Even with all the coffee or energy drinks in the world, humans require sleep. Doctors suggest a good seven to eight hours a night for optimum performance. But machines don’t have the same restrictions. No rest is required, no holidays. They are generally on 24/7. And that means they are sensing, analyzing and transmitting data 24/7.
another way: there are more than 7 billion people in the world. Each of those
people, on average, has five connected devices. There are literally billions of
machines around the world to keep those devices running. Quite frankly, those
machines need to run 24/7 to keep up
with demand. That is the stuff of machine-to-machine dreams.
the devices outnumber people. When all of those devices and computers start
talking to each other, that creates an extreme amount of stress on any network.
In 2018 and beyond, we see this stress getting only more profound. Not only
that, but it’s also a growing worldwide market. IDC
predicts the market could reach $7 trillion by 2020.
the immediate future in 2018, we see data centers affected in the following
three ways when it comes to machine-to-machine communication:
the foundation for 5G:
Yes, that happens in data centers, too. All of the devices that need to
communicate to each other and humans will drive a massive amount of fiber,
especially as we look to 5G coming to market in the next five to 10 years.
There is much to be done behind the scenes even before that happens. Wireless
networks need a lot of “wired” assets to effectively deliver fiber backhaul to
the core and edge. Densification of cell sites (small cells, for example) is
also required to enable 5G. Additionally, we’ll see several types of powering
solutions come to the market, allowing operators to power up many devices at
the edge of the network in a cost-efficient way.
can process information nearly as fast as they receive it. Humans can’t. In the
data center in particular, decisions are made instantaneously, and there needs
to be a strong network backbone to support. It’s a change from data centers
past that simply acted as storage for data. Now they are computing, analyzing
and processing information, and they need to do it in real time. IDC sees the
“modernization” of data centers as one of its top predictions of 2018, making
“heavy use of predictive analytics to increase accuracy and reduce downtime.”
density and speed:
Deploying copious amounts of fiber is a best-case solution. But it’s not always
feasible. The most efficient scenario is to deploy high density fiber from the
get-go to allow machine-to-machine conversations to happen fast. A modular,
high-speed platform that can support multiple generations of equipment is the
a machine learning example used repeatedly…but it’s a good one. Self-driving
cars are becoming a reality, thanks to the pilot
project in Pittsburgh. Backed by a strong network and nearly perfect
sensors, the project is going well. The cars are able to process the data much
quicker than any human could. It’s like an entire data center on wheels!
CLICK TO TWEET: Memo to 2018: Let's stay awake at the wheel in our data centers.
the cars stay sober. They don’t text and drive. They stay awake at the wheel.
And they have a quicker reaction time. As long as the cars make the right decision at the right time, they can drive well into the
have been driving for a century. We all make mistakes, and should a computer
replace a human behind the wheel? What about compassion and empathy, emotions
that a computer or machine can’t feel? Is the human element lost in this?
certainly depends on your perspective. Machines are only as good as their
algorithms and programming. They are vulnerable to manipulation (hacking) by
humans or perhaps even other machines. In fact, Gartner
predicts that by 2022, most people in mature economies will consume more
false information than true information. It even goes as far to say that false
information will “fuel a major financial fraud.” With more devices than people
in the world, it’s fair to say that we become more vulnerable to hackers and
data thieves. There are certainly data privacy concerns. There’s a school of
thought that believes machines will take over jobs that humans could at one
time only do. However, the same Gartner report suggests that machine learning
will create 2.3 million jobs by 2020 while eliminating only 1.8 million jobs.
There are still plenty of jobs for us humans, though they may certainly be
different than what we are doing today.
will also be problems. The world will not run on robots alone anytime soon.
Machine-to-machine technology requires a change in mindset, giving up control.
There will be problems, and it certainly won’t be perfect. But it’s a huge step
forward in this “fourth
industrial revolution.” It’s a great time to be part of this ever-growing
industry, and CommScope is ready for 2018 and beyond.