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Capacity Management in a Cloud Computing World
1. David S. Linthicum
David@bluemountainlabs.com
Twitter: @DavidLinthicum
Capacity Management in a Cloud Computing World
2. Shameless Self Promotion Slide
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3. FYI
• Slides will be on on
www.slideshare.net/linthicum by tonight.
– Slides are open source
• You can reach me with further questions at
david@bluemountainlabs.com.
4. 4 Myths of Capacity Management and
Cloud Computing
• Myth 1: I don’t need
capacity management
when leveraging cloud
computing.
• Myth 2: Clouds are
“elastic.”
• Myth 3: Costs are
always lower.
• Myth 4: Architecture
and planning less
important.
6. Datacenters Emerge
1940
1950 Rise of Timesharing
1960
Distributed
1970
Computing
Grids
1980
1990
Rise of the PC
2000
Rise of Client/Server
Rise of the Web 2010
Rise of “The Cloud”
7. Hardware/Software/Infrastructure On-Demand
2010
2012 IT On-Demand
Rise of
“Big Data” 2014
Rise of
Rise of “IT In-a-Box”
“Home 2016
Clouds” Distributed
Rise of Service Sharing
“Commodity 2018
Data Services”
The “Big 2020
Migration” Begins Rise of Shared
Enterprise Business 2022
Services
2024
9. Reflecting on the Hype!
• Gartner - Cloud computing revenue will soar faster than
expected and will exceed $150 billion within five years.
• Forrester - Cloud-Based Email Is Often Cheaper Than
On-Premise Email
• Vivek Kundra, CTO of Obama Government: “Growing
adoption of cloud computing could improve data sharing
and promote collaboration among federal, state and local
governments.” E.g: fedbizopps.gov
• Merrill Lynch: “By 2011 the volume of cloud computing
market opportunity would amount to $160bn, including
$95bn in business and productivity apps (email, office,
CRM, etc.) and $65bn in online advertising.”
• IDC: “Spending on IT cloud services will triple in the
next 5 years, reaching $42 billion and capturing 25% of
IT spending growth in 2012.”
9
Sources: http://www.infosysblogs.com/cloudcomputing/2009/08/the_cloud_computing_quotes.htm and http://www.mytestbox.com
14. • Moving from talking cloud to doing
cloud.
• Government entering the cloud
computing game now.
• Security continues to be a priority.
• Little or no expertise in corporate
and government IT.
• Moving to IaaS and then PaaS.
• Leading with private clouds, but
public clouds are the destination.
• Rise of “Big Data.”
16. Big Data Trends
• Data aggregation in
the cloud for
common analytics
within verticals.
• Combining enterprise
data into common
data sets.
• Critical BI.
18. NIST defines cloud computing as a set of characteristics, delivery
models, and deployment models
5 Characteristics
On-demand self-service
Ubiquitous network access 3 Delivery Models
Software as a Service (SaaS)
Resource pooling
Platform as a Service (PaaS) 4 Deployment Models
Rapid elasticity
Private Cloud
Infrastructure as a Service
Pay per use
(IaaS)
Community Cloud
Public Cloud
Hybrid Cloud
19. Delivery Models Morphing
• Software as a Service (SaaS)
– Applications as a Service
– Utilities as a Service
– Connected and Disconnected
• Platform as a Service (PaaS)
– Design as a Service
– Process as a Service
– Testing as a Service
• Infrastructure as a Service (IaaS)
– Database as a Service
– Management as a Service
– Middleware as a Service
– Integration as a Service
– Information as a Service
…and more.
22. • Buzzword “cloud
computing” is absorbed
into computing.
• Focus on fit and
function, and not the hype.
• Security moves to
“centralized trust” models.
• Centralized data becomes a
key strategic advantage.
• Mobile devices become
more powerful, but thin.
• The rise of the “composite
cloud.”
24. 4 Myths of Capacity Management and
Cloud Computing
• Myth 1: I don’t need
capacity management
when leveraging cloud
computing.
• Myth 2: Clouds are
“elastic.”
• Myth 3: Costs are
always lower.
• Myth 4: Architecture
and planning less
important.
26. So, What’s Changed?
• We can no longer assume that computing capacity is dedicated to
a group of users or a group of processes.
– Everything in a cloud computing environment is shared using some
sort of multitenant model.
– This makes capacity modeling and planning much more complex.
• Auto provisioning makes some aspects of capacity planning not
as important since capacity can be allocated when needed.
– However, considering that cost is a core driver for leveraging cloud
computing, using capacity that’s not needed reduces the value of
cloud computing.
• We now have the option to leverage cloud computing systems as
needed to cost effectively provide temporary capacity.
– Called “cloud bursting,” this type of architecture was difficult to cost
justify until cloud computing provided us with a cheaper “public”
option.
27. So, What’s the Same?
• What has not changed in the world of cloud computing
is that it’s still computing.
– Many in the emerging cloud computing space have a
tendency to define cloud computing as the “new
disruptive model” that will change the way we do
computing from now on.
• While many would argue that cloud computing does
not require as much planning as traditional
systems, including capacity modeling and
management, the more enterprises leverage
clouds, the opposite is proving to be true.
– Indeed, the core value of cloud computing is the effective
and efficient use of resources.
29. Best Practice One
• Model capacity should consider the
characteristics of a multi-tenant platform.
– We’ve been here before with traditional multi-
user, but the emerging cloud-based systems are a
bit different animal.
– Clouds typically offer up services or APIs to access
very fine-grained and primitive resources (e.g.,
storage).
– APIs call back to physical resources, typically
virtualized servers that many other tenants share.
30. Best Practice Two
• Make sure to account for distribution.
– Cloud providers typically don’t centralize your
processing in a single physical data center unless you
specify that in the agreement (at an additional fee).
– Thus, your request for 100 server instances to support
processing may mean that some virtualized servers
are allocated in a primary center, but dozens of others
could be allocated to remote data centers, some
perhaps out of the country.
31. Best Practice Three
• Focus on understanding, modeling, and
monitoring services, not systems.
– Most cloud computing implementations leverage core
patterns of SOA, including the decomposition and use
of services to create and recreate solutions.
– Thus, when creating a capacity plan where cloud
computing systems are in play, the most productive
approach is to focus on the services (APIs to the
resources), and how they behave under dynamic
loading versus modeling a system holistically.
33. • Focus on the reorganization and
outplacement of data.
• Focus on PaaS, and service
companies that are good at PaaS.
• Focus on centralized trust,
including moving to identity
management models.
• SOA patterns and technology find
new value in the cloud.
• Continued focus on mobile
computing.
• Home clouds (e.g., iCloud) create a
new track of application and
appliance development.
• Rise of the “cloud aggregator.”
