APM’s Service-Oriented Recipe for Success

Nastel Comments: A lot of companies need the capabilities to capture and monitor end-user experience for web-based applications as part of its overall delivery of business transaction monitoring to perform the following:

-Capturing real-time, operational performance metrics
-Measuring technology components that impact the end user

APM's Service-Oriented Recipe for Success

Many of us are grappling with the modern demands of digital business: developing new mobile apps, evaluating security in the face of IoT, moving to hybrid clouds, testing approaches to defining networks through software. It’s all part of the hard trend toward service-oriented IT, with a primary goal of delivering a premium user experience to all your users—internal, partner or customer—with the speed, quality and agility the business demands.

How do you meet these elevated expectations? As modern data centers evolve rapidly to tackle these agility demands, network and application architectures are becoming increasingly complex, complicating efforts to understand service quality from infrastructure and application monitoring alone. Virtualization can obscure critical performance visibility at the same time complex service dependencies challenge even the best performance analysts and the most effective war rooms. Although this situation may read like a recipe for disaster, within are secrets to success.

Service Quality Is in the Eye of the End User

Remember the adage “beauty is in the eye of the beholder”? The same idea applies here; service quality is in the eye of the user. It’s hard to argue with that sentiment, especially when we consider the user as the face of the business. So, of course, to understand service quality we should be measuring end-user experience (EUE), where EUE is defined as the end-user response time or “click to glass.” In fact, EUE visibility has become a critical success factor for IT service excellence, providing important context to more effectively interpret infrastructure performance metrics.

  • These agent-based solutions may be unavailable or unsuitable for operations teams
  • Not all Java and .NET apps will be instrumented
  • Some agent-based solution do not measure EUE
  • Some agent-based solutions only sample transaction performance (let’s call this some user experience, or SUE)
  • Many application architectures don’t lend themselves to agent-based EUE monitoring

An Important Lesson

For these and other reasons, IT operations teams have often focused on more approachable infrastructure monitoring—device, network, server, application and storage—with the implication that the whole is equal to the sum of its parts. The theory was (or still is) that by evaluating performance metrics from all of these components, one could assemble a reasonable understanding of service quality. The more ambitious IT teams combine metrics from many disparate monitoring solutions into a single console, perhaps with time-based correlation if not a programmed analysis of cause and effect. We might call such a system a manager of managers (MOM), or business service management (BSM). Some still serve us well, likely aided by a continual regimen of care and feeding; still more have faded from existence. But we have learned an important lesson along the way—namely, EUE measurements are critical for IT efficiency for many reasons, such as

  • Knowing when there is a problem that affects users
  • Prioritizing responses to problems on the basis of business impact
  • Avoiding chasing problems that don’t exist, or deprioritizing those that don’t affect users
  • Troubleshooting with a problem definition that matches performance metrics
  • Knowing when (or if) you’ve actually resolved a problem

Complexity Drives APM Evolution

Performance-monitoring capabilities continue to mature, evolving from real-time monitoring and historical reporting to more sophisticated fault-domain isolation and root-cause analysis, applying trending or more-sophisticated analytics to predict, prevent or even take action to correct problems.

One of the compelling drivers is the increasing complexity—of data center networks, application-delivery chains and application architectures. And with this complexity comes an increasing volume of monitoring data stressing, even threatening, current approaches to operational performance monitoring. It’s basically a big-data problem. And in response, IT operations analytics (ITOA) solutions are coming to market as an approach to derive insights into IT system behaviors—including but not limited to performance—by analyzing generally large volumes of data from multiple sources. The ITOA market insights from Gartner tell an interesting story: spending doubled from 2013 to 2014 to reach $1.6 billion, while estimates suggest that only about 10% of enterprises currently use ITOA solutions. That’s a lot of room for growth!

Read the source article at datacenterjournal.com

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But there is hope…GC can also function as an essential early warning system, letting you know about impending performance problems.  The trick here is to analyze the behavior of GC on your system and relate that to normal vs. abnormal application performance.   How do you do that?  Glad you asked…

Our CTO will be presenting a brief TechTalk on this subject.

In only 18 minutes he will cover 3 ways to detect Java application performance trends.

Please join us at: 3 Ways to Detect Java Application Performance Trends

Learn how to use real-time analytics to determine trends in application response times

3 Reasons to attend:
1) Learn how to spot emerging bottlenecks in applications spanning multiple JVMs
2) Find out how to reduce false-positives by eliminating static thresholds
3) Hear about measuring Garbage Collection behavior as a way to provide early warning for application performance