The IT Infrastructure Monitoring industry is currently undergoing robust growth. For example, with an average CAGR of 10.4%, the North American IT Infrastructure Monitoring market is projected to grow from $1.2 billion in 2021 to over $3.05 billion by 2030. The reasons for this growth are relatively easy to see.
Our modern business landscape is driven by technology, and with this dependency comes a wide range of issues. A 2017 study by Business Wire showed that 82% of companies experience at least one downtime event in three years, with most experiencing two.
Now, the main problem is not downtime events but the cost of downtime. A Splunk survey of 1,750 respondents reports that the costs of IT downtime can reach as high as $500,000 per hour. On a more granular level, Pingdom shows that these costs hit a whopping $636,000 per hour in the Healthcare industry, $2 million per hour in the Telecommunications industry, and $2.4 million per hour in the Energy industry.
IT infrastructure monitoring has proven to be a great solution, and leaders in observability say they experience 33% fewer outages per year, compared to beginners.
Keep reading as we dive IT infrastructure monitoring.
What is IT Infrastructure Monitoring?
IT infrastructure monitoring is collecting and analyzing data on the status and performance of IT systems. It is basically observability in IT infrastructures. IT teams compile data obtained from components such as servers, containers, databases, endpoint devices, networking components, and storage devices.
Due to the immense difficulty of IT infrastructure monitoring, it is typically facilitated by advanced software tools. These advanced tools allow DevOps and SRE teams to visualize ingested data, optimize business observability requirements, receive alerts when infrastructure components are in danger, identify the source of threats, and, with the more advanced monitoring tools, automatically remediate damage to the IT infrastructure.
How IT Infrastructure monitoring works
To understand how IT infrastructure monitoring works, you need first to understand what it intends to monitor in the first place — the application infrastructure.
The application infrastructure combines backend hardware, operating system, and application layers that power IT solutions/business services.
Here is what the layers look like;
The Hardware Layer
The hardware layer of the application infrastructure comprises the physical servers, routers, switches, storage devices, and endpoint devices within the IT system. This layer also includes processors such as logic chips, memory chips (RAM), and neural processing units (NPUs) that power these hardware devices. Even with cloud-based IT deployments, the hardware layer and its components still exist, but cloud providers virtualize these.
Operating System (OS) Layer
The OS layer is the software foundation of the infrastructure that connects the hardware layer to the application layer. Through programs, platforms, and runtime environments, it utilizes hardware components to execute the application layer of the infrastructure.
Application Layer
The application layer is the user-facing layer of the infrastructure that generates content for client applications like web browsers. It also specifies protocols or methods through which these user-facing software clients send and receive information to/from other clients, or users within a network.
So how does it work?
IT infrastructure monitoring involves using a software tool to identify log sources in the host environment and ingesting metrics on hardware resource usage. This data is then aggregated, visualized, and analyzed by the monitoring tool. By centralizing data on the monitoring tool, IT personnel can understand the overall status of the infrastructure and engage in seamless resource management to meet changing IT requirements.
The monitoring tool executes its functions either through in-built protocols or an agent, and this is where the different types of IT monitoring come in.
Regardless of the type implemented, however, IT infrastructure monitoring is an important operations process that ensures companies like yours manage availability better and meet service level agreements (SLAs).
Types of IT Infrastructure monitoring
There are generally two types of IT infrastructure monitoring:
- Agentless infrastructure monitoring
- Agent-based infrastructure monitoring.
Agentless IT infrastructure monitoring
Agentless infrastructure monitoring eliminates the need to install an agent onto systems to ingest data and supply data to monitoring tools.
Instead, the monitoring tool has ingestion technologies/protocols that allow it to collect data directly from hardware layer components.
Examples of these protocols include the IP Flow Information Export (IPFIX), Simple Network Management Protocol (SNMP), Sample Flow (sFlow), Juniper Flow (JFlow), NetFlow, and Hypertext Transfer Protocol (HTTP) protocols, to mention a few.
Through in-built protocols, agentless infrastructure monitoring tools connect to systems within your IT infrastructure and ingest CPU, Memory, Disk, and Network usage data. They help you avoid the extra costs of installing an agent for each server/network device.
Agent-based IT infrastructure monitoring
Agent-based infrastructure monitoring involves installing agents into your server/network environments. The agents are tasked with ingesting hardware usage data and sending this data to a monitoring platform.
Using an agent permits customization. Instead of relying on the capabilities of protocols, IT teams deploy an agent explicitly designed for the IT device or designed to ingest more specific, granular data.
Agent-based monitoring also allows DevOps and SRE teams to keep ingesting resource usage data even if the connection between the monitoring platform and IT systems is lost.
Data is then transferred when a connection is reestablished, allowing you to achieve more consistent IT monitoring. Agent-based IT monitoring is also more secure, as it involves unidirectional information flow — where a connection is initiated and only the agent transfers data.
What's more, installing agents onto server environments reduces the workload of the monitoring tool, giving it more room for more efficient data analytics.
What parts of the infrastructure should be monitored?
Infrastructure monitoring tools work with metrics. These metrics are compared with IT requirements to determine whether the IT infrastructure is in danger.
However, where exactly are these metrics pulled from? As you may have guessed, monitoring tools collect IT infrastructure metrics from the following:
- Hardware
- Operating System
- Applications
- Network
1. Hardware
Monitoring the health of hardware components is vital to limit availability problems. You monitor CPU usage, Memory usage, Disk usage, and Disk I/O, among others.
2. Operating System
Collecting metrics about the operating system allows you to measure how well the system facilitates the application's utilization of hardware resources.
Some of the most important metrics to measure here include
- the system response time,
- load balancing metrics,
- database performance metrics,
- cache usage metrics, and
- garbage management metrics.
We have a very informative article about IT metrics. Please be sure to look at it for deeper insights.
3. Application
The performance of the application layer directly affects user experience and indirectly indicates the health of the OS and hardware layers.
Some metrics to measure here include the application request rate, application latency, request error rate, application availability, and application dependency mapping.
4. Network
The IT infrastructure network determines how data is transferred from one layer or component to another. Any bottlenecks here will put your entire infrastructure's functionality on hold.
Some metrics to measure here include
- Network latency (the speed of transferring data packets),
- Network throughput (how many data packets are being transferred at a time), and
- Network error rates (how many transfer requests weren't successful).
DevOps and SRE teams should also monitor resource usage within virtualized environments if part of the IT infrastructure is deployed in the cloud. This includes tracking the allocation of virtual machines (VMs) and VM CPU, memory, storage resources, and networks, among others.
Monitoring these parts of IT infrastructures opens the door to many benefits.
The benefits of IT infrastructure monitoring
The ultimate goal of IT infrastructure monitoring is to use data-driven insights to improve infrastructure performance outputs, reduce wastage, and drive better business outcomes.
More specifically, you stand to gain these benefits;
1. Efficient resource management
IT infrastructure monitoring provides insight into hardware resource usage and application requirements.
Through these insights, IT teams understand when more resources are needed to support application performance. Based on this, they can scale resource allocation as application requirements change.
2. Improved IT system security and performance
Continuous ingestion and analysis of the infrastructure performance metrics enable IT teams to recognize the existence of bottlenecks and security threats.
IT teams may look deeper into resource usage, error rates, and event logs. With this, they can identify abnormalities and root causes and immediately remediate issues.
These measures may include rebalancing the load on servers, adding more servers to the environment, managing garbage on memory, or isolating threat actors or devices from the IT network.
3. Increased MTTF and MTBF
The mean time before failure (MTBF) in IT is a pre-incident metric that measures the time before an IT system develops defects or fails. The mean time to failure (MTTF) is a metric that measures the average time between when a fault is detected and when the defective IT system goes down.
Collecting this data from IT systems allows you to understand how long they stay in healthy and unhealthy states.
Through this, DevOps and SRE teams know how reliable systems are and whether it is necessary to make replacements.
IT teams can adopt more efficient supporting hardware or better IT networking structures and operating systems to ensure MTTF and MTBF times remain as high as possible.
4. Reduced MTTD and MTTR
The mean time to detection (MTTD) is a post-incident metric used to measure the average time it takes to identify a critical threat that results in an outage.
On the other hand, the mean time to repair (MTTR) is the amount of time it takes to fix this outage.
Infrastructure monitoring allows you to reduce these periods. You utilize data for root cause analysis to identify correlating events, find bottlenecks, and then apply specific fixes to identified threats.
Reducing your MTTD and MTTR, and increasing MTTF and MTBF, directly translates to reducing overall IT downtime.
5. Improved cost efficiency
The efficient management of hardware resources based on IT requirements reduces wastage and maintains optimal resource usage by discarding unused hardware components.
Cloud deployments make this even easier, as the server can be scaled seamlessly (up or down) as the application layer demands.
When you also reduce downtime by maintaining appropriate MTTF, MTBF, MTTD, and MTTR times, you keep business operations alive and avoid hundreds of thousands or even millions of dollars in downtime costs.
Overall, IT monitoring helps to maintain proactive supervision over the IT infrastructure. Through proactive management, critical issues are identified faster and immediate action can be taken to neutralize them before they result in prolonged IT failure and costs.
Please read this valuable guide for more on reducing IT costs.
The adoption of IT infrastructure monitoring is not all rosy, however., as we'll see in the challenges.
Common challenges that organizations encounter with IT Infrastructure Monitoring
As mentioned above, incorporating monitoring workflows into IT processes is more complicated than organizations would like. From team-related factors to size and cost, IT infrastructure monitoring faces the following challenges in its implementation.
1. The inefficiency of legacy tools within cloud environments
Legacy IT systems operate within physical on-premise environments where hardware components and their identifying IPs remain static for as long as possible.
This means IT teams have direct access to hosts; installing agents on these hosts is ordinarily a one-time endeavor. Hence, legacy systems aren't generally designed for frequent changes to the IT composition.
Cloud environments, on the other hand, come with scalability, where servers and resources are added and removed as the IT infrastructure demands. Frequent changes to hosts, as well as the existence of containerized servers and serverless environments, hinder the installation of monitoring agents.
Moreover, there may be a direct incompatibility between legacy tools and cloud environments. This is where legacy tools are incapable of accessing cloud-based metrics, or processes required for integration are extremely difficult, time-consuming, and costly.
2. The risk of excessive number of monitoring tools
Large enterprises adopt an average of 16 monitoring tools for different IT infrastructure components. For instance, they implement separate tools to monitor CPU usage, storage, application latency, network latency, throughput data, and database usage.
A large number of tools generate separate datasets. IT teams spend more time aggregating and unifying data than acting on threat indicators.
What's more, it isn't surprising to see IT operations managed through independent infrastructures and networks in distributed IT environments where team members are spread across multiple geographical locations. With so many tools, IT becomes more complex, and unifying data becomes even more difficult.
3. Siloed IT environments
When the development and operations teams work separately, getting value from IT monitoring becomes even more impractical.
This is where they use separate monitoring tools for test and production environments, leading to inconsistencies in the insights that drive decision-making.
Prioritizing threats and events become a tug of war, and root cause analysis is limited to deployment environments..
To overcome these challenges, thankfully, there are certain best practices organizations can apply while implementing IT infrastructure monitoring.
Best practices for successful enterprise IT infrastructure monitoring
The best practices in IT infrastructure monitoring focus on unifying IT processes across different teams and reducing inefficiencies in incident remediation efforts.
How do you achieve this?
1. Incorporate custom dashboards
To defeat the challenges arising from excess IT infrastructure monitoring tools, adopting a single software for all your monitoring needs is almost the default solution.
However, the metrics used by security teams are normally different from the metrics used by IT resource administrators. Hence, a single identical dashboard does not work for all. In this case, an IT monitoring tool with customizable dashboards comes as an excellent solution.
Creating custom dashboards for every class of users provides appropriate actionable insights at a glance. They provide each team with immediate access to relevant information, allowing faster decision-making between IT stakeholders.
2. Prioritize IT systems
IT teams should prioritize systems in relation to organizational goals and risk tolerance. Priorities are created based on the potential impact of a specific system's downtime on business goals, the exploitability of systems, and the value of IT assets associated with that system.
You can also prioritize systems based on their impact on compliance with regulatory standards.
3. Create ML-powered alerts
Monitoring tools compare metrics against baselines. They then use alerts to inform the IT team of excesses or inefficiencies that may have grave implications on the health of the IT infrastructure.
However, with comprehensive contextualization, IT teams can receive more alerts, leading to false positives or unexpected IT failure.
Creating actionable alerts involves setting up the proper metrics against the correct baselines. Although IT teams can create specific alerting rules for different IT systems, adopting a monitoring tool that uses machine learning (ML) to develop dynamic baselines can give more accurate alerting results.
Also Read: Machine Learning and Artificial Intelligence in Cybersecurity
4. Review metrics
What happens when IT monitoring tools don't work with the right metrics? It means they wait to send alerts, response workflows will lag, and warnings will become ineffective.
Hence, reviewing metrics as frequently as business goals and requirements change is essential.
Have you added more servers, and has this made availability requirements increase from 98% to 99%? Be sure to constantly update the KPIs and baselines through which you monitor performance issues.
5. Look towards AIOps
AIOps is the use of automation to power IT management processes. In IT monitoring, it involves, firstly, using automated workflows to compile data from different intelligence sources across the infrastructure. Secondly, it also involves the use of automated playbooks to trigger response workflows when threats or incidents arise.
This level of automation improves observability over IT, reduces MTTR, and saves costs. It’s important to look for automation capabilities when choosing IT infrastructure monitoring tools.
More best practices worth adopting include testing monitoring tools through free trials and demos before full implementation and quickly referring to vendors when your teams encounter issues.
Installing monitoring agents on systems across the entire IT infrastructure and not just systems deemed the most critical is also more effective.
How to get the most out of IT infrastructure monitoring
Adopting modern solutions that unify monitoring workflows across the entire infrastructure is crucial.
Traditional monitoring approaches often involve using separate tools for different components. This can lead to fragmented data and increased complexity.
On the other hand, modern monitoring platforms are designed to scale with the growing complexity of IT environments. This scalability ensures that monitoring remains effective as the infrastructure evolves.
Consider incorporating ML and AIOps for more effective incident response. ML algorithms can analyze historical data to identify patterns and anomalies. This enables proactive detection of potential issues before they escalate. AIOps leverages AI technologies to automate repetitive tasks and provide actionable insights.
Closely related to this topic is IT infrastructure management, and we have a very good guide on this. Don’t leave before checking it.
