C&L_December 2017 (1)

Insight
mechanical systems. When data is selfaware,
it can be tagged so it controls
who sees what parts of it and when,
without additional time consuming
and potentially error prone human
intervention to subdivide, approve and
disseminate the valuable data.
2. Virtual machines
become “rideshare”
machines
It will be faster, cheaper and more convenient
to manage increasingly distributed
data using virtual machines, provisioned
on webscale infrastructure,
than it will be on real machines.
This can be thought of in terms of
buying a car versus leasing one or
using a rideshare service like Uber
or Lyft. If you are someone that hauls
heavy loads every day, it would make
sense for you to buy a truck. However,
someone else may only need a certain
kind of vehicle for a set period of time,
making it more practical to lease. And
then, there are those who only need
a vehicle to get them from point A
to point B, one time only: the type of
vehicle doesn’t matter, just speed and
convenience, so a rideshare service the
best option.
This same thinking applies in the
context of virtual versus physical
machine instances. Custom hardware
can be expensive, but for consistent,
intensive workloads, it might make
more sense to invest in the physical
infrastructure. A virtual machine
instance in the cloud supporting variable
workloads would be like leasing:
users can access the virtual machine
without owning it or needing to know
any details about it. And, at the end of
the “lease,” it’s gone. Virtual machines
provisioned on webscale infrastructure
(that is, serverless computing) are
like the rideshare service of computing
where the user simply specifies the
task that needs to be done. They leave
the rest of the details for the cloud
provider to sort out, making it more
convenient and easier to use than traditional
models for certain types
of workloads.
3. Data will grow faster
than the ability to
transport it...and that’s ok!
It’s no secret that data has become
incredibly dynamic and is being generated
at an unprecedented rate that will
greatly exceed the ability to transport
it. However, instead of moving the
data, the applications and resources
needed to process it will be moved to
the data and that has implications for
new architectures like edge, core, and
cloud. In the future, the amount of data
ingested in the core will always be less
than the amount generated at the edge,
but this won’t happen by accident.
It must be enabled very deliberately
to ensure that the right data is being
retained for later decision making.
For example, autonomous car manufacturers
are adding sensors that will
generate so much data that there's no
network fast enough between the car
and data centers to move it. Historically,
devices at the edge haven’t created
a lot of data, but now with sensors
in everything from cars to thermostats
to wearables, edge data is growing
so fast it will exceed the capacity of
the network connections to the core.
Autonomous cars and other edge
devices require real-time analysis at
the edge in order to make critical inthe-moment
decisions. As a result, we
will move the applications to the data.
4. Evolving from “Big
Data” to “Huge Data” will
demand new solid statedriven
architectures
As the demand to analyze enormous
sets of data ever more rapidly increases,
we need to move the data closer
to the compute resource. Persistent
memory is what will allow ultra-low
latency computing without data loss;
and these latency demands will finally
force software architectures to change
and create new data driven opportunities
for businesses. Flash technology
has been a hot topic in the industry,
however, the software being run on it
didn’t really change, it just got faster.
This is being driven by the evolution
of IT’s role in an organization. In the
past, IT’s primary function would have
been to automate and optimize processes
like ordering, billing, accounts
receivable and others. Today, IT is integral
to enriching customer relationships
by offering always-on services,
mobile apps and rich web experiences.
The next step will be to monetize the
data being collected through various
sensors and devices to create new business
opportunities and it’s this step
that will require new application architectures
supported by technology like
persistent memory.
5. Emergence of decentralized
immutable mechanisms
for managing data
Mechanisms to manage data in a trustworthy,
immutable and truly distributed
way (meaning no central authority)
will emerge and have a profound
impact on the datacenter. Blockchain is
a prime example of this.
Decentralized mechanisms like
blockchain challenge the traditional
sense of data protection and management.
Because there is no central point
of control, such as a centralized server,
it is impossible to change or delete
information contained on a blockchain
and all transactions are irreversible.
Current datacenters and applications
operate like commercially managed
farms, with a central point of control
(the farmer) managing the surrounding
environment. The decentralized
immutable mechanisms for managing
data will offer microservices that
the data can use to perform necessary
functions. The microservices and data
will work cooperatively, without overall
centrally managed control
–Mark Bregman, CTO, NetApp outlines 5 key
CTO predictions for 2018.
December 2017 | CIO&LEADER
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