Application Performance Management (APM)

Application Performance Management Overview

What is APM?

Application Performance Management (APM), also called Application Performance Monitoring, refers to the practice of monitoring and the tools that enable IT operations and application developers to monitor an application's performance. The primary function of APM is to detect and diagnose complex internal application performance issues to ensure optimal performance and compliance with service-level delivery requirements. Most APM solutions utilize application code level performance data that requires code injection to collect application monitoring data and insights.

In order for users to take advantage of APM, application development is required to enable APM analytics. This restricts legacy APM tools to monitoring applications that are developed in-house.

Industry analyst firm, Gartner®, breaks APM capabilities into three main functional categories:

  • Digital Experience Monitoring (DEM)—Digital experience monitoring is an availability and performance monitoring discipline that supports the optimization of the operational experience and behavior of a digital agent, human or machine, as it interacts with enterprise resident software applications and services. It includes real user monitoring (RUM) and synthetic transaction monitoring (STM) for both web and mobile-based end users.
  • Application discovery, tracing and diagnostics (ADTD)—ADTD tools automatically discover your application topology, map services, and trace user requests as they navigate and traverse through an application.
  • Application Analytics (AA)—Application analytics is the ability of the APM product to assist in troubleshooting an application's performance problems. Application analytics enables the automated detection of the source (or root cause analysis) of performance anomalies for HTTP/S transactions supported by Java and .NET application servers, web browsers and mobile applications through machine learning, statistical inference and other methods.

APM solutions addressing these capabilities should include the following capabilities:

APM Digital Experience Monitoring

  • Real-user monitoring of applications via code injection performed by server-based agents
  • Support for various synthetic application monitoring tests including HTTP server, page loads, and other web server transactions
  • End-user experience monitoring through the capture of data about how end-to-end application availability, latency, and quality of service impact the user experience

APM Application Discovery, Tracing, and Diagnostics

  • Agentless real user monitoring (RUM)
  • Application path snapshots
  • Automated discovery of web application servers, Java and .NET application servers, as well as other middleware
  • Automated detection of the source (or root cause analysis) of performance anomalies for HTTP/S transactions supported by Java and .NET application servers through machine learning, statistical inference or other methods

Ideal Solution Characteristics

A useful application performance analytics best practice, in general, is to collect raw data from the APM environment that allows for a broad variety of performance issues to be examined. Many performance monitoring tools will summarize or roll-up the detail data for reporting and archiving purposes but fall short in answering long-term trending questions required for accurate troubleshooting, so capturing full underlying detail is key to fast problem resolution.

APM solutions that implement a big data approach makes it much easier to access detailed performance data across more prolonged periods of time, facilitating accurate long-term analysis.

NPM Complements APM

APM alone isn't sufficient to troubleshoot application issues, since applications rely on IT and network infrastructure for delivery. APM solutions are complemented by Network Performance Monitoring (NPM) functions that monitor, diagnose and generate alerts for:

  • Network endpoints—On-premise servers, virtual machines, storage systems or anything with an IP address by measuring these components directly, in combination with a network perspective, including cloud-hosted and wireless endpoints
  • Network elements—Routers, switches, and other network devices that includes SDN and NFV components
  • Network links—Connectivity between network-attached infrastructure

Network infrastructure KPI metrics provide additional troubleshooting detail to better distinguish between application and network problems.

Multi-Layer Correlated Insights for Application Delivery

ThousandEyes offers DEM that integrates synthetic app-layer monitoring with network performance metrics, Path Visualization, Internet routing and Internet outage insights. ThousandEyes DEM addresses the challenges of modern application delivery that depends on complex, external Internet and cloud ecosystems that aren't directly under the control of IT. ThousandEyes provides visibility into experience for:

  • Public cloud, hybrid cloud and multi-cloud adoption
  • Customer-facing digital experience delivery
  • Employee-facing digital experience delivery
  • SaaS adoption
  • Modern hybrid and SD-WAN transformation