvement in service quality and operational efficiency
Gartner’s representation of an AIOps platform

Embedded Domain Expertise of the
Application and IT Landscape


Any AIOps platform must provide full coverage of your entire application stack and IT infrastructure both on-premises and in the cloud. Beyond this technology integrations will ideally be domain-aware and leverage the APIs and other interfaces available and be tuned to collect and prioritize the most important and relevant data, metrics, logs and events.

eG Enterprise supports over 200+ technologies with layered modules designed by domain experts to ensure data is collected intelligently from technologies such as F5 load balancers, public clouds (AWS, Azure), Citrix sessions, PHP, Java applications, databases, storage and much more.

This in-built domain intelligence ensures the eG Enterprise AIOps engine avoids excessive noise or redundant data and avoids the excessive associated storage costs. Moreover, correlations and root-cause diagnosis at scale are faster and more precise ensuring accurate insights and reliable observability.

Auto-baselining for Proactive Problem and Anomaly Detection

It is impossible to analyze millions of metrics manually. eG Enterprise’s auto-baselining technology uses machine learning to automatically determine normal bounds of metrics. This technology tracks time of day, day of week behaviour of each metric and uses past history to estimate dynamic thresholds for each metric. Administrators can choose the granularity with which they apply this derived intelligence to allow thresholds to be automatically and dynamically fine-tuned.

Auto-baselining is a key to making the monitoring solution proactive and easy to use:

  • Early warning alerts: Administrators get alerts to anomalies and abnormal usage of resources, unusual traffic patterns before end users are impacted.
  • Early warning alerts: Administrators get alerts to anomalies and abnormal usage of resources, unusual traffic patterns before end users are impacted.

Automatic Problem Investigation and Diagnosis

When anomalies or problems are detected, additional diagnosis is automatically performed to collect more details about a problem. Deep domain expertise is required to determine what additional diagnosis is required. The diagnostic checks vary from one application to another and from system to system.

When a problem happens, IT admins often don’t have time to investigate. They may need to reboot a system and the problem might go away. But then the same problem could occur again … Hence, collecting detailed diagnosis automatically when a problem occurs is vitally important and eG Enterprise automates this process.


End-to-end Auto-correlation for Precise Root-cause Diagnosis

The eG Enterprise AIOps engine provides both top-to-bottom and end-to-end auto-correlation to ensure precision root-cause alerting avoiding false alarms and alert storms. The built-in intelligence that understands the relationships between IT infrastructures and applications ensures that root-causes rather than secondary symptoms are identified.

By continually processing millions of metrics and data points beyond the capabilities of a human operator, eG Enterprise ensures rapid root-cause diagnosis allowing IT staff to focus on resolving issues before business systems are impacted or the user experience of staff or customers is affected.


Auto-discovery and Dependency Mapping

  • eG Enterprise’s auto-discovery and dependency mapping ensures modern auto-scaled environments can be handled processing data at scale against a background of dynamic interdependencies.
  • Rich topology maps of discovered architecture and components offer the operator instant observability into the configuration of their systems.
  • Auto-baselining ensures the changing IT environment is continually benchmarked, enabling anomaly detection and avoiding false alerts. Thresholds for alerts are determined by machine learning algorithms including seasonal variations on an hour-by-hour, day-by-day, month-by-month and longer.

Continuous Improvement with Bottleneck Detection, Forecasting and Capacity Planning

  • KPI analysis and bottleneck detection: Analysis of historical alerts is used to provide a clear indicator of which tiers and which layers are responsible for past issues in the infrastructure.
  • Forecasting: Use built-in prediction mechanisms and forecasting techniques to automatically compute how a metric is likely to change in the future. Using this capability, you can determine when in the future the current resource capacity may get exhausted.
  • Right-sizing: For virtualized environments, analyze the performance of VMs over time and provides recommendations on which VMs are over-sized or under-sized, which VMs have not had significant activity in the past, etc.
  • Capacity planning: Trend analysis and outlier detection is used to analyze traffic patterns and resource utilization trends. Use these insights to determine how to plan effectively for future growth.

AIOps Fully Integrated into Business Processes and IT Operations

With eG Enterprise the AIOps engine is embedded and fully integrated into an enterprise monitoring platform to ensure that the data ingested, conclusions and alerts raised, and ensuing actions can be used and processed within standard business processes including for regulated industries with high security, compliance and due diligence needs. With eG Enterprise you can leverage AIOps within a rigorous enterprise framework including:




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  • Works on cloud environments, hybrid-cloud setups and on-premises deployments
  • Deploy eG Enterprise using our SaaS platform or on-premises
  • Suitable for monitoring applications, digital workspaces and IT infrastructures
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