Economic Analysis of Cloud Computing Cost Savings and Benefits

ECONOMIC ANALYSIS OF CLOUD COMPUTING

ECONOMICS OF CLOUD COMPUTING
Pravin K Asar

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Table of Contents
Abstract..................................................................................................................................................................... 3
Introduction ............................................................................................................................................................ 4
Qualitative Analysis ................................................................................................................................... 5
Productivity improvements .................................................................................................................. 7
Innovation ............................................................................................................................................. 8
Barriers to Adoption ................................................................................................................................. 9
Common challenges .................................................................................................................................. 9
Quantitative Analysis-Financial Analysis:................................................................................................ 10
Construction of Calculation Model: ........................................................................................................ 15

Closing Remarks ................................................................................................................................................. 15
Appendix A: Traditional and Cloud Computing Architecture ........................................................... 17
Cloud deployment models .................................................................................................................. 18

References ............................................................................................................................................................ 20

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Abstract
Cloud computing is receiving an increasing level of attention, as evidenced by the rapidly
growing number of qualitative surveys and analysis that has been published over the past
few years.
Cloud computing is a paradigm shift organizations use the computing resources to conduct
their business. Cloud computing is a new general purpose Internet-based technology
through which information is stored in servers and provided as a service and on-demand to
clients. The computing resources are accessed by mainstream businesses as a pooled or
leased resource over networks. Hence traditional IT investment decisions models are not
directly suitable to perform the cost-benefit and investment decisions for cloud computing
resources.
This paperpresents research on the return-on-investment and pricing models and seeks to
build a model for quantitative assessment of cloud computing.
The results of this analysis model are intended to facilitate a more informed decision
making for cloud computing resources.

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Introduction
“Today IT is a major financial, operational, and organizational component of any strategy” Mische (2001)
In today’s high-tech world, Business relies heavily on Information Communication
Technology (ICT) and Application softwareas a strategic tool for success and survival. As a
result many IT and Software systems are either built or bought leading to exponential
growth in data center. Over the years the underlying technologies (hardware platforms,
software deployment methods) has evolved. Latest trend is to deploy the application on
distributed systems (n-tier deployment) and application to be accessible from anywhere.
The increasing use of Information Technology (IT) has brought with it overheads in the
implementation and maintenance of in-house computing systems. The amount of time and
finances invested in managing IT has increased exponentially; each decade since the 1970s
has seen the evolution of IT into a new phenomenon, starting with mainframes in the
1970s, the rise of the personal computer in the 1980s and client server architecture from
the 1990s. The next phase emerging in IT evolution is cloud computing. The concept of
cloud computing has been around for some time (salesforce.com, Steve Jobs); however it
has only recently become feasible from both a supplier and consumer perspective.
The term cloud computing has become widespread amongst the business community,
government and the media, but there is still some level of confusion outside of the
technology industry about what cloud computing actually is, not the least because the
language around cloud is constantly evolving. Indeed, cloud computing is a catchall
termdescribing a range of related activities, but which is identified by each of the following
corecriteria:
Accessing computing resources as external services, instead of as products that are
purchased, installed and managed within an organization.
The ability to rapidly scale the allocation of computing resources to match
fluctuations in business demand.
Utility-based pricing, so that user’s only pay for computing resources actually used
(rather than for full load capacity) as they do, for example, with electricity.
Appendix A discusses in details the enterprise data center and cloud data center
deployment architectures.At the heart of cloud computing lays the ability of computing
resources to be reliably and efficiently accessed by mainstream businesses as a pooled
resource over networks.

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Qualitative Analysis
A number of recent international surveys by CDW (2011) shows that businesses are
increasingly becoming aware of the potential benefits of cloud computing and moving
along the ‘cloud journey’ and its potential benefits
Oracle (2010) whitepaper summarized the business benefitscommon to both public and
private cloud as follows:
Improved efficiency: Because both public and private cloud are based on grid
computing13 and virtualization, both offer high efficiency and high utilization due to
sharing pooled resources, enabling better workload balance across multiple
applications.
Increased availability: Another benefit of being based on grid computing is that
applications can take advantage of a high availability of architecture that minimizes
or eliminates planned and unplanned downtime, improving user service levels and
business continuity.
Elastic scalability: Grid computing also provides public and private cloud with elastic
scalability; that is, the ability to add and remove computing capacity on demand.
This is a significant advantage for applications with a highly variable workload or
unpredictable growth, or for temporary applications.
Fast deployment: Because both public and private cloud can provide self-service
access to a shared pool of computing resources, and because the software and
hardware components are standard, re-usable and shared, application deployment
is greatly accelerated.
Additional benefitsthat are unique to public cloud computing includes:
Low upfront costs: Public clouds are faster and cheaper to get started, providing
users with a low barrier to entry because there is no need to procure, install and
configure hardware.
Economies of scale: Large public clouds enjoy economies of scale in equipment
purchasing power and management efficiencies. Savings may be passed on to
consumers, and will increase as competition in the sector increases over time.
Simpler to manage: Public clouds may require fewer IT personnel to manage and
administer, update, patch, etc. Users rely on the public cloud service provider
instead of an internal IT department.
Operating expense: Public clouds are paid out of the operating expense budget, often
by the users’ line of business, not the IT department. Capital expense is avoided,
which can be an advantage in some organizations.
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The emergence of cloud computing brings many benefits which are shifting the economics
of IT. Cloud technology standardizes and pools IT resources and automates many of the
maintenance tasks performed manually today. Cloud architectures facilitate elastic
consumption, self-service, and pay-as-you-go pricing. Cloud also allows core IT
infrastructure to be brought into large data centers that take advantage of significant
economies of scale.
The economics of cloud computing can be grouped into three broad categories:
Direct cost savings
Productivity improvements
innovation
Direct cost savings
The largest and most identifiable economic benefit of cloud computing is the direct
cost savings. Direct cost savings for organizations occur from changes both within
the organization, and also the large data centers housing the IT infrastructure.
Direct cost savings(Microsoft 2010) occurs at the data centers through significant
economies of scale in three areas.
Supply-side savings:
Large-scale data centers potentially lower costs per server due to superior buying
power and expertise.
Demand-side aggregation:
Aggregating demand for computing can smooth overall variability, allowing multiple
users across varying industries, regions and time zones allowing average server
utilization rates to increase.
Multi-user efficiency:
Increasing the number of users often lowers the application management and
server cost per tenant.With large data centers housing the IT infrastructure, cloud
computing activities remove many IT operational considerations from an
organization altogether. This can not only reduce overheads associated with day-today operations of computer hardware and software, but can also simplify
procurement, the need to plan for upgrades and patches to software, management
of software licensing and facilities management. Removing this complexity from an
organization frees personnel who are otherwise occupied with daily technology

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operations. This may not translate into a reduction in overall headcount, but rather
a reallocation of people (and tasks) within an organization.
With these changes, an individual organization’s costs can change from mainly
capital expenditure to predominantly operating expenditure. This can be achieved
through lower upfront IT costs, as discussed earlier, and because cloud computing
follows a utility basedpricing model in which service costs are based on
consumption. That is, a company onlypays for those services that it uses rather than
a fixed price for a potential level of services that may not suit actual demand.
Another direct cost saving may come with lower electricity consumption (including
for cooling apparatus) and accommodation costs for IT infrastructure, which are
often among the largest components of overall IT costs.
Specifically, several key factors(Accenture 2010)enable cloud computing to lower energy
use and carbon emissions, including:
Table 1 How Cloud Computing Leads to Green IT
Factor
Dynamic
provisioning
Multi-tenancy
Server utilization
Data center
efficiency

Rationale
Reducing wasted computing resources through better matching
of server capacity with actual demand.
Flattening relative peak loads by serving large numbers of
organizations and users on shared infrastructure and reducing
costs through sharing of applications.
Operating servers at higher utilization rates.
Utilizing advanced data center infrastructure designs that reduce
power loss through improved cooling, power conditioning, etc.

Additionally, large data centers may be able to take advantage of geographical variability in
electricity rates and choose to be situated in locations with either less expensive electricity
supply or to negotiate bulk purchase agreements to lower the cost of electricity.
Productivity improvements

With the implementation of cloud computing, changes to business can be achieved without
the need for detailed capacity planning, changes to installed technology or new technology
purchases. Translated into business outcomes, this allows for the ability to open offices,
geographically move staff and operations without compromising access to business
systems, put new ideas into practice, and to meet new business requirements faster than
before.

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Cloud also enables organizations to scale up or down to the level of service required,
allowing optimization of required capacity and reduced costs. The on-demand up/down
elasticity of cloud-based computing services allows the ability to quickly scale computing
resources to match business growth while minimizing downside risk, that is, preserving
the ability to release resources if a new project fails to get traction.
Additionally, cloud computing allows staff to access files and data when they are working
remotely or outside of office hours. E-commuting has widespread potential benefits to both
business — via a reduction in overheads (i.e. smaller office space may be required) — and
to consumers (e.g. through a reduction in commuting time).
Innovation

The cost and efficiency benefits that initially drive interest in cloud computing may be
augmented by other benefits. For example, organizations may gain further increased
business flexibility and agility, collaboration, and an ability to take new products and
services to market. An example of this is an online DVD hiring company that is transferring
to cloud services to enable streamed delivery; the customer is able to receive, and the
company to distribute, the product with significant time and cost savings.
Cloud services may be particularly beneficial to small businesses that might lack the capital
to acquire the in-house ICT solutions required in the absence of cloud services.
The benefits of cloud computing may also translate into a faster ‘time to market’ for
customer-facing activity. New services can potentially be built, and existing services
adapted, more rapidly in response to feedback or changing customer requirements. In
some cases, this could mean that improvements are significant, coming down from months
to weeks or from weeks to days. Organizations may also progress to building entirely new
services and products on cloud platforms taking full advantage of centralized data, easy
scalability and web accessibility.
Furthermore, many companies spend a significant portion of their IT budget on
maintaining existing services and infrastructure, leaving few resources available for
innovation. Cloud computing has the potential to free up significant resources that can be
redirected to innovation.
In spite the potential benefits described earlier, the all businesses are not adopting the new
technology.

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Barriers to Adoption
While there is often a strong case for the adoption of cloud services, there are several
constraints that need to be overcome. The natural barriers to full adoption include, but are
not limited to:
speed/latency issues and reliance on telecommunications services providers
consistency of current processes and applications with cloud offerings (for example,
‘off the shelf’ cloud services may not integrate well with a business’ existing
operations)
location of data and related security and data sovereignty issues (including
implications of the US Patriot Act17)
business continuity/disaster recovery and integrations
Limited knowledge of product offerings and lack of familiarity of businesses with
opportunities.
Based on the survey by various organization(KPMG, 2012) organizations identified as
‘pushing the boundaries’ of cloud computing came up with several findings.
Although cloud computing makes it possible to access services located anywhere in the
world, there is a strong desire for services located within certain geographical borders. For
government/defense data, research data location is important in conjunction with security.
A significant barrier to take-up is the wide variation in maturity and quality of cloud
services and service providers — a particular problem is the inability to get enterprise
grade service level agreements.

Common challenges
Common challenges include uncontrolled adoption of cloud applications in large
organizations, non-compliance with local regulations (especially those that relate to the
handling of customer information), and concerns about regulations applying to services in
other jurisdictions, preparing ‘apples to oranges’ business cases for cloud computing, and
measuring the performance of cloud service providers.
Over time the above challenges to adoption will substantially overcome, as seen in early
adopter countries such as the US (TCS, 2012)and Europe (European Commission, 2012)

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Quantitative Analysis-Financial Analysis:
Various financial and economic models have been derived used to financial analysis to
evaluate the return on investment, total cost of ownership.
Zhipeng Wu and Aiping Gan (2011) also discussed the intangible benefits (in addition to
return of investment) from migration of the enterprise data center to cloud environment.
Sharma et al(2012) has looked at cloud computing economic analysis from pricing the
cloud computing from service provider point of view. Their model is based on the BlackScholes-Merton model for option pricing. Their model could be useful to the pricing for the
cloud computing services.
Andrzejak, Knoda and Yi (2011) have discussed the real instance price traces and workload
model (referred as spot pricing model by authors) and its use to consumer to optimize the
cost of peak time cloud computing needs.
Mach and Schikuta (2011) developed an analytical model that supports the decisionmaking process to be applied with business cases and enables cloud consumers and cloud
providers to determine their own business strategies and to analyze the respective impact
on their business, including the energy costs.

Li et al (2009) has proposed a cost analysis method based on the Total cost of ownership.
Their modelis based the Total cost of ownership approach. To compare the TCO for
enterprise and cloud computing data center, it is important to calculate the total
investment over the period of desired time, for this Net Present Value (NPV) can could
applied.
Net Present Value (NPV) concept is commonly used in financial analysis to evaluate an
investment considering the time value of the investment over a fixed duration. NPV has
been used to compare purchasing with leasing /renting CPU cycles from the cloud taking
CPU performance depreciation into account (Walker, 2009). It has also been used to
compare the cost of hosting application workloads considering additional cost factors such
as software licenses, electricity, workload growth, and multiple models of cloud usage.
Where NPV is defined as the total investment cost over the course of Y years into the
future, where c(t) is the cost invested at year t. k is the cost of capital where the money
invested this year is worth more than money invested next year by a factor of (1+k).

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Table 1 (Ellström,2011)below summarizes the various cost models in use for procurement
of good and services by business.
Table 2 Different cost perspectives and corresponding Cost Modeling methods
Purchasing
perspective
Supply chain
perspective
Supplier perspective

Method
TCO
TCR
SCC

Focus
Supplier selection
Logistics outsourcing
Including logistics costs and information
costs
Traditional ABC Product cost focusing production
SCC+value
The same as SCC, value is also calculated
Supplier costing Not focus on one product but on supplier

In context with Information and Communications Technology investments, TCO is best
suited(ITIL, 2012) since TCO philosophy aimed at understanding the relevant cost of
buying a particular good or service from a particular supplier. TCO looks at an entire
lifecycle cost analysis. In addition to the price paid for the product, TCO includes the costs
incurred by purchasing for order placement research and qualification of suppliers,
transportation, receiving, inspection, rejection, storage and disposal. Although it may not
possible to quantify the cost breakdown for each lifecycle phase, TCO is an important tool
to support strategic cost management. It is a complex approach that requires the decision
makers to determine which costs it considers most relevant or significant in the
acquisition, possession, use, and subsequent disposition of a good or service.
The Information Technology Infrastructure Library (ITIL) provides a good reference to
various topics including the cost breakdown structure for Information Communication
Technology (ICT) infrastructure to calculate all IT costs (ITIL, 2012)
While ITIL indicates no preference for either cost centered accounting or service based
costing, the logical preference would be service for the simple reason that the philosophy of
service management is more closely aligned with service based costing. The name Service
Based costing suggests an end-to-end view of the costs of delivering an IT service.
Practically this means that a costing methodology and set of cost centers need to be defined
using the service definitions provided by the Service Level Management process and as
published in the service catalog. In principle this means that the line items appearing on
the client bill are synchronized with the services as they are defined within the Service
Level Agreements and how Configuration Items (CIs) are captured and defined.

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The NPV computed over Y years of the annual cost of a delivery model based on in-house
/enterprise data centers are presented below. The cost of enterprise data center includes
the cost of hardware, software procurement and licensing costs, utilities costs and salaries
of IT personnel (data administrators, IT support staff, etc.)
With reference to earlier work done in the area of IT financial management (ITIL, 2012),
the following equations can be formulated for the costs associated with enterprise data
center (edc).

Hardware cost (Costhw) and Software cost(Costsw) are based on the number of physical
servers (these servers are the data crunching severs) and data storage servers needed to
run application workloads. Their cost is accounted for only in the year in which they were
purchased. However, for applications that require software licenses and upgrades, this cost
should be accounted for accordingly.
Utilities cost consists of electricity and network bandwidth. The first term in equation
represents the electricity costs, which is modeled based on the power (P) required to run
and cool the physical compute and storage servers. The number of units needed to run a
server is based on its power supply unit, PSU. The number of units needed for cooling is a
[0.5, 1] factor of the number of units needed for running the servers (Walker,2009). We
model electricity cost as a monthly function based on total units of kilowatt-hours (kWh)
consumed. For example, for less than t1 units consumed, r1 is applied. Many utilities,
including those offered by cloud providers commonly use this type of cost function. The
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total cost depends on the total units consumed. If u, greater than t2, is consumed for a cost
function based on r1 ($/kWh), r2, and r3, then the total cost is c. Both electricity and
network are computed monthly and then scaled by 12 to obtain the annual amount.
The salary that is accounted for as part of operational expenses and is the total amount
needed to support the work related to administrating physical compute and storage
servers in the enterprise data center. We note that administration of virtual machines is
also required but we do not represent them in this term as virtual machine administrators
are needed for both the enterprise data center delivery model and the cloud delivery
model. They end up being the same amount, so we drop them from this term for simplicity.
The FTERatio (Clarke, 2010), n:1 is defined as the number of physical compute or storage
servers n that one system administrator can manage. A “lower” FTE Ratio such as 10:1
means that workers are less efficient than a “higher” FTE Ratio such as 100:1
Cloud Cost Model:
Similarly cost model for ICT service delivery based on Cloud computing infrastructure can
be defined. Amazon EC2Cloud (Amazon, 2012) is one of early cloud service provider.
Amazon provides various virtual server configurations to suit the needs of computing tasks
(speed and scale). Hence Amazon EC2 is used as a reference service provider.
Infrastructure-as-a-Service (IaaS) is a very popular cloud model, as it mimics the setup of
enterprise data center; in terms of hardware and software configuration. Biggest difference
in this model is business do not have higher upfront cost, but have recurring usage costs.

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Cost per instance of cloud depends on the configuration of cloud virtual machine, and
normally termed as “Small”, “Large”, etc. The reservation fee (generally one-time fee for
instance setup is charged and varies based on the period of contract) and usage fee is
charged hourly (a pay per use pricing concept).
The cloud charges for both the total amount of data stored and the total number of I/O
requests (rate of IO). Depending on the cloud storage used, such as EBS or S3, the storage
fees and the usage fees vary. The fees are often modeled using a similar rate function to the
one depicted.
Network: The cloud charges for both the total amount of data transferred out of an
availability zone (cloud data center). In some cases, data transferred (data upload as well
as data download) into a cloud data center is also charged. Many cloud providers (such
Amazon, Microsoft) have recently removed data upload charges.
Many cloud systems management service offerings such as instance monitoring, backups,
load balancing, elasticity, etc. at nominal cost. Depending on application requirements, a
cloud user could employ one or more of these services in addition to its compute, storage
and network needs. Our application in Section III-D requires snapshots for backup
purposes, so we represent the cost of storing the snapshot based on the size of the
snapshot and using the frequency of snapshot service (i.e., total I/O writes to disk) in this
term. One may exclude other services for simplicity
Putting all these components together, we can built both the cost models for using an
enterprise data center vs. using the cloud to deliver the ICT services.

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Construction of Calculation Model:
Using the research and with reference to Amazon’s online cloud computing cost calculator
(Amazon, 2012) and literature published by Varia (2012),a model is developed using
Microsoft Excel 2010. The model organized in different spreadsheets. The model is selfexplanatory and well documented. Assumptions used for the calculations can be edited.
1. Main: When user key-in the requirements (numbers of servers for each desired
configuration) for standardized (day-to-day usage) and peak time usage.
2. Cloud RDS (Cloud Remote Data Service)
3. Co-location: Standard demands are met on-site and peak demands are met by cloud
servers.
4. On-site: All infrastructure is located, operated on-site
5. Definitions: Explanation of terms and definitions used in the calculation.

Closing Remarks
The TCO model could cost estimation and financial planning to decision maker for a period
of time. The Figure 1 below gives overall Annual Total Cost of Ownership (TCO) summary.
Figure 2, 3 and 4 give the cost breakdown for cloud, co-location and on-site IT setup. The
calculations are done for 20 Servers (10 small, 10 large) and based on the cost factors and
values.
Cloud RDS (On-Demand
Instances Only)
Cloud RDS (w/ 1 Year Reserved
Instances)
Cloud RDS (w/ 3 Year Reserved
Instances)

$74,847
$59,516
$55,805

Co-Location
On-Site

$162,768
$195,659

Figure 1: Annual Total Cost of Ownership (TCO) Summary

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Figure 2Cost Breakdown for Cloud data center

Figure 3 Cost Breakdown for Co-located data center

Figure 4 Cost Breakdown for Onsite data center
This model could be customized to suit the need of individual organization.

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Appendix A: Traditional and Cloud Computing Architecture

Figure 5 Traditional computing, owned, installed and operated on the premises by individual organizations

Figure 6 Cloud computing, rented and accessed as external, shared services over networks

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Cloud deployment models (Oracle, 2010 and Oracle, 2011)

Cloud computing generally has four deployment models, as briefly explained below:

Figure 7 Cloud Deployment Models
Private cloud: For exclusive use by a single organization and typically controlled, managed
and hosted in private data centers. The hosting and operation of private clouds may also be
outsourced to a third party service provider, but a private cloud remains for the exclusive
use of one organization. This is typically the first step in a company’s cloud journey. The
computing resources shared by user groups.
Public cloud: For use by multiple organizations (multi-tenants) on a shared basis and
hosted and managed by a third party service provider (examples include Amazon EC2,
Google Apps and Microsoft Azure).
Community cloud: For use by a group of related organizations that wish to make use of a
common cloud computing environment. This deployment is ideally suitable for
organizations who share data on regular basis.
Hybrid cloud: When a single organization adopts both private and public cloud for a single
application in order to take advantage of the benefits of both.

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Cloud computing can be categorized into three service models:

Figure 8 Cloud Service Models
Software as a service (SaaS): Renting access to software as Web-accessed services instead
of installing it on the premises (example services include Salesforce.com, SAP Business-By
Design, Google Apps).
Platform as a service (PaaS): Developing and hosting bespoke software in cloud
environments (platforms) that provide all required tools, languages, databases and
resources (example services include Force.com, NetSuite Business Operating System,
Microsoft Azure and Office 365 and Google App Engine).
Infrastructure as a service (IaaS): Renting access to computer processing power and
storage over networks (example services include Amazon EC2 and Amazon S3).

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Economic Analysis of Cloud Computing Cost Savings and Benefits

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Economic Analysis of Cloud Computing Cost Savings and Benefits