Infrastructure Operations Today: Modern Trends for I&O

Key technologies in modern ITOps

To meet the demands of modern IT environments, organizations are increasingly adopting advanced technologies that enhance the effectiveness and agility of their ITOps. The following key tools and approaches are shaping how teams automate, monitor, and optimize their infrastructure operations.

Infrastructure as Code (IaC)

One major advancement is Infrastructure as Code (IaC), an approach for managing and provisioning infrastructure resources through automation scripts.

ITOps users write machine-readable configuration files instead of manually configuring infrastructure systems or using a GUI-based tool. These configuration files define the desired state of the infrastructure. An IaC automation script reads these files, evaluates the existing environment state, and automatically executes the changes required to reach the desired future state. This process is called state management in IT operations.

With IaC, the infrastructure is continuously in sync with the code; if a change takes place, the IaC tool can detect the drift and optionally fix it - a process called drift detection. This approach also makes infrastructure management idempotent: the same automation code can run multiple times to reproduce the result. Old infrastructure components are replaced instead of modified, which is referred to as having immutable structure. Finally, automation requires infrastructure systems to be modular and reusable through code, supporting scalable and reliable operations.

Monitoring and observability

In addition to automation, monitoring and observability are critical in modern infrastructure environments.

Modern infrastructure systems are highly scalable and operate in complex IT environments. Business services are highly dependent on IT assets underlying the software systems and applications. Because of this dependency, ITOps must ensure optimal infrastructure performance to guarantee quality end-user experience.

In DevOps organizations, the development pipeline must ensure continuous improvement. This requires DevOps teams to identify and manage issues and requirements proactively. Engineers responsible for ITOps must know exactly when things go wrong (monitoring) and understand exactly why something is wrong (observability). With this knowledge, ITOps aims to reduce outages and deliver a reliable infrastructure system. Reliability is typically measured in terms of metrics such as mean time to recovery (MTTR), which measures how long it takes to recover from a system failure or IT outage.

AI for ITOps (AIOps)

Another transformative approach is AI for ITOps, also called AIOps.

AIOps teams rely on both data and intelligence to make informed decisions about infrastructure and operations. Large volumes of real-time data streams are captured from monitoring tools. The data includes:

• Metrics.
• Events.
• Logs.
• Traces.
• Tickets.
• Sensor data.

(Related reading: logs vs. metrics and MELT (metrics, events, logs, traces) .)

A data platform ingests this information, where it is preprocessed, standardized and normalized for downstream use cases such as predictive analytics and automation controls.

Third-party analytics tools can analyze the available information using advanced machine learning algorithms, which correlate data patterns to detect anomalous behavior, predict outages, discover IT assets and dependencies in a multi-cloud IT environment.

Control actions are performed through automated scripts and ITSM integrations that execute predefined company policies on infrastructure and operations.

AIOps and IaC in DevOps

AIOps and IaC also form a strategic backbone for DevOps environments. The technology is now responsible for empowering Devs and QA teams with ITOps capabilities. For example, IaC facilitates CI/CD environments with capabilities for provisioning resources programmatically.

(Related reading: DevOps monitoring/ CI/CD monitoring .)

Teams can version control their infrastructure systems, track, and manage changes in infrastructure operations in the same way they control changes with software builds.

Moving from manual to data-driven operations

The role of Ops teams is increasingly replaced by intelligent automation and AIOps, shifting from manual to data-driven processes. Data is collected in real-time, ingested, and analyzed using predictive analytics tools. The resulting knowledge drives critical decisions on resource management, capacity planning and optimization.

At the same time, IaC serves as a foundation for shifting operations left. This enables Devs, Ops, and QA teams to handle ITOps limitations earlier in the Software Development Lifecycle (SDLC), resulting in more resilient and efficient IT operations.