Intel, Cloud Telemetry and Jevons Paradox

I recently heard on a tech podcast that Intel were contributing to the open source community via their open telemetry framework called ‘Snap’. Snap is designed to collect, process an publish system data through a single API.

Here’s a quick diagram along with the project goals taken form the Github page that will provide you with a simple overview:

Project Goals:

Empower systems to expose a consistent set of telemetry data
Simplify telemetry ingestion across ubiquitous storage systems
Improve the deployment model, packaging and flexibility for collecting telemetry
Allow flexible processing of telemetry data on agent (e.g. filtering and decoration)
Provide powerful clustered control of telemetry workflows across small or large clusters

The Intel representative went very deep and wide on how Snap works from both operational and development perspectives. All very exciting, especially when a tech company like Intel uses it’s resources to contribute to open source. Naturally, like many others I do get a little suspicious. Why are they doing this? What’s in it for them? What’s the hidden agenda?

As I was thinking about this, one of the show hosts asked the Intel rep the self confessed cynical question… he stated that it all sounded very useful and technically interesting but why would Intel spend time on this ‘open source’ stuff? What’s in it for them? Intel responded quite honestly about their motives. They said it wasn’t ‘Rocket Science’. They want consumers to buy more silicon… more Intel chips, it’s no secret. They then went on to talk about ‘Jevons Paradox’ which I found very interesting. Intel’s Snap wasn’t an expensive project using a lot of their expertise and engineering resources with no return on investment, it was a project that supports a business model.

Jevons Paradox, sometimes referred to as ‘Jevons Effect’ states that the use of a resource tends to increase rather than diminish the more efficient it becomes. The following diagram clearly shows the concept using the cost of fuel, taken from the Jevons Paradox Wikipedia page:

Using this proven theory, Intel believe that the easier they make the monitoring and the analysis of their chipsets via an open source telemetry framework, the more they will be consumed, meaning customers will buy more Intel chips. The open plugin model means that community and propriety plugins can be loaded into Snap so it can be easily be extended and tailored to meet even the far left field business needs in the analytics space.

The team over at Grafana were quite impressed by Snap, so much so that they created Snap datastore. If you are yet to hear of Grafana an what it does, I briefly discuss it in a previous article found here. In short, it’s an open source dashboard for system monitoring. Having the Snap datastore in Grafana means you can natively create monitoring tasks and metrics without having to jump through a load of hoops.

CPU monitoring in Grafana using the Snap datastore connector: