A superior customer experience (CX) is built on accurate and timely application performance monitoring (APM) metrics. You can’t fine-tune your apps or system to improve CX until you know what the problem is or where the opportunities are.
APM solutions typically provide a centralized dashboard to aggregate real-time performance metrics and insights to be analyzed and compared. They also establish baselines to alert system administrators to deviations that indicate actual or potential performance issues. IT teams, DevOps and site reliability engineers can then quickly identify and address application issues.
Application performance monitoring is the initial phase of application performance management. Monitoring tracks app performance and enables the management of that app. An APM solution brings administrators the instrumentation tools needed to quickly gather data and conduct root cause analysis; they then isolate, troubleshoot and solve that problem.
There are a number of metrics you can choose from, but we recommend focusing on these eight metrics to reap the most benefits within your IT organization.
Let’s start with application performance index (Apdex) and service level agreement (SLA) scores, since they are the foundation of superior customer experience. The speeds and feeds you’ll measure are the specific aspects that ought to add up to fast performance, but they are the means, not the end. Happy customers are your goal—hopefully leading to increased sales.
The Apdex and SLA scores are the most popular way to view end-user experience monitoring. The Apdex score tracks the relative performance of an app by specifying a goal for the time a web request or transaction should normally take. The SLAs are the metrics in your customer contract and anything lower than the defined SLA risks a drop in CX (and possibly predefined penalties).
This is the most basic metric: Are the lights on? You are monitoring and measuring if your application is online and available. Most companies use this to measure service level agreement (SLA) compliance. Uptime is often a shorthand for assessing overall system reliability and health. Excessive downtime can negatively impact user satisfaction for organizations delivering online services. For a web application, you can verify availability with a simple, regularly scheduled HTTP check.
A high percentage of CPU capacity being used by an application can be a sign of a performance problem. A sudden spike in CPU usage can result in slower response times. Fluctuations in demand for an app might also be an indication that you need to add more application instances. A general rule is if CPU usage exceeds 70% more than 30% of the time, you could be running out of CPU capacity.
Resource usage can also include memory and disk usage. Tracking RAM helps identify memory leaks that could lead to failure or the need for greater memory. Disk usage metrics can help prevent an app from running out of persistent storage, which could cause it to fail. High disk usage could also be a sign of inefficient backend data storage or faulty data retention policies.
Your APM metrics software should monitor applications to record the percentage of requests that result in failures. This helps to identify and prioritize the resolution of issues that impact the user experience. Application errors can include server errors, a 404 response or timeout in a web app. You can configure your APM solution to send notifications when an error rate goes above a set parameter. For example, send an alert when 2.5% of the previous 25 requests have resulted in an error.
Garbage collection (GC) can improve performance by identifying and eliminating the ongoing heavy memory usage of Java or other languages. The good news is that GC automation reclaims memory devoted to unused or redundant objects or data that are no longer being used by an application. Unused objects or data are deleted and live objects are copied to a later-generation memory pool. This is a metric you want to keep in the happy middle. If GC is run too often, it might require too much overhead; but if GC is not run often enough, then your system could be left with too little memory.
Tracking instances enables you to scale your application to meet actual user demand, based on how many app or server instances are running at any time. This can be especially important for cloud applications. Auto-scaling can help you ensure modern applications scale to meet demand and save budget during off-peak hours. This can also create infrastructure-monitoring challenges. For example, if your app automatically scales up on CPU usage, you might not ever see your CPU usage rise—instead, you could see the number of server instances rise too far, along with your hosting bill.
You can measure the traffic received by an application to identify any significant decreases, increases or coinciding users. Correlating request rates with other application performance metrics will help you understand the scalability of your software applications. APM software can also monitor traffic to identify anomalies. User monitoring showing an unexpected increase in requests could be a denial of service (DoS) attack. A large number of requests from the same user could be an indication of a hacked account. Even unusually low requests could be bad—inactivity or no traffic at all could mean a failure in almost any part of your system.
By tracking the average response time to a request—that is, how long it takes an application to return a request for resources—you can assess app performance. These requests can be inclusive of transactions initiated by end-users, such as a request to load a web page, or can include internal requests from one portion of your application to another, such as a process or microservice requesting data from disk or memory. The total response time includes server response time (the time it takes your server to process a request) plus network latency (the total time it takes the request to move across the network).
A related metric is page load time, which measures the time it takes a webpage to load into a browser. Tracking page load times enables your application performance monitoring tools to identify the issues causing slow-loading pages and then improve the digital experience. Slow page loads can mean page abandonment and lost business. APM solutions can be set for a baseline of performance for this metric and then alert you when that benchmark is not met.
For those who are looking for a more comprehensive set of metrics related to application performance monitoring, you might want to consider the following metrics:
IBM Instana Observability provides real-time observability that everyone—and anyone—can use. It delivers quick time to value while ensuring your observability strategy can keep up with the dynamic complexity of today’s environments and tomorrow’s. From mobile to mainframe, Instana supports over 250 technologies and growing.
Learn more about application performance monitoring with IBM Instana
The post Top 8 APM metrics that IT teams use to monitor their apps appeared first on IBM Blog.
One of the significant challenges statisticians and data scientists face is multicollinearity, particularly its most…
Artificial intelligence is not just altering the way we interact with technology; it’s reshaping the…
This post was co-written with Mickey Alon from Vidmob. Generative artificial intelligence (AI) can be…
Prompt Poet allows you to ground LLM-generated responses to a real-world data context, opening up…
NASA has three more operational Starliner missions on the books. It hasn't decided whether it…
When training a machine learning model, you may sometimes work with datasets with a large…