Cloud Computing Market Segmentation
Caesar Wu, Rajkumar Buyya and Kotagiri Ramamohanarao
Cloud Computing and Distribution Systems (CLOUDS) Laboratory,
School of Computing and Information Systems,
The University of Melbourne, Australia
Keywords: Cloud Market Segmentation, Hierarchical Clustering, Time Series Forecasting, Cloud Service Providers.
Abstract: The topics of cloud pricing models and resources management have been receiving enormous attention
recently. However, very few studies have considered the importance of cloud market segmentation.
Moreover, there is no a better, practical and quantifiable solution for a cloud service providers (CSP) to
segment cloud market. We propose a novel solution that combines both hierarchical clustering and time
series forecasting on the basis of the classical theory of market segmentation. In comparison with some
traditional approaches, such as nested, analytic, Delphi, and strategy-based approaches, our method is much
more effective, flexible, measurable and practical for CSPs to implement their cloud market strategies by
rolling out different pricing models. Our tested results and empirical analysis show that our solution can
efficiently segment cloud markets and also predict the market demands. Our primary goal is to offer a new
solution so that CSPs can tail its limited cloud resources for its targeted market or cloud customers.
1 INTRODUCTION
The issue of cloud pricing models, revenue, and
resources management (cloud economics) is one of
the most critical topics in the cloud computing
(Buyya, 2002; Wang, 2012) because it does not only
become increasingly important for many CSPs to
implement their cloud business strategy but also
allow them to innovate their business processes and
models(Weinman, 2012; Berman, 2012). However,
previous studies only focus on finding an optimal
solution from a pure CSP perspective (internal
rationality) and often ignore market impacts
(external rationality). In this study, we concentrate
on the problem of cloud market segmentation,
especially for business to business (B2B) market by
taking into account both CSP’s resources and market
factors(McDonald, 2012).
The B2B cloud market segmentation is believed
to be a complex problem for many CSPs (Shapiro,
1984). It is challenging because it involves many
disciplines such as managerial decision, market
theory, cloud computing, and microeconomics.
Moreover, it is often very subjective and arbitrarily.
We restrict in our current study to B2B because
the B2B market is more significant than business to
consumers (B2C) and consumer to consumer (C2C)
according to US Census Bureau. Statista reported
(global-ecommerce, 2017) the size of the Global
B2B e-commerce market ($7.7 Trillion) is about
235% larger than B2C ($2.3 Trillion) in 2017. The
cloud is a type of e-commerce as it shares the
characteristic of online access (NIST, 2011).
Although the size of the B2B market is considerable
large and it is crucial for CSP’s business strategy
and pricing, as of now to the best our knowledge, no
work has been done on this topic. Yet, many CSPs
urgently need to understand how to serve their
targeted customers well for the limited resources.
Hence, our goal is to find a better solution to
segment cloud market.
To motivate the problem, we consider the
following scenario. Suppose a local Internet Service
Provider (ISP) has decided to expand its hosting
business into the B2B cloud market with a limited
investment budget. The CEO asks the management
team to formulate a business strategy with different
pricing models to grow both the cloud business
revenue and profit. One of the most straightforward
solutions is the “one-size fits all” or uniform pricing.
It means that the ISP can set up a markup price for
its desired profit margin while the customers have to
decide either “take or leave it” regardless of what the
customer’s needs are. The subsequent question is
888
Wu, C., Buyya, R. and Ramamohanarao, K.
Cloud Computing Market Segmentation.
DOI: 10.5220/0006928008880897
In Proceedings of the 13th International Conference on Software Technologies (ICSOFT 2018), pages 888-897
ISBN: 978-989-758-320-9
Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

would this business strategy work. If not, what is an
alternative solution that can be pursued?
An intuitive answer could be to deliver the cloud
services or product with personalized pricing to suit
each customer’s need. However, it is impracticable
for a CSP to offer personalized service and price
because of the limited budget or resources.
Fortunately, many individual customers have similar
requirements, and their usage patterns may have
some common characteristics, such as a size of
computing (CPUs) and memory. It means that we
can group these customers’ demands. This idea leads
to the group pricing, which is also called as market
segmentation. The original concept of the market
segmentation was introduced by (Smith, 1956). He
defined the term of the segmentation at a strategic
level, which “is based upon developments on the
demand side of the market and represents a rational
and more precise adjustment of product and
marketing effort to consumer or user requirements.”
He argued that good market segmentation will lead
to a successful business strategy.
As a matter of fact, the uniform pricing and the
personalized pricing are two extreme ends of the
group pricing (Figure.1). Belleflamme et al.
(Belleflamme, 2015) stated: “the better the
information about consumers, the finer the partition
of the consumers into groups and the larger the
possibilities for firms to extract consumer surplus.”
Figure 1: Uniform, Group, and Personalized Pricing.
Therefore, the goal of the segmentation process
is to extract the customer information, such as usage
patterns or behaviors and then to develop various
pricing models and service configurations to meet
their needs. In fact, (Yankelovich, 2006) argued the
good market segmentation should meet the
following criteria:
1. Align with the company’s strategy;
2. Specify where the revenue and profit come
from;
3. Articulate cloud customers’ business values,
attitudes, and beliefs, which are closely
associated with the product or service (such as
cloud instance) offerings;
4. Focus on actual business customers’ behaviors;
5. Make sense to the firm’s senior executive team
and the broad;
6. Flexible and quickly accommodate or
anticipate changes in markets or consumer
behaviors.
Based on these criteria, we develop a novel
solution that allows CSPs to identify the B2B cloud
market segment quickly. In comparison with other
traditional methods, such as analytical(Wind, 1978),
strategy-based(Verhallen,1998), nested (Shapiro,
1984), survey, and Delphi methods(Best, 1974), it is
much more tangible, flexible, agile, and cost-
effective for a CSP to roll out different cloud pricing
models for its cloud business strategy. It also enables
CSP to respond to the ever-changing environment of
the cloud market rapidly. The inputs and outputs of
the process are illustrated in Figure 2.
Figure 2: The Solution Process of Cloud Market
Segmentation.
The solution is summarized in three steps: 1) We
use hierarchical clustering to segment cloud market;
2) We apply time series forecasting (TS) for the
sales volume prediction; 3) We combine both results
for each market segment. We use both Google’s and
the local hosting service datasets in our analysis to
demonstrate our methodology. The final results are
expected in Table 1.
Table 1: The Expected Results of Segmentation.
Cloud Market
Segmentation
Segment1 Segment k Total
Demand (Sales
Quantity)


⋯ 








The proportion of
Each Segment




/

⋯ 



/



1


Market Segment’s
Charectretsics
Memory Pattern
⋯
Memory, CPU,
Network
k
By doing so, we make three contributions:
1. We demonstrate how to use hierarchical
clustering (HC) algorithms to identify the
Cloud Computing Market Segmentation
889

optimal number of cloud market segments and
extract (or assess) various cloud usage patterns.
2. We use TS forecasting to predict the local B2B
market demand for virtual machines (VMs).
3. Finally, we combine both results into the final
cloud market segmentation table so that a local
CSP can leverage it further to build different
cloud price models for its targeted market.
The rest of the paper is organized as follows, In
Section 2 we provide a brief literature review of
market segmentation. In Section 3 we describe the
details of our solution of market segmentation, such
as fundamental principles of the experimental
methods, and some assumptions that we made. In
Section 4, we illustrate how to use the HC to
segment cloud market and find the appropriate
number of segments. In Section 5, we present how to
forecast the quantity of VMs demands and then
combine both results. In Section 6, we analyze and
discuss our empirical results. Section 7 provides the
summary of this paper with conclusions.
2 RELATED WORK
Since (Smith, 1956) first cast the term of market
segment, the topic has been studied in great detail in
term of its theory, methodology (Wedel, 1998),
concept, foundation, and process (McDonald, 2012).
Along with the consumer market, the B2B market
theory (Wind, 1974) has also been developed due to
its growing momentum and substantial market size
and values. Due to the targeted value proposition of
the B2B cloud market, namely product, price, place,
and promotion (or Kotler’s four Ps), the related work
consists of theory, analytic approach, and cloud
pricing in term of the market segmentation.
According to (Wedel, 1998), the essence of the
market segmentation is “a theoretical marketing
concept involving artificial groupings of consumers
constructed to help managers design and target their
strategies.” In practice, it is an iterative process to
assign a set of variables (e.g., four Ps) to many
potential customers that help a firm to form
homogenous groups. Under the Wedel’s concept,
(Thomas, 2012) gave a further clarification of the
B2B market segmentation, which is “a dynamic
business decision process driven by an (economic)
theory of how market functions.” In practice, it is a
set of decision process and activities that can be
divided into two different approaches: One is the
top-down approach, which is the process of splitting
customers into different segments. Another is
bottom-up one, which is to agglomerate each
customer into different groups (Claycamp, 2000).
claimed that although the top-down approach is a
simple and appealing, it is very challenging to
implement because the splitting process is mainly to
drive the potential value of customer surplus.
Claycamp exhibited that market segmentation is
ultimately a bottom-up process of aggregation in
theory. However, the bottom-up approach is also
facing challenges in practice because some
parameters are very hard to estimate, such as
marginal response or managerial requirements
(
Laughlin, 1991 ). One of the solutions is to propose
some controllable marketing variables in identifying
marketing stimuli, which is down to only one “P”
(Promotion). It is like an analytic approach.
Ralph (Oliva, 2012), indicated the B2B market
“segmentation is an analytic discovery process for
dividing a large group of customers or prospects into
smaller groups.” Similarly, Seufert (Freemium,
2014) presented an analytic approach to segment
user groups for the freemium pricing model. Their
approach focused on the core value of the business.
If we compare the core value with the hedonic value
analysis (Pakes, 2003), we can draw an analogy
between Irwin Gross’ core value, cost, and prices
with the hedonic function (Equation 1).



∑










/



(1)
where p
j
is the price of the cloud VM instance
“j.” mc
j
is the marginal cost, Q
j
is a quantity, |∂Q
j
/∂p|
is the partial derivative of the quantity taken in term
of a price, and Q
j
/|∂Q
j
/∂p| is a markup price, and p
l
is the CSP’s purchasing price from other vendors.
Here, a potential value lost is defined by consumer
surplus (CS
i
) (Belleflamme, 2015). The core value is
the economic driving force (Figure. 3)
Figure 3: Analytic Method of The B2B Market
Segmentation.
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890

However, Richard (Plank, 1985) criticized the
analytic approaches because many methods are
complicated to translate their analytic results into a
business strategy. In order to improve the analytic
approach, Theo (Verhallen, 1998). proposed a
strategy-based approach, which is to identify
unobservable characteristics (e.g., firm’s goals,
objectives, strategy types, and long-term plans) in
contrast to observable traits (business size, location,
and four Ps). Furthermore, in relation to different
input variables, Benson (Shapiro, 1984) proposed
the nested- approach, which is to nest from
demographics, operating, purchasing, and situational
variables to personal characteristics but the author
indicated their approach could not be generalized.
Alternatively, (Best, 1974) offered an expert
solution that can become a prior probability of input
variables for market segmentation process.
In contrast to the above methods, Balakrishna’s
(
Balakrishna, 1980) focuses on a solution based on
how to better use the industrial market concept for
the B2B market segmentation. It is more like a
generalized solution for the B2B market. Although
these solutions are very persuasive, the main issue
remains unsolved, which these solutions are
unquantifiable for CSPs to implement their cloud
business strategy by rolling out different cloud price
models. As a consequence, many recent studies
directly focus on cloud pricing models for CSPs to
maximize its revenue.
As early as in 2002, (Buyya, 2002) proposed
economic models or pricing schemes to regulate the
grid computing resources, which can be considered
as one of the prototypes of cloud pricing model.
(Javadi, 2011) developed a statistical model for
Amazon Web Service (AWS) spot instance prices in
public cloud environments. Although the model is
valid, the spot instance is not desirable for the
mainstream of B2B cloud resources because many
B2B applications require the mission-critical cloud
infrastructure to support its business.
Similarly, Hong (Xu, 2013) proposed the alpha-
fair utility function to quantify the applications’
needs for cloud users in term of cloud resource
allocation. Although the study is beneficial for a
theoretical exploration, the model assumptions
require further consolidation because the alpha-
fairness utility function is mainly applied to the issue
of traffic congestion of communication networks
(Hande, 2010) rather than cloud services.
Practically, different cloud applications (such as web
hosting, database, data storage, virtual desk
infrastructure, and so forth) will have different
requirements, which lead to different market
segments. As respect to the word of segmentation,
(Wang, 2012) investigate this problem from an
aspect of segmenting cloud capacity, which is to
formulate an optimal capacity segmentation strategy
for revenue maximization to satisfy the random
market demand.
Overall, we can see that there is a gap, which is
how to find a quantifiable solution to segment the
B2B cloud market so that CSPs can build various
optimal price models for its targeted market or
customers in connection with both internal costs and
external market demand. Our solution provides the
answer for this gap.
3 PREPARATION TESTS
As (Claycamp, 2000) stated in their theoretical
study, the clustering analysis is one of the practical
solutions for the market segmentation. However,
there are many clustering methods of clustering
methods, such as categorical (hard vs. soft),
structure (flat vs. hierarchical), data type (model-
based vs. cost-based), and regime methods
(parametric vs. nonparametric). The question is
which one is the right method for our problem.
A good strategy is to explore the datasets in our
hands. The first dataset is Google’s cloud trace
(cluster-data, 2011) which consists of large cloud
clusters for more than 12,500 VMs. It has six
dimensions: timestamp, job ID, Task ID, and job
type, normalized task cores, and normalized task
memory. However, Google has obfuscated some
information of the dataset, in which “certain values
have been mapped onto a sorted series” for
confidential reasons. Fortunately, the encryption
schemes will not impact market segmentation
because we are looking for underlying customer
usage patterns.
The second dataset is collected by one of the
leading Australia telco firms for its hosting business.
The dataset has sales records of web servers for its
business customers between 2003 and 2009. The
idea of the first experiment is to estimate the number
of cloud market segments and the proportion of each
segment. The Google’s dataset would unveil the
cloud usage patterns. We assume that both global
and local cloud customers have the same usage
pattern in this case. The 2
nd
experiment is to forecast
the local B2B market demand because the local B2B
market demand is closely associated with a robust
B2B relationship (Narayandas, 2005).
Cloud Computing Market Segmentation
891

3.1 Proposed Method of Segmenting
On the base of the good criteria for segmenting
market (Yankelovich, 2006) and the dimensions of
Google’s dataset, we propose HC method. The
reasons are as follows:
1. We do not know the exact number of cloud
market segments in advance.
2. Referring to the Claycamp’s theory, it has to
be an agglomerative process of fusion
clustering, which is a bottom-up process of
clustering.
3. Furthermore, it would be preferable to
leverage HC because we can form a
dendrogram (a tree diagram) that allows us to
choose the dendrogram at any desired level.
This analytic feature allows CSPs to segment
the B2B market at any granularity level so
that a CSP can explore opportunities of any
niche market.
However, all methods have its disadvantages.
One of the primary difficulties of HC is too sensitive
to the number of clusters. One solution to solve this
problem is to use Ward’s algorithm to minimize the
variance of Sum Square of Errors (SSE) by
consideration of all possible methods. Our overall
strategy of the 1
st
experiment is illustrated in Figure.
4. The essence of the clustering algorithms is to
calculate dissimilarity that is measured by the
Euclidean distance of data points. For the Ward’s
algorithm, the equations of SSE are as follows:
∆


∪

 


∪























(2)












,











,



∪
















(3)
where Δ
Ca
∪
Cb
is the cost function to combine
two clusters C
a
and C
b
that have the number of
observations n
a
and n
b
respectively. a
i
, b
i
, and c
i
are
the ith observations in the cluster C
a
and C
b
, and the
merged cluster C
a
∪ C
b
. Likewise, μ
a
, μ
b
, and 

are
the centroid of these clusters. To update the
Euclidean distance, we can use Lance-Williams
dissimilarity update formula (Murtagh, 2012).
Figure 4: The Map of Hierarchical Clustering Method.
3.2 Proposed Method of Predicting
The idea of the second test is to predict or forecast
the B2B market demand in the next 12 months so
that we can build cloud infrastructure capacity to
meet the local B2B cloud market demand. Several
techniques can be applied for prediction, such as
linear and multiple regression, random forest,
decision tree, ANN, and time series forecast.
In this study, we adopt the TS forecast model to
predict the total volume of VM sales. The reasons
are. 1.) TS forecasting is simple. It would be easier
to be presented. 2.) We can estimate each sales
volume for every month or year so that it would be
convenient for cloud capacity planning. 3.) The
forecasting result will tell the confidence interval. 4.)
It can be updated very quickly.
4 CLOUD MARKET SEGMENTS
We test the Google’s dataset first and see whether
the dataset has the meaningful patterns or not. This
process is called “clustering tendency evaluation.”
The reason to check the clustering tendency of the
data is that a hierarchical clustering method can
impose patterns or clusters onto a randomly
distributed dataset even if there are no such
definable or extractable clusters within the dataset.
(Wang,2010) and Kotagiri (Lawson, 1990) did
some studies regarding of clustering tendency
assessment. There are many techniques available for
cluster tendency evaluation. One of the methods is
Hopkins statistic (Brain,1954) null hypothesis test.
Hopkins’ test can be expressed using the following
equation:

∑





∑




∑








(4)
where I
i
square is the distance between an
observation x
i
and its nearest neighbor x
j
(x
i
, x
j
∈ D
SE-CLOUD 2018 - Special Session on Software Engineering for Service and Cloud Computing
892

= dataset). P
i
square is the distance between a
random y
i
and its nearest neighbor y
j
(y
i
, y
j
∈ D
r
=
random dataset). The null hypothesis test shows that
if H value is equal or close to 0.5, the tested dataset
D has no meaningful clusters so that we accept the
null hypothesis. Otherwise, we reject the null
hypothesis. Based on the above Hopkins’ equation,
we calculate the Hopkins’ index. It is equal to 0.064,
which is approaching zero. We also use R command
“fviz_dist” (display dissimilarity matrix) to visualize
the Google’s dataset with a comparison of a
randomly generated dataset (Figure 5, on the left)
Figure 5: Assessing Clustering Tendency of Google’s
Dataset.
The pink color indicates I
i
square = 0 and the
purple color means I
i
square = 1. In contrast, the
right diagram of Figure5 shows that both values are
randomly distributed across the dissimilarity matrix.
Hopkins null hypothesis test result tells us Google’s
dataset has clustering tendency.
4.1 Extract Cloud Usage Patterns
For R system, the bottom-up and top-down is known
as Agglomerative Nesting (or AGNES) and Divisive
Analysis (or DIANA) respectively. The linkage
algorithm is “Ward’ because we want to minimize
the SSE variance. If we temporarily assume the
number of segments is four (McDonald,2012)
suggested the number is between 5-10 and others
suggestion is between 4 and 5(Thomas J. 2016)), we
can plot out the dendrogram or segment (Figure. 6).
We can also cut the cluster dendrogram into
seven segments by moving the vertical distance
height around to height distance 10. Consequently,
the cluster 1 and 4 are split further, and 2 and 3
remain the same (Figure.6). The number of clusters
seems to be decided arbitrarily. Now, the issue is
how we chose an optimal number of clusters, “k.”
Figure 6:The Result of Cloud Market Segmentation.
4.2 Deciding Optimal Number
This is a challenging question. If the number is
predetermined, we can adopt other algorithms to do
the clustering, such as k-means. However, this
number is unknown. Fortunately, many existing
schemes can help us to estimate this number, such as
Dark Block Extraction (DBE) (Wang, 2009),
hierarchical, partitioning, direct, statistical testing,
density mode seeking, clumping, grid-based
clustering, etc. R has more than 30 methods or
indices to decide this optimal number (Charrad,
2014) developed “NbClust” package to decide the
number of clustering. Our analysis of Google data
shows the optimal number “k” is four (Figure. 7).
Figure 7: Optimal Number of Test Result by NbClust
Package.
The index shown in Figure7 is the Dindex
graphic to determine the optimal number of clusters.
Dindex is to measure clustering gain on intra-cluster
inertia, which is the degree of homogeneity between
the data points in a cluster. The equation of Dindex
can be presented as follows:






1


1






,





∈



(5)
Cloud Computing Market Segmentation
893

  









(6)
where P
q
is the “q” number of partitions by
imposing “k” number of clusters, “d” is the distance
and “c
k
” is the center of a cluster, “n
k
” is the number
of data points in a cluster. “x
i
” is any data point
within a cluster. The clustering gain on intra-cluster
inertia should be minimized. Ultimately, the Dindex
is to measure “the degree of homogeneity of the data
in a cluster.” (Charrad, 2014)
5 DEMAND PREDICTION
Ralph (Oliva,2012) suggested any B2B market
strategy has to focus on the object of Key Account
Market (KAM). In this case, ISP has to predict own
local cloud market demand so that ISP can achieve a
realistic sales forecast. This target can be either
arbitrarily or rational. If an executive team requires a
making- sense sales target, the forecast demand has
to come from a local dataset.
For the local ISP firm, the natural extension of
the cloud business is its existing web hosting
business. We can leverage the previous sales records
to estimate the cloud market demand. Our second
dataset has 3,192 data points (Windows servers
only) over 67 months (between Aug-2003 and Feb-
2009). We plot these data points monthly (Figure.8).
Figure 8: Local Hosting ServiceMonthly Dataset.
The red line in Figure 8 is to smooth the observation
data points. As we can see it, the sales volume is
quite low in the first 40 months but the movement of
the next 27 months was very volatile.
There are many different methods to estimate or
predict the future sales volume, such as logistic
regression, support vector machine (SVM), decision
trees or Classification and Regression Tree (CART),
random forests, and time series (TS). In comparison,
TS (Shumway, 2011) would be a better tool to
estimate the sales volume because the dataset is
collected in a time series. Moreover, it can give us
the monthly and yearly forecasting quantity or VM
sales. The result will be valuable for the cloud
capacity planning and budgeting.
Although the seasonal component is not
apparent, we still set the “gamma” value equals to
“False” to remove the seasonal components in the
TS model. We then use “forecast” package of R to
plot the next 12 months (Figure.9, up) and eight
years trends (Figure.9, bottom). We can see there is
a downward trend in sales volume for the monthly
but upward trend for the yearly forecasts.
Figure 9: VM Sales Prediction Results.
Now, the issue “Is the TS a valid model for the
forecasting?” We can plot the model residual to
visualize the errors trend. If we find any pattern in
the residual plot, it means the model is inadequate
for prediction. Otherwise, we can say it is a good TS
model. Based on Figure 10, we can see the residuals
are moving around zero.
Figure 10: Residuals of TS model Sales Volume.
We can also use both histogram plot and Auto
Correction Function (ACF) function (Figure. 11) to
valid the TS model. The histogram plot (left of
Figure11) shows a normal distribution and the ACF
plot (right of Figure. 11) shows there is only one line
that exceeds the boundary limit lines. So, we can
conclude the TS model is valid.
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894

Figure 11: TS Residuals Histogram and ACF plot.
If we adopt recent Gartner’s reports to assume
the average market share of Windows server is
around 36.56%, we can estimate the final result of
total VM quantity is 6,250 in 2009 (2,285 for
Windows servers) or market demand in next 10
years as noted in Table 2.
Table 2: VM Sales Yearly Forecast.
Year 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Win. Servers 2,285 3,241 4,197 5,153 6,109 7,065 8,021 8,977 9,933 10,889
All VMs Qty. 6,250 8,865 11,480 14,095 16,710 19,324 21,939 24,554 27169 29,784
Per our proposed solution in Table 1, we can
combine two test results for the final market
segmentation is shown in Table 3.
Table 3: The Final Result of Market Segmentation.
Segment Seg. 1 Seg. 2 Seg. 3 Seg. 4
Job Priority 2 1 0 3
Cores 1 1 23 11
Memory 6 5 6 99
% 10.05% 56.46% 22.97% 10.53%
Sales Vol. 593 3329 1354 620
Possible Workload Static Dynamic High Availability Backend
6 ANALYSIS AND DISCUSSION
Our three-step process solution shows how to
segment the B2B cloud market for the ISP to expand
its existing business from hosting to the cloud. The
novel idea of our solution is that it can practically
extract the cloud customer usage patterns from a
cloud trace dataset. The job priority (as shown in
Table 3) means the scheduling constraints on some
jobs. The most substantial proportion of cloud usage
(or workload) is the segment 2, which the most of
customers were using only one core and lower
amount memory. It is not surprising that Google
indicates users often overestimate their resource
consumption. In contrast, the lower priority jobs (or
backend data processing workloads) consume the
most significant amount of memory capacity
(Segment 4). Although the top priority job of
segment 3 consumes a lot of computing power (23
cores), the memory usage is relatively less.
Based on the limited parameters shown above
Table 3, we can probably guess what type of
workload is most likely even though Google data did
not provide this information. Segment 1 is more like
static web hosting workload; Segment 2 would be
dynamic (because of job priority ranking is high
than static one). Segment 3 is more like Highly
Availability workload, such as customer relationship
management(CRM) applications, and segment 4 is
more like backend workloads, such as database
backup or business analytics. One of the insights
from Table 3 is the cloud infrastructure, or a server
farm should be tailored into 12 units per cloud server
cluster. A memory configuration should be built in 6
GB per slot.
For the HC algorithm, it is essential to indicate
that one of the influencing factors for the optimal
number of the market segment is “seed,” However,
it does not only impact on the clustering method but
also other methods that require setting “seed.” In this
study, we assume there are no differences regarding
of usage patterns between B2C and B2B for the
Google’s dataset. By using HC algorithm, we can
meet the good market segment criteria
(Yankelovich, 2006) 3, 4 and 6. However, HC
algorithm alone is not enough because the input
dataset comes from a global dataset. It only provides
information about the customer behaviors.
The total demand estimation has to come from a
local B2B dataset for business strategy. Typically,
the sales’ target often becomes Key Performance
Index (KPI) for senior management. It is desirable to
use TS model for the local market demand because
the B2B cloud market is often built upon the long-
term B2B relationship. Furthermore, the purchasing
decision is made by a group of people rather than a
single individual. The TS can deliver both monthly
and yearly sales forecasts. By adopting TS model,
we can satisfy the criteria (Yankelovich, 2006) of
good market segmentation 1, 2, and 5. In
comparison with other solutions (Table 4), our
solution has the following advantages:
Table 4: Segmentation Solutions Comparison.
Different Methods
for Market
Segmentation
Customer
s’
Business
Values
Focus
Usage
pattern
Flexibl
e
Align
with
business
strategy
Specify
revenue
and profit
Make
sense
Analytic Method
√ √ √ √
Nested Method
√ √ √
Strategy-Based
√ √
Delphi Method
√ √ √
HC + TS
√ √ √ √ √ √
Cloud Computing Market Segmentation
895

• The solution is practicable and quantifiable,
which has the input variables (Table 3) for the
process of the B2B cloud market segmentation.
• The solution can quickly be updated for the
rapidly changing environment of the cloud
market, such as customer behaviors shift, the
internal investment budge variation, and the
cloud technology eruption.
• It can assist senior executives for a managerial
decision to test different local niche markets
that many global CSPs might not have a local
B2B relationship.
• The solution allows CSP to develop pricing
model based on both the market and customer-
value, which it emphasizes on both the external
rationality rather than internal rationality.
In contrast, the analytic method cannot extract
usage patterns, and the nested approach has to be
case-by-case. The strategy-based method is often
quite challenging to be translated into a practical
solution. Survey and Delphi methods often take too
long to be accomplished and often it is indirect.
To the best our knowledge, it is the first kind of
study on B2B cloud market segment. Many existing
and incoming CSPs require this kind of knowledge
to assist their cloud business investment strategy in
term of budgeting and resource capacity planning.
Market segmentation helps CSPs to find a better
pricing strategy for maximizing their profits.
7 CONCLUSIONS
This paper demonstrates how to combine both
Hieratical Clustering (HC) and Time Series (TS)
forecast to segment the cloud market and predict
market demands. In summary, we show HC + TS is
a better method to understand the market potential. It
is also very practical for any CSP to implement its
cloud market strategy by rolling out different pricing
models for various market segments. Our approach
allows CSPs to tailor their limited cloud resources
for the targeted customers. Moreover, CSPs can
optimize their cloud pricing beyond the reach of the
traditional cost-based cloud pricing. It leads to
opportunities for the CSP to maximize the revenue
and profits based on the various cloud customers’
utility and surplus. The details of how to define the
customer surplus or cloud customer utility functions
and how to establish and optimize different cloud
pricing models are our future works. We will
explore these two topics in future studies.
REFERENCES
Buyya R, et al., 2002, Economic models for resource
management and scheduling in Grid computing.
Concurrency and Computation, pp1507.
Weinman J., 2012, loudonomics, The Business Value Of
Cloud Computing, Hoboken, N.J. : Wiley, pp20
Wang W., et al., 2012, Towards Optimal Capacity
Segmentation with Hybrid Cloud Pricing. 2012 IEEE
32Nd International Conference On Distributed
Computing Systems, Distributed Computing Systems
(ICDCS), pp425
Berman S, et al., July 2012, How cloud computing enables
process and business model innovation. Strategy &
Leadership.40(4), pp27-35
http://www.businessinsider.com/visa-partners-with-banks-
for-cross-border-b2b-payments-2017-11/?r=AU&IR=
T (Business Insider, 2017)
Smith, R., 1956, “Product Differentiation and Market
Segmentation as Alternative Marketing Strategies.”
Journal of Marketing, vol. 21, no. 1, pp. 3–8.
Belleflamme, P & Peitz, M, 2015, Industrial Organization,
Cambridge: Cambridge Univ. Press, pp.195
Yankelovich, D., & Meer, D., 2006, Rediscovering Market
Segmentation, Harvard Business Review, Vol. 84,
Issue 2, pp.122-131
Claycamp, J. & William, M., 2000, “A Theory of Market
Segmentation.” Journal of Marketing Research, vol. 5,
no. 4, 1968, pp. 388–394.
Wedel, M, & Kamakura, WA, 1998, Market segmentation:
conceptual and methodological foundations / Michel
Wedel, Wagner A. Kamakura, Boston: Kluwer
Academic.
McDonald, M, & Dunbar, I., 2012, Market Segmentation.
how to do it and how to profit from it, Chichester:
John Wiley & Sons.
Wind, Y, & Cardozo, R,1974, Industrial Market
Segmentation,' Industrial Marketing Management, vol.
3, no. 3, pp. 153-165.
Kotler P. Feb-1964, Marketing Mix Decisions for New
Products. Journal of Marketing Research (JMR) 1(1):
pp. 43-49.
Ariel Pakes, A., 2003, A Reconsideration of Hedonic Price
Indexes with an Application to PC's. The American
Economic Review;(5):pp1578
Abramson D & Buyya R, Giddy J., Jan-2002, A
computational economy for grid computing and its
implementation in the Nimrod-G resource broker.
Future Generation Computer Systems. 1; pp18
Javadi, B. et al., 2011, Statistical Modeling of Spot
Instance Prices in Public Cloud Environments. 2011
Fourth IEEE International Conference On Utility And
Cloud Computing, Utility And Cloud Computing
(UCC), pp219
Hong X & Baochun L, 2013, A study of pricing for cloud
resources, SIGMETRICS Perform. Eval Rev, 40(4):
pp.3–12
Hande, P. et al., 2010, Pricing under Constraints in Access
Networks: Revenue Maximization and Congestion
Management. Proceedings IEEE INFOCOM
SE-CLOUD 2018 - Special Session on Software Engineering for Service and Cloud Computing
896

https://github.com/google/cluster-data/blob/master/Trace
Version1.md, (cluster-data 2011)
https://hbr.org/2005/09/building-loyalty-in-business-mark
ets (Narayandas, D., HBR, 2005)
Wang L, & Ramamohanarao K., 2010, iVAT, and aVAT:
Enhanced Visual Analysis for Cluster Tendency
Assessment. Lecutre Notes in Computer Science:16
Lawson, R G, Jurs, P C, Feb-1990, Journal of Chemical
Information & Computer Sciences., Vol. 30 Issue 1,
p36-41.
Brain H, J. G. S., 1954, A New Method for determining
the Type of Distribution of Plant Individuals. Annals
Of Botany;(70):pp.213
https://www.decisionanalyst.com/whitepapers/marketsegm
entation/ , (Thomas J. 2016)
Liang Wang, & Ramamohanarao K, 2009, Automatically
Determining the Number of Clusters in Unlabeled
Data Sets. IEEE Transactions on Knowledge and Data
Engineering, 3) pp.335.
https://www.jstatsoft.org/article/view/v061i06 (Charrad,
M., 2014)
Shapiro B, Bonoma T.1984, How to segment industrial
markets. Harvard Business Review.62(3): pp.104-110
Shumway R, Stoffer D. 2011, Time Series Analysis And
Its Applications: With R Examples, New York:
Springer
Kuo R, et al., 2016, An application of a metaheuristic
algorithm-based clustering ensemble method to APP
customer segmentation. Neurocomputing September
12; 205: pp.116-129
Wind Y, et al., Jul-1978, Market-based Guideline for
Design of Industrial Products, Journal of Marketing.
;42(3): pp. 27-37.
Verhallen T, et al., Jul-1998, Strategy-Based Segmentation
of Industrial Markets. Industrial Marketing
Management;27(4): pp.305-313.
Best R.,1974, An Experiment in Delphi Estimation in
Marketing Decision Making. Journal of Marketing
Research (JMR) November;11(4): pp. 448-452
McDonald M & Dunbar I., 2012, Market Segmentation.:
How To Do It And How To Profit From It,
Chichester: John Wiley & Sons, pp.255
Seufert E. Freemium, 2014, Economics: Leveraging
Analytics And User Segmentation To Drive Revenue,
Waltham, Massachusetts: Morgan Kaufmann.
Laughlin J, Taylor C., 1991, An approach to industrial
market segmentation. Industrial Marketing
Management, pp127
Plank R. A, May-1985, Critical Review of Industrial
Market Segmentation. Industrial Marketing
Management; 14(2):pp79-91
Balakrishna G. Feb-1980, Better Used the industrial
Marketing Concept. Industrial Marketing
Management, 9(1): pp71-76
https://www.shopify.com/enterprise/global-ecommerce-sta
tistics, (Global e-commerce ,2017)
https://www.nist.gov/news-events/news/2011/10/final-ver
sion-nist-cloud-computing-definition-published
(NIST, 2011)
Murtagh F & Contreras P., 2012, Algorithms for
hierarchical clustering: an overview, II. Wiley
Interdisciplinary Reviews-Data Mining And
Knowledge Discovery, 2 1, p86-p97, 12p.
https://en.wikipedia.org/wiki/Usage_share_of_operating_s
ystems (StatCounter, 2018)
Shin, K, 2013,'The Executor of Integrated Marketing
Communications Strategy: Marcom Manager's
Working Model'
Oliva, R. A., 2012, a High-Level overview: a value
perspective on the price of Business to Business
market, Handbook of B2B Marketing. Cheltenham,
Gloucestershire: Edward Elgar Publishing, pp.15-40
Thomas, R. J., 2012, B2B market segmentation,
Handbook of B2B marketing, Cheltenham, Glouces -
tershire: Edward Elgar Publishing, pp.182-207.
Cloud Computing Market Segmentation
897