Modern observability on Ubuntu servers has moved far beyond collecting basic metrics and checking whether services are running. In production environments today, the goal is to build systems that are intrinsically understandable — where every slowdown, failure, spike, or anomalous behavior can be quickly traced back to its root cause with minimal guesswork. This requires a deep understanding of how the operating system exposes internal state, how different telemetry signals relate to each other, and how to compose them into a coherent picture of system and application behavior.
Preparation Work – Building a Strong Observability Foundation
Before deploying any collector or dashboard, several foundational aspects must be addressed. Time synchronization is non-negotiable: distributed tracing, log correlation, and even basic metric aggregation become unreliable when clocks drift more than a few tens of milliseconds. Ubuntu’s systemd-timesyncd should be configured to use low-latency regional NTP pools, and chrony is often preferred in high-precision environments due to its better handling of intermittent connectivity and faster convergence.
Enabling Ubuntu Pro (free for up to five machines) provides long-term kernel and package security coverage, which is especially valuable when running long-lived observability agents that need to remain patched without frequent reboots. Equally important is ensuring that the kernel supports modern eBPF features without restrictions. Most recent Ubuntu LTS releases ship with unprivileged eBPF enabled by default, but it is still worth verifying that no security module (SELinux, AppArmor in very restrictive mode) blocks required helpers.
Finally, the system should be tuned to expose richer internal signals. Enabling PSI (Pressure Stall Information) metrics, activating extended block layer accounting, and increasing perf_event_paranoid to at least -1 are small but high-leverage changes that dramatically improve visibility into memory, I/O, and CPU contention.
Core Configuration – Mapping OS Signals to Observability Signals
Ubuntu’s systemd is the central source of truth for service state and runtime behavior. journald provides structured logging with metadata such as unit name, priority, process ID, and cgroup path. This makes it possible to correlate log events with resource consumption and request flows without adding instrumentation to every application.
At the kernel level, several interfaces provide deep visibility:
- /proc and /sys expose scheduler statistics, memory zones, NUMA balancing, interrupt distribution, softirq execution times, and network queue states.
- Pressure Stall Information (PSI) quantifies the actual delay caused by memory, CPU, and I/O contention — a far more actionable signal than traditional utilization percentages.
- eBPF allows safe, dynamic instrumentation of virtually any kernel or user-space event without modifying code or rebooting.
- tracepoints, kprobes, and uprobes offer stable and unstable hooks into kernel and application execution paths.
Modern collectors (node_exporter, process-exporter, blackbox_exporter, bpf-based tools) map these signals into time-series metrics, while promtail or OpenTelemetry Collector turns journald and text logs into searchable, structured streams.
Optimization Enhancements – Reducing Noise and Increasing Signal
One of the biggest challenges in large-scale observability is cardinality explosion — too many unique label combinations leading to memory pressure and slow queries. Thoughtful relabeling and metric aggregation at the source are essential. High-cardinality labels (container IDs, full HTTP paths, user IDs) should be dropped or aggregated early.
Another key optimization is moving from polling to event-driven collection wherever possible. journald push-based forwarding, eBPF ring-buffer programs, and OpenTelemetry’s OTLP protocol all reduce latency and CPU overhead compared to periodic scraping.
Continuous profiling is one of the highest-leverage additions. Tools that use eBPF to sample CPU, memory allocations, off-CPU time, and lock contention provide flame graphs and Pareto views without modifying application code. This allows teams to identify hot code paths, memory allocation patterns, and synchronization bottlenecks that traditional metrics rarely reveal.
Latency histograms (especially at multiple quantiles: p50, p90, p95, p99) should be preferred over simple averages for every latency-sensitive measurement — whether it is request duration, disk I/O service time, or network RTT.
Verification and Troubleshooting – Turning Signals into Actionable Insight
Effective observability is measured by how quickly and confidently teams can answer two questions during an incident:
- Where is the problem?
- Why is it happening there?
Golden signals (rate, errors, duration, saturation) remain valuable, but they are rarely sufficient alone. Correlating them with PSI pressure, scheduler run-queue latency, block-layer queue depth, and network softirq time often reveals the real bottleneck.
Common diagnostic patterns include:
- High CPU utilization but low application throughput → inspect scheduler statistics, run-queue length, context-switch rate, and involuntary preemptions.
- Memory saturation with low swap activity → examine PSI memory stall times and reclaim pressure.
- Slow I/O despite low utilization → check queue depth, average service time per operation, and whether the workload is read or write heavy.
- Network performance degradation → examine softnet backlog, softirq execution time, TCP retransmit rate, and congestion control state.
When metrics alone are inconclusive, targeted eBPF programs or bpftrace scripts can provide sub-second resolution insight into exactly which processes, files, or sockets are contributing to the observed behavior.
Conclusion
Observability on Ubuntu has reached a level of maturity where it is no longer about collecting more data — it is about collecting the right data in the right way and composing it into a system that can explain itself. The combination of systemd’s structured journal, the Linux kernel’s rich instrumentation surfaces (especially via eBPF), and modern open-source standards (OpenTelemetry, Prometheus exposition format, Loki’s label model) creates a foundation that allows teams to move from reactive firefighting to proactive understanding.
The most valuable investment is not in adding yet another dashboard, but in developing the discipline to instrument thoughtfully, reduce cardinality aggressively, correlate across signals meaningfully, and continuously validate that the collected data actually explains real incidents faster than before.
When building high-availability, low-latency Ubuntu server environments that require robust observability — especially for web services, APIs, databases or real-time applications — choosing infrastructure with excellent connectivity and stability makes a significant difference. Hong Kong-based cloud servers are frequently selected for their superior regional latency and line quality.