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The Autonomic Computing Journey: Let Me Waze That for You

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The Autonomic Computing Journey: Let Me Waze That for You

In the next step of the journey, the idea of collecting and displaying data from millions of points in real time becomes a reality with Google's Waze traffic layer.

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Have you heard of the site LMGTFY? It is an acronym for Let Me Google That For You as a sort of dig when folks ask questions of their IT colleagues that they could have used Google for. The somewhat witty (read: passive-aggressive) response is to use LMGTFY to run the query and give that result to the person who asked the question.

Since our last post covered how Watson was tackling areas of natural language and much more, we can take the same LMGTFY concept out on the road, literally.

How Much Do I Love Thee? Let Me Count the Waze

Ok, Shakespeare didn’t pen the phrasing using the Waze name, but what we have come to love is the wildly popular Waze platform. Google Maps is probably the de facto favourite mapping system for many. I know that it’s mine because I’ve been led down the wrong path by a few other mapping systems in the past.

Google also opened the door to something else that changed the way we think about navigation by adding traffic to the equation. Google traffic data is fed by the various mobile devices and systems that create inputs showing where traffic patterns are changing in real-time. This gives you the ability to add a traffic layer on top of the existing Google Map system and to use it to derive the duration of your trip.

waze google map traffic

Then, along came a company named Waze. The Waze model uses crowdsourced data such as traffic stops, police checkpoints, accidents, road work, and any other slow down that could be reported via the app and then shared among every Waze user.

If You Can’t Beat ‘em, Join ‘em

Waze had such a perfect fit with what Google was trying to accomplish that they bought them in 2013 for a reported 1.3 billion dollars. Waze continues to operate today as a subsidiary within the Google universe because they continue to integrate in a very interesting way.

This new combined system fed by device inputs (millions of devices), and now by interactive input from the community of users (potentially millions), to make the central system more intelligent. This is where the proverbial rubber hits the road as we strive to reach the next generation of computational engine.

Between Watson, and now Waze/Google Maps, we are seeing the trend towards the autonomic era. This is an era of computing that brings vast numbers of inputs into centralized engines and presents the ability to consume the data both via apps and by any number of distributed systems that provide APIs and SDKs to extract usable, intelligent data.

Shifting Acceptance of Autonomic Computing 

This is also the societal pivot toward a more broad acceptance of the use of real-time systems that deliver decisions that we have had to make ourselves for decades or more. When you think of traffic, we have generations of people around the world who grew up without any electronic assistance to get from A to B.

Some of us may still have the map buried in the trunk of a car somewhere. Why? Because we still hold onto that “just in case” feeling of needing a backup. This is a long way that we’ve come from an early distrust of much of these systems altogether. In recent years, the shift towards a truly wide acceptance of systems driven by real-time data has overtaken most of the “just in case” concerns.

This is the start of an even more exciting stage in the journey towards autonomic systems. This is where we not only listen to them for advice, but as we will see in the next post, we have even let them take the wheel.

Learn how to auto-discover your containers and monitor their performance, capture Docker host and container metrics to allocate host resources, and provision containers.

data ,devices ,mobile ,google maps

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