What is IT infrastructure monitoring?

4 min read

by BigPanda | Mar 18, 2024

Behind the scenes of any IT infrastructure lies the ongoing challenge of monitoring and managing never-ending systems, applications, and network devices. As an IT professional, your task list always includes staying ahead of potential issues, minimizing downtime, and navigating disruptive alerts.

What is IT infrastructure monitoring?

IT infrastructure monitoring is a systematic and ongoing process of regularly observing, collecting data, and analyzing the performance, availability, and overall health of the components within your infrastructure. From servers and networks to applications and databases, all are essential parts of your organization’s digital operations.

The main goal is to quickly identify faults and define the root causes behind system failures or performance issues. This meticulous process uses multiple metrics and logs to pinpoint issues accurately, whether they originate from hardware failures, software misconfiguration, network outages, or application errors.

To facilitate the process, you can leverage specialized infrastructure monitoring software like ServiceNow, Solarwinds, Datadog, Splunk, and Dynatrace. These tools are crucial in aggregating and presenting diverse data generated by the IT infrastructure components.

Insights derived from these tools empower teams to proactively manage and optimize IT system performance and reliability, ensuring seamless operation of digital services.

Common IT infrastructure monitoring challenges

The key challenges in IT infrastructure monitoring typically stem from the complexity of backend systems, particularly when transitioning to cloud platforms or relying on cloud infrastructure. For example, consider the following:

Legacy tools and cloud: Transitioning to the cloud poses challenges in finding suitable infrastructure monitoring tools. Designed for on-premises environments, it’s a strugglel to adapt legacy tools to the dynamic nature of cloud-native components. Additionally, the traditional host-centric approach is ineffective when dealing with auto-scaling elements like containers and serverless functions.
Integration and access: Legacy tools often face integration issues with cloud services, compromising their capability to authenticate access to cloud-based metrics through APIs. Moreover, the manual SSH access required for retrieving infrastructure metrics from cloud servers takes significant time and expertise, adding to the complexity.
Cultural silos and collaboration: DevOps teams often use different monitoring tools than IT, creating a silo and leading to misalignment in identifying and fixing performance issues. Limited access to teams’ respective tools hampers collaborative issue resolution. A unified performance monitoring tool is crucial to integrate infrastructure and application performance metrics, ensuring seamless collaboration.
Comprehensive monitoring: For many organizations, infrastructure monitoring is only one facet of a complete solution. Effective troubleshooting requires a comprehensive monitoring solution that combines data from infrastructure monitoring with information from applications, networks, and other system elements to understand root cause.
Multicloud challenges: More clouds introduce unique challenges, with providers each offering their own native monitoring tools. Juggling multiple solutions complicates the task of gaining a unified, comprehensive view of cloud infrastructure, requiring modern monitoring capabilities.
DevOps transition and operational service model: DevOps teams may face challenges transitioning to an operational service model, especially with adopting microservices-based architecture and cloud-native technologies. Managing cloud infrastructure efficiently is important to minimize unplanned downtime and improve end-user experience.

How AIOPs makes IT infrastructure monitoring more effective

AIOps uses AI and advanced analytics to streamline IT infrastructure monitoring tasks and allow IT teams to focus on strategic priorities.

The main goal is simplifying tasks so IT teams can focus on important priorities. This happens by automating routine tasks efficiently. AIOps uses real-time pattern recognition and machine learning to spot anomalies and identify possible issues. The predictive ability of AIOps allows for early intervention, reducing downtime and improving system reliability.

Investing in AIOps platforms can expedite root-cause analysis by correlating data from multiple sources to facilitate quicker issue resolution. The scalability of these systems allows them to adapt as IT landscape evolves, accommodating increased complexity. Integration with incident management systems ensures a cohesive response, thereby improving overall efficiency.

AIOps also addresses alert fatigue by filtering and prioritizing alerts to minimize distractions from non-critical issues. Continuous learning ensures adaptive improvement over time, maintaining the effectiveness and relevance of the monitoring system. A unified view of application and infrastructure performance metrics further streamlines collaboration, fostering better communication among IT teams.