The increasingly complex nature of modern tech infrastructure means IT teams need a highly robust approach to monitoring performance and reliability.
Driven by ubiquitous trends such as cloud adoption and digital transformation, state-of-the-art observability now goes well beyond legacy approaches to system monitoring to provide advanced capabilities, including proactive issue detection, root cause analysis and automated remediation.
For example, observability enables cloud native application monitoring by tracking microservices performance, detecting bottlenecks and providing real-time insights into container-based workloads. It is also applied to incident response, cybersecurity threat detection and IT infrastructure optimisation by analysing logs and metrics to improve reliability, security and cost efficiency.
Observability has also become key to the effective implementation and management of edge computing technologies – a major advantage given the highly distributed nature of edge environments brings a range of unique challenges.
For instance, with potentially thousands or even millions of devices, sensors and nodes operating at the edge, it is becoming increasingly impractical to manually curate technology deployments and manage their operational lifecycle. Moreover, technical and operational constraints add further complexity to the observability puzzle, with issues such as intermittent network connectivity and limited resources often getting in the way of effective data collection and transmission.
In this context, observability provides insight into how edge environments perform and function. IT teams need advanced monitoring and analysis capabilities to accelerate troubleshooting, optimise edge deployments and ensure reliable, responsive applications for end users. These solutions must also be capable of managing the vast amount of data generated and ensuring comprehensive visibility across diverse locations.
How edge observability works
But how does this work in practice? At a fundamental level, edge observability works by capturing and utilising telemetry data such as metrics, logs and traces to monitor the state of applications and infrastructure.
The most effective observability platforms not only gather data but also provide actionable insights that support holistic monitoring across the entire lifecycle of edge components, including services, hardware, applications and networks.
For instance, centralised observability is essential for maintaining control over distributed systems because it ensures that despite the geographical dispersion of edge nodes, operators
can still manage and respond to issues in real-time, ensuring distributed systems work seamlessly.
One of the key tools used to achieve effective observability is OpenTelemetry. An open source project, it has evolved to become an industry-standard cloud native technology that enables developers and operators to collect and transmit telemetry data via a consistent approach that maintains visibility across all system components.
OpenTelemetry lays the foundation for collecting standardised telemetry data, but to fully harness its value, observability platforms enable users to integrate AI-driven predictive analytics, automated anomaly detection and intelligent correlation of telemetry data. These platforms also allow proactive issue resolution, optimise performance across distributed environments and enhance security. This ensures that edge observability is not just about data collection but delivering actionable insights that drive resilience and operational efficiency.
Ultimately, cloud native edge observability should not be limited to raw telemetry data, which can be difficult to interpret. Instead, it should leverage a platform that integrates topology mapping, correlation, issue detection and automated remediation to create a clear, actionable view of edge infrastructure health and performance.
For organisations to meet end-user expectations that edge devices “just work,” observability must provide more than just data – it must enable real-time insights, optimise operations and enhance system resilience. This ensures that edge deployments remain reliable, efficient and fully compliant with performance standards.
About the Author
Mark Dando is General Manager, North Europe at SUSE. SUSE is a global leader in innovative, reliable and secure enterprise open source solutions, including SUSEⓇ Linux Suite, SUSEⓇ Rancher Suite, SUSEⓇ Edge Suite and SUSEⓇ AI Suite. More than 60% of the Fortune 500 rely on SUSE to power their mission-critical workloads, enabling them to innovate everywhere – from the data center to the cloud, to the edge and beyond. SUSE puts the “open” back in open source, collaborating with partners and communities to give customers the agility to tackle innovation challenges today and the freedom to evolve their strategy and solutions tomorrow. For more information, visit www.suse.com.
