Application monitoring tools provide detailed visibility into the performance, availability, and user experience of applications and the supporting infrastructure and networks. Helping IT and DevOps deliver consistent application performance and rapidly diagnose and resolve issues are the primary objectives of these tools.
Application monitoring, also called application performance monitoring or APM, can be delivered on-premises or via SaaS. The immediate benefits of application monitoring are rapid root cause diagnosis, faster time to resolution, and improved IT staff productivity. When used consistently as part of DevOps processes, you can also accelerate application deployment, deliver higher-quality user experiences, and improve business productivity. The most effective solutions provide a unified view of performance of all enterprise applications across the technology stack, from the device to the network to the application to the infrastructure backend.
How does the application monitoring process work?
Application monitoring tools collect, store, and analyze the necessary data and metadata for troubleshooting and optimizing performance. They typically rely on different types of instrumentation to provide real-time data. Instrumentation types include:
✓Agents for tracing Java or .NET code
✓ Distributed tracing of end-to-end transactions
✓ APIs for observability, cloud and other platforms
✓ Agents on devices and servers to continuously collect detailed end user, application, container and systems data
✓ High-speed, passive appliances to measure end-user response time at key locations
The resulting data may be analyzed using AIOps techniques and application performance is frequently visualized via intuitive dashboards. When performance exceeds automatically-defined baselines, application teams can drill down contextually to resolve issues, can trace transaction performance across the distributed infrastructure, and can use application mapping visualizations to better understand complex, dynamic relationships between components.
Note that with the shift to cloud-native applications, you may need to re-evaluate how you monitor applications. Cloud-native application monitoring solutions support apps deployed in Docker and Kubernetes, including Pivotal Platform, OpenStack, Red Hat OpenShift, IBM Cloud, Amazon AWS Elastic Container Service (ECS) and Elastic Kubernetes Service (EKS), Google Kubernetes Engine (GKE), and Microsoft Azure Kubernetes Service (AKS). They also measure performance at higher frequencies, typically 1-second intervals for infrastructure metrics, to gain better visibility into these dynamic environments, and will leave no distributed tracing blind spots, capturing every transaction end-to-end across every tier.
For SaaS environments, application monitoring is especially difficult since IT lacks control over the backend systems. End-user experience monitoring solutions can help you understand how an application performs as it renders on your user’s screen and pinpoint the source of delay.
What are the key requirements for application monitoring?
Evaluate how the right solution can improve performance across all applications and types of devices with the following list of key requirements.
Monitor digital experience of web, mobile, and SaaS
Monitor all of the apps in your portfolio to ensure performance meets or exceeds expectations.
Troubleshoot performance problems down to the device
Identify whether poor application performance is due to devices issues or something else.
Improve tier 1 triage processes
Quickly drill down from end user experience to user device health, application backend, and infrastructure performance data.
Scale to monitor microservices and hyperdistributed cloud applications
Use an enterprise-wide big data approach that captures user and code-level diagnostics data for every transaction.
Provide data granularity for containerized cloud environments
Second-by-second granularity to accurately pinpoint performance issues caused by insufficient CPU cycles or shared resource dependencies. This is necessary to track changes in highly dynamic environments
Apply machine learning and data analytics
Leverage machine learning and visualizations to analyze and uncover patterns and anomalies in large volumes of data that humans and existing tools cannot handle.
Hold IT vendors accountable
Hold vendors accountable to SLAs by monitoring what users actually experience with cloud-delivered and third party services, and SaaS apps.
Assess the financial impact of poor performance
Prioritize dev and customer support efforts based on financial impact and optimize workforce productivity, customer service, and the bottom line.
Foster cross-team collaboration
Reach consensus and resolve issues faster with self-service dashboards that use a consistent set of rich data and analytics.
Build higher-quality applications
Find and fix bottlenecks, errors, and resolve problems before they impact users with granular diagnostics. Prioritize future development efforts based on adoption rates, performance data, business value, and usage patterns.
What challenges are posed by cloud-native applications?
Highly-distributed, dynamic cloud-native applications pose many challenges for traditional application monitoring systems. Cloud-native applications are also usually developed by Agile/DevOps teams, meaning they will also be highly changeable, with code updates occurring weekly or even daily. In addition, in containerized environments, the relationships between app components are constantly changing which makes application dependency mapping prone to error without high definition data.
Studies show that instrumenting the modern cloud-native environment results in 2-3x the volume of monitoring data, compared to traditional data center infrastructures. The resulting volume of data demands a new generation of tools that can scale to capture, store, and analyze transaction data and metadata in real-time without impacting user experience or straining the storage infrastructure. Legacy tools that were developed for monolithic mainframe and client-server architectures that changed infrequently simply can’t keep up.
Next generation application monitoring systems are designed on big data foundations and operate effectively at cloud scales. They capture data and metadata to provide clarity and context when issues are identified, removing the need to try and reproduce an issue. They analyze and correlate information across multiple sources tracing the transaction across distributed architectures. Finally, they use machine learning and innovative visualizations to rapidly detect patterns and surface both business and IT operational insights from these huge datasets.
Does application monitoring factor in the end user perspective?
Definitely. In fact, monitoring from the end-user’s point of consumption, whether it is a mobile device, web browser, computer, or virtual desktop provides the most valuable perspective. For some organizations, end users are effectively the application monitoring system since 80% of issues first identified by the users themselves. A better alternative is to invest in end-user experience monitoring solutions that measure device health, application performance, and usage trends to identify performance and experience issues before users notice them. When integrated with application monitoring, these tools provide proactive notification and diagnosis support from the user’s device to the network to the backend.
Outfitted with this level of visibility and analytical assistance, enterprises can readily quantify the impact of IT issues on workforce productivity and customer satisfaction. DevOps can more easily prioritize their efforts and resources towards the most critical and impactful issues. Performance trends and before-and-after measurements validate updates and development priorities. Business are better equipped to understand the usage of their applications and services, monitor and manage service level agreements, and gain valuable insight into user and customer behavior.