Industry executives and experts share their predictions for 2023. Read them in this 15th annual VMblog.com series exclusive.
Organizations Will Design for Observability at the Architectural Level
By Avi Freedman, CEO and Co-founder,
Kentik
As enterprise organizations continue to adopt
a myriad of services and platforms into the different layers of their systems, enabling
visibility into how these systems function is paramount.
In 2023, more organizations will achieve
success by designing for observability at the architectural level. For a modern
system to function at scale, you must be able to see what's happening inside of
it. After all, a properly designed architecture should illuminate and not
obfuscate.
Using telemetry to detect and
isolate issues
The easiest way to avoid eventual visibility
problems is to design the different layers around providing rich telemetry -
whether OpenTelemetry
or not.
When things go wrong with a piece of a complex
system, issues tend to spill out and cause problems in other areas. Telemetry
data gives you the data that can help detect issues and isolate the root cause
of these issues, making remediation much easier (and less panic-inducing.)
An example - services meshes can be great for
visibility. If built correctly, service meshes help run microservices at scale
by providing a more flexible release process, availability and resiliency, and
secure communications between services. However, unnecessarily using service
meshes, or without the right telemetry, can make it harder to observe and
understand your applications and infrastructure.
Implementing telemetry pipelines
A second related trend we expect to grow in
2023 is the use of telemetry pipelines to underpin a broad range of operational
systems.
No "system or platform to rule them all" is
available today, so designing your architecture to enable sending telemetry to
multiple systems is necessary. Between infrastructure-focused platforms,
application-focused platforms, and operational systems, there is no shortage of
services that benefit from telemetry. This includes data lakes, internal AI/ML
projects, observability platforms, BI platforms that look at revenue impact,
and many more.
A system designed to accept and collect broad
ranges of telemetry can copy, massage, and send it to many different systems,
adapting it as needed for the advantages and limitations of those platforms.
There is a huge amount of innovation in the open source and commercial world
around this design pattern, and it is part of most observability architectures
we are seeing now.
Enabling systems to be
self-driven
A third related trend that great telemetry
collection enables is the ability to enable systems to be increasingly (though
still not totally) self-driven. Whether it's applications, or enterprise or
service provider networks, we're seeing an increasing number of customers use
telemetry collected and shared on busses to carefully enable control loops for
auto-scaling and remediation.
Of course - you need to be careful with all
feedback loops. It can be tricky to only add stability. The best architects
always remember the Tacoma Narrows Bridge, and complex distributed
systems have their own harmonic frequencies to avoid.
Observability as a means for
success
I've said this before, but as observability
solidifies and modern architecture scales out to using even more services, it's
more apt than ever. Designing for observability at the architectural level is
crucial for understanding how systems function. This empowers organizations to
understand and fix problems faster and ultimately achieve better results, which
is critical to staying competitive in the market.
##
ABOUT THE AUTHOR
Avi Freedman is the
co-founder and CEO of Kentik, with decades of experience
as a leading technologist and executive in networking. Freedman was with Akamai
for over a decade as VP of Network Infrastructure and then as Chief Network
Scientist. Before that, he started Philadelphia's first ISP (netaxs) in 1992,
later running the network at AboveNet and serving as CTO for ServerCentral.