The Doppler Quarterly Special Edition 2019 | Page 94
data centers requires less networking, so we shaved $600
million off our carrier fees. Eliminating 100 instances of SAP
saved about $100 million in licenses. A remaining $1 bil-
lion-plus of savings came from rationalizing our application
portfolio from 7,000 to 1,800, and reducing IT headcount
from 20,000 to 2,000.
Just Getting Started
At this point, we were pretty happy with our progress. We
were running the company’s IT with six shiny new data cen-
ters, and costs seemed like they were at a manageable level.
We didn’t realize it at the time, but we were only getting
started.
Within 18 months of building the new data centers, we
began running out of capacity. We decided to plug the gap
with EcoPODs, modular, containerized data centers we
manufacture, which each generate a megawatt of power.
We set up an EcoPOD next to each data center and planned
to add two a year for the next five years. They were an
expense, but far less costly than new data centers.
Our team was happy with our strategy, but our leader was
not. Meg Whitman, HPE’s CEO at the time, questioned us
about our plan and its metrics, such as CPU utilization. At
the time our utilization was about 10 percent, just below the
industry average. She said she would like to see it in the 80
percent range.
We had work to do. When we looked at our environment, we
saw that we had about 10,000 virtual machines (VMs) that
essentially were not being used. The reason? Developers
were hoarding them. On average, it took 21 days to get a
VM approved. Developers didn’t want to wait that long.
They wanted to have capacity on hand to start developing
immediately, as they can today on a PaaS. So, they ordered
extras – dozens of them.
This got us thinking in terms of a larger transformation.
Taking the Next Step
The first thing we did was set up a cloud-like system we
referred to as highly automated platform provisioning. It
was not really a cloud. There were no APIs, just automation.
92 | THE DOPPLER | SPECIAL EDITION 2019
Developers could go to a portal and order up cores, storage,
memory, an operating system, middleware databases and
load balancing. Twenty minutes later, they would have an
environment.
This helped us to do a better job managing our IT environ-
ment. We identified VMs that were overprovisioned, used
automation tools and drove our utilization up to 30 percent.
We were able to eliminate the use of EcoPODs and shrink
the number of data centers down to four.
The next step was to move to the cloud. We started by cre-
ating an OpenStack cloud for cloud native development
projects and then started brokering workloads to Azure.
The positive response was immediate. People were tired of
the old way of relying on on-premises resources, so we put
together a project to move the majority of our workloads to
the cloud.
Our plan called for the dissemination of workloads into four
main buckets. The first would house about 10 percent of
our applications – traditional IT resources, such as SAP
HANA appliances and IBM mainframes, which would have
to remain on premises. The remainder would go to the
OpenStack VM (10 percent), to the public cloud (60 per-
cent) and to SaaS applications (20 percent).
In the end, we moved far more workloads to OpenStack
(about 50 percent) and far fewer to the public cloud (10
percent). The problem was we didn’t have a good plan in
place to manage costs throughout the process. Public cloud
costs were swelling, and we weren’t shutting off VMs
quickly enough to harvest the savings so we could move
fast on public cloud deployments. We got scared and scaled
back our cloud efforts.
Understanding the “Why”
This is something CTP’s business model could have helped
with. HPE had set a goal to move 60 percent of our work-
loads to the public cloud, but we did not consider the many
factors involved in making migration decisions. CTP helps
customers understand the “why.” Our problem was, we had
no clear idea of why we should move workloads into certain
buckets at certain times. We just wanted to do it.