DATA ER
CENT5
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Data Center Management and Control:
Status Quo or Self-Healing?
Participants in Data Center 2025 almost all agreed that progress will be made in data center
management and control, but there was no clear consensus on how far it will advance. Here,
experts from Emerson Network Power and DLB Associates champion each end of the spectrum.
Resistance to Change
Will Limit Advancement of
Data Center Management
A Revolution in Data Center
Management is on the Horizon
Steve Hassell
Mark Monroe
President, Data Center Solutions, Emerson Network Power
There is increasing pressure on the data center to achieve
new levels of efficiency without compromising availability.
Management platforms are going to be key to accomplishing that, and I expect self-healing and self-optimizing
data centers to be viable by
2025.
50%
If you define self-healing as
the ability to adapt to failures
without impacting application
availability, self-healing
capabilities already exist.
45%
40%
35%
30%
25%
Chief Technology Officer, DLB
Associates
While we operate in a very fast-paced environment,
the data center industry is, at its core, risk adverse and
resistant to change. For that reason, I believe by 2025
we may see industry leaders on the brink of operating
self-healing facilities, but
the vast majority of
colocation and enterprise
facilities will be manually
43%
operated very similarly to
the way they are today.
29%
Full visibility, to me, means
that every parameter that
15%
makes a difference to the
Thanks to virtualization,
10%
cost of operation or the
many organizations have the
5%
reliability of services is
capability—and the capacity—
3%
0%
Same as today Full visibility Self-optimizing Self-healing
available to the operators
to shift loads within the data
in the timeframe necessary.
center without impacting
Data Center 2025: Which of the following best describes what you
For the most part, we
think data center management and control will be like in 2025?
availability. What they lack is
haven’t, as an industry,
the visibility across systems
defined the set of parameters
to understand the impact
that are required for optimization or the ideal
of those changes and adapt support systems based on
frequency with which they should be collected.
them.
20%
That’s why the “single pane of glass” that DCIM promises
is so critical to the future.
The primary technical challenge is that the data center
is a Tower of Babel—every system is communicating in a
different language.
Continued
25%
And, even if we were able to deliver an integrated view
of data, most organizations aren’t structured to take
advantage of it. I agree with Steve that management silos
may be the biggest challenge that we as an industry
face in achieving more sophisticated levels of data
center management.
Continued
Until recently, there hasn’t been a way to translate the
data coming, for example, from the power system so
that it is meaningful in relation to the data coming from
the servers. As a result, managers have operated in silos.
This challenge is being addressed through a new
generation of data center devices that consolidates data
across systems and translates it as necessary for use by
the management system. Additionally, multi-vendor
initiatives such as Redfish are creating common
communication protocols that will ultimately reduce the
need for translation.
As data comes together, so will management teams,
presenting a potentially larger challenge than aggregation
and translation. Management silos must be broken down
and a holistic mindset nurtured to realize the potential of
the technology.
Once that happens, the industry will be well positioned
to address self-optimization. This will require the
development of sophisticated optimization algorithms
that do not exist today. This is a complex challenge but
one that will be addressed within the context of broader
efforts to use data to drive competitive advantage. I
don’t expect the data center industry to be a laggard in
the use of data to optimize operations.
Conquering these challenges and achieving selfoptimization will give us the ability: to maintain optimum
conditions across the data center at all times, driving
down operating costs; to respond to failures in a way
that is transparent to users, improving our value to the
business; and to maximize the utilization of resources,
reducing capital costs.
As a former CIO I can only say the time can’t come soon
enough.
But I am not as optimistic that organizations will be
successful in breaking down these silos. That will occur
only when a new generation of data center managers
raised in integrated data center environments assumes
leadership roles in a majority of data centers.
Achieving self-optimization and self-healing presents
its own set of technical challenges. Self-optimization
requires a high degree of intelligence built into data
center systems, because we are never optimizing
against just one variable. Optimization requires
balancing and prioritizing sometimes competing
variables, and that introduces a high degree of
complexity we are not close to addressing today.
Self-healing – the ability to recognize, isolate and
rectify a failure in a system—may be even more of
a complex challenge. I contend that developing a
self-healing data center is a more difficult engineering
challenge than developing a self-driving car. It’s likely
that we see autonomous cars on the road by 2025,
but I don’t think they will be the norm.
Even though the technical challenges are huge, I
don’t think technology will hold back the evolution
of data center management. These are difficult but
solvable problems.
The reason we won’t see self-optimizing or self-healing
data centers by 2025 is because data center
operators won’t have enough trust in these systems
to rely on them. Remember, these are men and
women whose primary role is to ensure the availability
of data center services. They are not going to turn
that responsibility over to machines until they are
absolutely confident those machines can be trusted.
That will happen eventually, but it will take a lot
longer than ten years.
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