F5 Friday: Performance Analytics–More Than Eye-Candy Reports
#v11 Application-centric analytics provide better visibility into performance, capacity and infrastructure utilization
Maintaining performance and capacity of web sites and critical applications – especially those of the revenue-generating ilk – can be particularly difficult in complex environments.
The mix of infrastructure and integration can pose problems when trying to determine exactly where capacity may be constrained or from where performance troubles are originating. Visibility into the application delivery chain is critical if we are to determine where and at what points in the chain performance is being impaired or constraints on capacity imposed, perhaps artificially.
The trouble is that the end-user view of application performance tells you only that a site is or isn’t not performing up to expectations. It’s myopic in that sense, and provides little to no assistance in determining why an application may be performing poorly. Is it the Internet? An external component? An integration point? A piece of the infrastructure? Is it because of capacity (load on servers) or a lack thereof? It’s easy to point out a site is performing poorly or has unacceptable downtime, it’s quite another to determine why such that operations and developers can resolve the problem.
Compounding the difficulties inherent in application performance monitoring is the way in which network infrastructure – including application delivery controllers – traditionally report statistics related to performance. It’s very, well, network-focused – with reports that provide details on network-oriented objects such as Virtual IP addresses and network segments. This is problematic because when a specific application is having issues, you want to drill down into the application, not a shared IP address or network segment.
And you wouldn’t be digging into performance data if you didn’t already know there was a problem. So providing a simple “total response time” report isn’t very helpful at all in pinpointing the root cause. It’s important to be able to break out, by application, a more granular view of performance that provides insight into client, network, and server-side performance.
BIG-IP v11 introduced iApp, which addresses a real need to provision and manage application delivery services with an application-centric perspective. Along with iApp comes iApp Analytics, a more application-centric view of performance and capacity-related data.
This data provides a more holistic view of performance across network, client and server-side components that allows operations to drill down into a specific application and dig around in the data to determine from where performance problems may be originating. This includes per URI reporting, which is critical for understanding API usage and impact on overall capacity. For organizations considered more advanced architectural solutions to addressing performance and capacity, this information is critical. For example, architecting scalability domains as part of a partitioning or hyper-local scalability pattern will need to have a detailed understanding of per-URI utilization. Being able to tie metrics back to a specific business application enables IT to provide a more accurate operational cost to business stakeholders, which enables more a accurate ROI analysis and proactive approach to addressing potential growth issues before they negatively impact performance or worse, availability.
Transactions can be captured for diagnosing customer or application issues. Filters make it easy to narrow down the results to specific user agents, geographies, pools or even a client IP. The ability to view the headers and and the response data can shave valuable time off identifying problems. Thresholds on a per application, virtual server or pool member can be configured to identify if transactions or throughput levels increase or decrease significantly. This can be an early warning sign that problems are occurring. An alert can be delivered via syslog, SNMP or email when these thresholds are exceeded.
Viewing of analytics can be accomplished through iApp application-specific view which provides the context of the associated business application. Metrics can also be delivered to an off-box SIEM solution such as Splunk.
While detailed, per-application performance and usage data does, in fact, make for very nice eye-candy reports, such data is critical to reducing the time associated with troubleshooting and enabling more advanced, integrated scalability-focused architectures to be designed and deployed. Because without the right data, it’s hard to make the right decisions for your applications and your infrastructure.
Happy Performance Monitoring!
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