Accelerating Cloud Operations: Optimizing the Enterprise for Speed and Agility

192 Pages·2020·7.619 MB·English

by  Michael Kavis, Ken Corless

Accelerating
Cloud Adoption
Optimizing the Enterprise for Speed and Agility
Michael Kavis
“Mike Kavis has written a gem on cloud operating models—
a must-read for companies who want to accelerate their
journey and capture value from cloud computing.”
—Jeff Dowds, former CTO, Vanguard

Accelerating Cloud
Adoption
Optimizing the Enterprise for Speed and Agility
Michael Kavis
Accelerating Cloud Adoption
by Michael Kavis
Copyright © 2020 Michael J. Kavis. All rights reserved.
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978-1-492-05595-2
[LSI]
Contents
| Foreword v
| Preface ix
PART I |
1 | Introduction: The Shift to the Cloud 1
2 | Technology 25
3 | People 43
4 | Process 57
PART II |
5 | Cloud Operating Models: Implement Your Strategy
81
6 | Platform Engagement and Support Models 105
7 | Cloud Operations and Reliability 121
8 | Conclusion: Moving Forward, Embracing Change
157
| Index 165
iii

Foreword
I am privileged to be able to write the foreword to Mike Kavis’ second book. In
early 2014, while leading the global cloud transformation for a multinational
financial services firm, I personally purchased two dozen copies of Mike’s previ-
ous book, Architecting the Cloud: Design Decisions for Cloud Computing Service
Models. I freely handed out copies to the members of the CIO Council that gov-
erned all technology programs at the bank. I also gave copies to all of the manag-
ing directors who reported to me in the Office of the CTO, and to senior leaders
in the technology organizations supporting the corporate, consumer, and capital
markets businesses. This was the first time most of these leaders were intro-
duced to concepts such as infrastructure as a service (IaaS) and platform as a ser-
vice (PaaS). I advised my colleagues to read just the first seven chapters,
particularly Chapter 3: Cloud Computing Worst Practices. The goal of that rec-
ommendation was to make everyone aware of what not to do, before we
embarked on a journey to architect and enable a global private cloud with IaaS
and PaaS service models. Enthusiasm for the new-new thing often has to be miti-
gated by an up-front recognition that there are always complex risk trade-offs
associated with any technology transformation, and some words of wisdom by
Mike Kavis, who had gone down the path before, was advice to be well consid-
ered.
In this book I see the same wisdom and caution offered for how cloud oper-
ating models are the essential element to figure out and get right before embark-
ing on a public cloud or a multicloud journey. I have spoken many times to
customers and in various public forums about the concepts of cloud economics,
cloud speed, cloud scale, and cloud safety & security. These are all key architec-
tural and operational objectives for any cloud, whether private, hybrid, public,
edge, or multicloud. The goal is to accurately quantify the economic trade-offs of
capex and opex, enable faster speed of delivery of infrastructure and applications,
v
vi |
and have the ability to scale up/down and/or scale out/in dynamically, while all
the time ensuring that resiliency, safety, and soundness are preserved through
end-to-end security, resiliency, and disaster recovery policies and mechanisms.
What is common to all of these goals is achieving maturity in cloud operating
models, and the people, processes, and technologies that all have to come
together to achieve a level of operational maturity to enable the desired business
outcomes.
The analogy Mike makes with the evolution of power utilities in the early
part of the 20th century is apropos for the type of technology transformation to
utility-like consumption models for infrastructure and software services. How-
ever, moving electrons around is actually much simpler than moving compli-
cated application workloads and their data to a cloud consumption-based
operating model from a traditional enterprise IT cost-managed operating model.
And the reality for most customers is that it is not an all-or-nothing proposition,
but some combination of a private, hybrid, public, and colocation cloud model
based on a complicated set of application and business risk-management con-
straints, as well as the economics, speed, scale, reliability, and security trade-offs
mentioned above.
Cloud computing models offer significant opportunities for businesses to
enable new types of services, delivered at a velocity and scale previously unima-
gined. The key enabler is all the software, much of it originating in the open
source community, that is able to deliver very high degrees of automation, both
by the cloud providers in delivering their services and by the cloud consumers
delivering applications built on those services. There is, however, an element of
risk concentration that must be given due consideration. Just as electrical power
utilities can incur planned or unplanned outages affecting millions of users, risk
concentration of applications and services in public cloud utilities can incur out-
ages since they usually offer only 99.9% reliability of the infrastructure. You
have to bring your resiliency models along with your applications when you
make the journey to the cloud. No application is an island unto itself and usually
has an ecosystem of services that provide the essential life-support mechanisms
that application teams have come to rely on. Many application developers do not
fully understand resiliency the way that outage-hardened IT infrastructure people
do, so they have to work together as a joint team, which may not be a natural ten-
dency. The later chapters of this book dig into the operational models that enable
you to ensure that operational resiliency and recoverability are part of the cloud
strategy, planning, and operating model. Site reliability engineering models are a
| vii
key part of the process to achieve high degrees of resiliency in a cloud world. Just
as I did when recommending Mike’s first book to my former colleagues, I
strongly recommend you read to at least Chapter 7: Cloud Operations and Relia-
bility, so that you understand the failure models and what you can do to avoid
them.
—Greg Lavender, Ph.D.
SVP & CTO VMware, Inc.
Palo Alto, CA