Recently, while visiting a friend in a local hospital, I found myself facing a frustrating distraction: trying to pay parking fees using USSD (a mobile text-based system for quick transactions). The service was either painfully slow or not working at all.
I wasn’t alone. Other visitors were just as exasperated, and parking attendants stood idle, their handheld devices frozen in endless loading loops. It made me wonder if the county’s IT team knew what we were all enduring — or how much revenue was slipping away because the system simply didn’t work.
Monitoring the uptime of IT systems and components is a critical activity that ensure IT service availability and that its performance remains within agreed thresholds and targets. Here’s what we can say about uptime, without any doubts:
So, let’s understand this critical business activity: we will review activities and roles involved in uptime monitoring, then look at what the future holds for this critical element.
With a focus on the reliability and availability of IT services and their components, the purpose of uptime monitoring is to:
Failure to monitor uptime effectively can result in serious negative effects. A delayed response to recovery efforts can result in financial losses, lower customer perception, and even potential regulatory penalties. In fact, today in 2025, data center outages are becoming less frequent and less severe relative to the rapid growth of digital infrastructure — yet major failures still occur and will continue to occur. That’s because of growing complexity across data centers, networks, and systems.
Let’s look at monitoring activities through two lenses: strategy and then tactics — how to actually monitor for uptime.
From a strategic standpoint, uptime monitoring starts with understanding the needs of those who use IT services or the business units that request them on their behalf. These needs might be:
Where possible, these uptime requirements are negotiated with the IT provider to balance system capabilities and costs. Then, service level agreements (SLAs) document and formalize these uptime targets.
Once agreed upon, IT teams set to work, configuring monitoring systems to track and report on performance, ensuring these targets are met. So how do they do that? Let’s take a look at the main activities that inform uptime monitoring:
Monitoring technology components usually employs two approaches:
Metrics provide the raw data needed for monitoring, spanning multiple layers like infrastructure, databases, applications, services, and end-user experiences.
Monitoring based on system internals like logs or HTTP endpoints— referred to as "white-box monitoring" by Google — is often preferred over "black-box monitoring," which tests only the external behavior visible to users.
(Related reading: MELT metrics, events, logs, and traces.)
Once collected, uptime metrics are processed into information that makes sense to those monitoring it in order to adequately respond to it. Key focus areas for processing monitoring data for IT system uptime are:
Once collected, uptime metrics are transformed into meaningful information to help the right people respond as quickly as possible. The key areas of focus when processing this data for IT system uptime are:
Latency: The time delay for a data packet across IT systems. The longer the delay beyond expectation, the more the system is perceived as being down.
Errors: The rate of system requests that fail over a time period. The greater the error rate, the more the perception of uptime being impaired.
Saturation: A measure of resource constraints. If a system is unable to handle load, it may be unable to process new or existing requests resulting in downtime.
Processing metrics ensures the right balance between measurement frequency, detail, and cost. It also categorizes uptime monitoring data into three event groups based on their impact, with each group linked to a specific response:
Processed uptime metrics are next grouped by time intervals and data points, to the appropriate level of granularity. Aggregation organizes large volumes of data into a cohesive set that highlights trends and outliers, helping pinpoint the symptoms and causes of downtime. It ensures that those monitoring uptime receive summarized alerts that:
Aggregated data also enables the calculation of key availability metrics like MTBF (mean time between failures) and MTRS (mean time to restore service).
Once aggregated, uptime monitoring data — like alerts and trends — are presented in dashboards that summarize core uptime metrics for IT services or components. These dashboards include filters and selectors, making it easy to drill down into the data most relevant for investigating and troubleshooting downtime.
These dashboards can also be shared with service consumers to keep them informed about uptime status.
Uptime monitoring typically involves sysadmins and, especially in enterprise environments, the NOC.
IT system administrators are the frontline responders for uptime monitoring. Once IT systems go live, these teams are responsible for receiving and addressing alerts generated by monitoring tools.
While their primary focus is operational, they also play a crucial role during the design phase of IT systems, ensuring monitoring requirements are built into new or updated solutions. Additionally, they configure and test uptime monitoring during the transition phase, as newly developed or acquired services move into production.
In larger IT environments, particularly within mid-to-large-sized service providers, uptime monitoring is often centralized in a network operations center (NOC). A NOC is staffed by a dedicated team that monitors service uptime and performance around the clock. This team provides first-line support to resolve issues or escalates them to specialized teams or external vendors as needed.
The NOC’s primary goal is to detect and triage downtime issues as quickly as possible, typically within the response times defined in SLAs.
A NOC is easily recognized by its digital display screens, which show critical information such as alerts, logs, trends, and other signals that reflect the current state of IT services. These services may be hosted locally or subscribed from third-party providers. Equipped with robust communication and collaboration tools, the NOC facilitates the sharing of expertise and information across teams to support the investigation and resolution of downtime.
The complexity of modern IT environments makes uptime monitoring an increasingly challenging task. Mid-to-large-sized organizations often manage thousands of interdependent components, automated changes and deployments via microservices, containers, and CI/CD pipelines, and integrations across multiple service providers.
This sprawling ecosystem often leads to "alert storms," where system administrators and NOC analysts are overwhelmed with excessive alerts, resulting in analysis paralysis.
To address these challenges, AIOps (Artificial Intelligence for IT Operations) solutions have become invaluable. By ingesting and correlating monitoring data from distributed IT systems, AIOps tools leverage machine learning to:
These solutions continuously learn and improve, offering IT teams better visibility, faster decision-making, and more efficient operations.
Splunk IT Service Intelligence (ITSI) is an AIOps, analytics and IT management solution that helps teams predict incidents before they impact customers.
Using AI and machine learning, ITSI correlates data collected from monitoring sources and delivers a single live view of relevant IT and business services, reducing alert noise and proactively preventing outages.
Effective uptime monitoring relies on carefully managing the scope and meaningfulness of monitoring data. Organizations require a well curated approach to monitoring that identifies and prioritizes:
Understanding dependencies from underlying components and services ensures that the appropriate resources and efforts are focused on monitoring what matters most to the organization and its customers.
IT teams should strive to design their uptime monitoring systems with rules that are simple, predictable, and reliable. To maintain efficiency, system administrators and NOC stakeholders should regularly review the monitoring scope to remove rarely referenced data, ensuring that only actionable alerts reach the monitoring teams. This reduces noise and allows teams to concentrate on critical issues.
Additionally, investing in modern monitoring solutions that integrate AI can significantly enhance the speed and effectiveness of detecting and resolving issues that impact uptime. These tools bring advanced capabilities to streamline operations, improve visibility, and ensure IT services remain reliable for users and customers alike.
Splunk’s market-leading observability tools deliver real-time visibility into every transaction and infrastructure component. That means businesses can reduce MTTR (mean time to repair) from 30 minutes to just 5 minutes while maintaining 100% uptime — even during unexpected traffic surges.
Try Splunk Observability Cloud for free to see how it can transform your uptime monitoring.
Say goodbye to blind spots, guesswork, and swivel-chair monitoring. With Splunk Observability Cloud and AI Assistant, correlate all your metrics, logs, and traces automatically and in one place.
See an error or have a suggestion? Please let us know by emailing splunkblogs@cisco.com.
This posting does not necessarily represent Splunk's position, strategies or opinion.
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