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Summarize the trend 250 to 400 words. Remember, a trend is a quantifiable change in something over time. Don’t just talk post about something you think is a trend toward some type of technology. Yes, there’s a trend toward cloud computing but you need to find an article that quantifies a trend towards its adoption by tracking some sort of metric over time. Yes, there’s a trend toward IoT, but you need to find an article that quantifies a trend in some facet of IoT development. Yes, there’s a trend cybersecurity attacks, but you need to find an article that quantifies a trend in some facet.

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EVERYTHING AS A SERVICE (XAAS) ON THE CLOUD: ORIGINS, CURRENT AND

FUTURE TRENDS

Article · April 2016

DOI: 10.29268/stcc.2016.0006

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Services Transactions of Cloud Computing (ISSN 2326-7550) Vol. 4, No. 2, April-June 2016

EVERYTHING AS A SERVICE (XAAS) ON THE CLOUD:
ORIGINS, CURRENT AND FUTURE TRENDS

Yucong Duan1, Qiang Duan2, Xiaobing Sun3, Guohua Fu4,
Nanjangud C. Narendra5, Nianjun Zhou6, Bo Hu7, Zhangbing Zhou8

1,4Hainan University, Haikou, 570228, China
2Pennsylvania State University, USA

3School of Information Engineering, Yangzhou University, Jiangsu, China
5Cognizant Technology Solutions, India

6IBM T.J Watson Research Center, Yorktown Heights, NY 10598, USA
7Kingdee International Software Group, Hong Kong

8 China University of Geosciences (Beijing), Beijing, China
Email: 1duanyucong@hotmail.com, 2qiangduan@gmail.com, 3xbsun@yzu.edu.cn, 4fghfz328@163.com,

5ncnaren@gmail.com,6jzhou@us.ibm.com, 7bob_hu@kingdee.com, 8zhangbing.zhou@gmail.com
Abstract
For several years now, scientists have been proposing numerous models for defining anything “as a service (aaS)”,
including discussions of products, processes, data & information management, and security as a service. In this paper,
based on a thorough literature survey, we investigate the vast stream of the state of the art in Everything as a Service
(XaaS). We then use this investigation to explore an integrated view of XaaS that will help propose approaches for
migrating applications to the cloud and exposing them as services.
Keywords: Everything as a Service; Anything as a Service; Cloud computing; SOA
___________________________________________________________________________________________
1. INTRODUCTION
New IT paradigm is increasingly shaped by various
emerging trends especially Cloud Computing and Big Data
that can be identified by different “as a Service (aaS)”
models. The trend of providing everything as a service
(XaaS) (Duan et al., 2015) depicts a promising scenario
where service-oriented architecture and design supports the
development & deployment of software applications as
services1. However, it has been verified (Esteves et al.,
2011) that in the last several years related terms in this area
have been used arbitrarily creating some confusion. For
example, Esteves (Esteves et al., 2011) regarded XaaS as
the universe of all cloud deliverable services; whereas,
Robison et al. (Robison et al, 2008) proposed that
Everything as a Service refers to the services that have been
or will be migrated to the Cloud. This confusion should be
avoided via a unified classification based on a clear
understanding of the state of the art of various “aaS”. None
of the existing literature or effort has yet dealt with this
issue, which demands a survey covering the broad existing
work that extends back to the very early notions of services.
Based on the hypothesis that the classification under the
name of “aaS” partially reflects the trends of natural
evolution of services sharing common characteristics, we
present in this work our literature survey towards
describing a technical classification of various “aaS” which
include explicitly focused “aaS”, mentioned “aaS” and

1http://en.wikipedia.org/wiki/Service

implicitly derived “aaS” covering the work from traditional
IT applications. Through the survey on various “aaS”, we
make the following contributions in this article:
1) We provide a historical understanding of various
sources of the “aaS” notion, which would help forge a clear
meaning of “XaaS” as an active and continuously evolving
concept.
2) Alongside the general development routine of “SaaS
→ PaaS → IaaS → DaaS”, we explain the formation
of existing “aaS” in the Cloud, the migration of them to
the Cloud and their durations, and the prediction on the
trends of the formation of new “aaS” in the Cloud.
3) We also discuss the impact of the XaaS paradigm on
future development of information infrastructures and
service provisioning. We particularly review the trend of
convergence of networking and computing infrastructures
and unified Cloud and Internet service provisioning
enabled by the XaaS paradigm.
The rest of the paper is structured as follows: Section 2
presents our survey method and operation process. Section
3 shows origin of “XaaS” with collected “aaS” and
empirical classifications. Section 4 analyzes the
relationship among “aaS” and trends of application
migration towards the Cloud. Section 5 discusses the
impact of the XaaS paradigm on the development of future
information infrastructure and service provisioning. Section
6 concludes the work and discusses future directions.

2. SURVEY METHOD

mailto:qiangduan@gmail.com,

mailto:hu@kingdee.com

Services Transactions of Cloud Computing (ISSN 2326-7550) Vol. 4, No. 2, April-June 2016

We consider DBLP 2 as the primary search tool. The
search words include “as a service” and “as-a-service”. In
addition, we also browsed the following databases:
1) IEEE Xplore Digital Library http: //
ieeexre.ieee.org/Xplore/home.jsp
2) ACM Digital Library http://dl.acm.org
With the keywords and different databases, we snapshot
first 500 items in each database and select about 100 papers
full text for further analysis. The selection criteria include:
1) Selecting from as many papers with different uses of
the “* as a Service” terminology as possible. During the
searching process, we introduced keywords like “XaaS”,
“*aaS”, “EaaS’ and even “as services”.
2) Excluding disturbing papers, for example, papers
including “* as a Service” but focusing on “as a service
system”, “as a service archive”, “as a service voter”, etc.
While reading existing papers, we also searched earlier
published papers from their bibliographies. In this way, we
trace back to earlier related literature. We managed to keep
the balance between literature in the pre-Cloud era and the
Cloud era. For example, we kept Database-as-a-service
model proposed in (Hacigumus et al., 2002) in 2002 when
database was not yet proposed as a Cloud service, while
retaining DaaS proposed in the Cloud.

3. SOURCES,ORIGIN AND TYPES OF VARIOUS
“AAS”
3.1 INFORMATION COLLECTION
Figure 1 shows that most of the surveyed papers are
proceedings or journal articles and a few of them are
technical reports and books.

Figure 1. Distribution of surveyed literatures

Table 1. Origin, type and strength of various “aaS” by year

Year Literature Type Source of “aaS” EMD Abbr.
1984 Ives and Learmonth, 1984 traditional information E
1988 Howson et al., 1988 traditional mathematics E
1997 Beaumont et al., 1997 traditional ownership M

consulting D
1999 Kailer and Scheff, 1999 traditional education D

knowledge management E
Bennett et al., 2000 SaaS,ASP software E SAAS

2000 Sarawagi and Nagaralu, 2000 Internet data mining models E

2001 Edworthy, 2001 traditional health D

2 http://dblp.uni-trier.de

telemedicine

Fano and Gershman, 2002 traditional medical care M

2002
Furmento et al., 2002 WebService computational resource E

Hacigumus et al., 2002 SaaS,ASP
database

database management E

Figueiredo et al., 2003 programming virtual cpu M

Papazoglou, 2003 SaaS
business process D

transactions

2003
Perrey and Lycett, 2003 SOA

print D

quote

Sirin et al., 2003 WebService web service composition D
Viroli and Omicini, 2003 programming coordination E
communication M

2004 Dunkels et al., 2004 programming function M
program call M

Laitinen et al., 2005 network
authentication E

cellular authentication E

Panlilio et al., 2005 traditional health M

2005 Ott et al., 2005 WebService experiments E
knowledge E
Xu and Zhang, 2005 SOA application M
database M
Gilart-Iglesias et al., 2006 SOA,WebService industrial machines E IMaaS

2006 Lakshminarayanan et al., 2006 network routing E
van Deursen and Pieterson, 2006 traditional Internet M
Bender et al., 2007 network accountability E

Bottaro et al., 2007 traditional
media rendering D

washing machine

2007 Dan et al., 2007
programming data access D

SOA information

E IaaS
Emig et al., 2007 WebService identity E
Milanovic and Malek, 2007 SOA operating system E
Papazoglou et al., 2007 SOA function D

Cloud
hardware E HaaS

Aymerich et al., 2008 platform

E PaaS

2008
SaaS software E SaaS

Dwivedi and Kulkarni, 2008 SOA
data

data analytics

Robison et al., 2008 Cloud everything E
Agrawal et al., 2009 Cloud database management E
Cai et al., 2009 Cloud commerce E CaaS
Grossman et al., 2009 IaaS storage E
Itani et al., 2009 Cloud privacy E PaaS

Jansen et al., 2009 programming
component D

functionality

application M
Cloud business process M
UML modeling tools M
Kaufman, 2009

IaaS
hardware M

IT infrastructure management M

SaaS
custom relationship M

management

middleware M

2009
Maamar and Badr, 2009 SaaS social network E SNaaS

Cloud computing resources M

Patel et al., 2009 Cloud,SOA infrastructure M IaaS
SOA functionality D
Pauwels et al., 2009 traditional dashboards E
infrastructure E IaaS
business M BaaS
database M DaaS
Rimal et al., 2009 Cloud desktop M DaaS
development M DaaS
framework M FaaS
organization M OaaS
Rodr´ıguez et al., 2009 Cloud videoconference E VaaS
Singh et al., 2009 Cloud search E

Truong and Dustdar, 2009
Cloud storage M

SaaS,WebService data E

van der Aalst et al., 2009 SOA,WebService flexibility E FAAS
Bruneliere et al., 2010 SaaS modeling E MaaS
Candea et al.,2010 Cloud automated software testing E TaaS
Craciunas et al., 2010 Cloud information acquisition E
Dawoud et al., 2010 Cloud,SOA component M
Doerr et al., 2010 traditional music E
Kaliski Jr and Pauley, 2010 Cloud risk assessment E

IaaS
communication M

computing M

development M

2010 PaaS modeling M

Tsai et al., 2010
testing M TaaS

design M

Email M
SaaS ERP M

http://ieeexplore.ieee.org/

http://dl.acm.org/

Services Transactions of Cloud Computing (ISSN 2326-7550) Vol. 4, No. 2, April-June 2016

Office M
user interface M
Wang et al., 2010 SOA process E
Wood et al.,2010 PaaS disaster recovery E
Wu et al., 2010 cloud virtual machine D
Yu et al., 2010 SaaS testing E
Aho et al., 2011 Cloud IDE and hosting E

Alabbadi, 2011 cloud
education and learning E ELaaS

IT M ITaaS

Amelung et al., 2011 WebService E-assessment E
Banerjee et al., 2011 Cloud IT management D
Beimborn et al., 2011 SaaS Application based PaaS E aPaaS
Chen et al., 2011 SaaS,PaaS continuous analytics E CaaS
Chen et al., 2011 Cloud routing E RaaS

Christophe et al., 2011 WoT,Cloud
Things E

environment M

2011
Howe et al., 2011 PaaS database E

Mizusawa and Kitsunezaki, 2011 Cloud,network hybrid network E HaaS

Nascimento et al., 2011 Cloud
IP networks E

virtual routers E

Perakovic´ et al., 2011 Cloud secure communication D

Senk and Dotzler, 2011 SaaS
authentication E AaaS

Biometric authentication E BioAaaS

Subashini and Kavitha, 2011 Cloud capabilities D

Feng et al., 2011
Cloud,network networking E

programming network protocol M

Wang et al., 2011 SOA cashier E CaaS
Agmon Ben-Yehuda et al.,2012 IaaS resource E RaaS
Grier et al., 2012 SaaS exploit E

2012 Horey et al., 2012 IaaS big data platform E
Rajagopalan et al., 2012 Cloud disaster tolerance E
Tsai et al., 2012 Cloud threat E
La et al.,2013 SaaS component E

MPP database E
MPPDBa
aS

Wong et al., 2013 Cloud OLTP database M
2013 Parallel database M

auditing D
Zargari and Smith, 2013 Cloud forensics E
policing E
Cloud E-commerce D
Internet integrated AAA D
Bitterman et al., 2014 SaaS simulation E SMaaS

WebService
modeling D

training D

Black et al., 2014 Cloud,SOA E-health E eHaaS
Caminero et al., 2014 WebService laboratories E LaaS
business analytics E BAaaS
Chang, 2014 SaaS business intelligence E BIaaS
heston volatility and pricing E HVPaaS

Jingliang et al., 2014 Cloud,BigData
analysis E AaaS

value E VaaS

2014
data E DaaS

information E InaaS
Chen et al., 2014 Cloud,IoT

knowledge E KaaS
wisdom E WaaS
Chu et al., 2014 SOA,SaaS traffic analysis E

Cicic and Elmokashfi, 2014 Cloud
media network E MNaaS

telepresence E TPaaS

Jin et al., 2014 Cloud

content delivery E CoDaaS

content distribution M CoDaaS
Lomotey and Deters, 2014 Cloud,SOA analysis E
Perera et al., 2014 Cloud,IoT sensing E
Liu et al., 2014 Cloud consistency E CaaS
Varadharajan and Tupakula, 2014 IaaS security E
Yao et al., 2014 SaaS hospital information software E HI-

Table 1 shows our sorted sources of various “aaS” in the
time order from the earliest to the newest. We denote the
strength of the relativeness of different from strong to weak
with symbols of E(explicit), M(mentioned) and D(derived)
as are explained in Table 2.
We keep the original proposed abbreviation of the “aaS”
in the column of “Abbr.”. For example, we keep “SAAS”
which is created by Bennett et al. (Bennett et al., 2000) for
Software as a Service instead of replacing it with “SaaS”.
We filter those literatures which only propose an
abbreviation of “aaS” without any other content.

Table 2. Strength of the surveyed “aaS”
Abbr. Explanation

E Explicit: “aaS” is explicitly investigated.

M Mentioned: “aaS” is mentioned but not in
depth.

D Derived: we derived “aaS” based on the
content.

3.2 TYPES OF “AAS”
Classifying “aaS” is a challenging work since it involves
evolving terminologies. During the covered period from
1984 to 2014, the meaning of the “aaS” has been
continuously evolving. For example, software is proposed
as a service in 2000 however it is redefined in NIST’s
definition (Mell and Grance et al., 2011) as a Cloud service.
So we classify the former appearance of “SAAS” in 2000
under ASP (Application Service Provider) and classify the
latter appearance under Cloud. Towards revealing the
concept of “aaS”, based on our survey, we empirically

identify the following types of “aaS” as classifications
(which, however, may be overlapping). In this manner we
keep the complete evolutionary meaning of every “aaS”
and will also provide clues on service evolution and
migration towards the Cloud.
★ Traditional (Santos et al., 2013) These services are
provided either by individual people directly with concrete
actions, or nominally by institutions/society at conceptual
level but still implemented by real people who interact with
end users.
★ Network (Willig et al., 1979) The services are
applications running at the network application layer and
above which are based on application layer network
protocols for provision of data storage, manipulation,
presentation, communication or other capability in an end
to end/server architecture.
★ASP (Tao et al., 2001) The services are provided under
the Application Service Provider (ASP) model.
★Internet (Nguyen et al., 2003) The services are delivered
through Internet.
★Programming (Richard and Spencer et al., 2001) The
services are used in the context of programming, operating
system and as a daemon process in a computer system by a
process to response to users’ requests.
★SOA (Papazoglou et al., 2003) The category marks
services which follow the design pattern of
Service-Oriented Architecture in which distinct pieces of
software provide application functionality as services to
other applications via a protocol.
★Web Service (Newcomer and Lomow et al., 2004) It

Services Transactions of Cloud Computing (ISSN 2326-7550) Vol. 4, No. 2, April-June 2016

refers to software functions, at a network address with
machine-processable format of interfaces, provided over
the Web with Web-related standards.
★Cloud (Robison et al, 2008) These “aaS” are proposed
under the paradigm
of Cloud Computing which aims to leverage utility and
consumption of computing resources specifically related to
public, private or hybrid cloud infrastructures.
• IaaS: The services provide computing resources such as
virtual machines, servers, storage, load balancers, networks,
etc, with scalability according to customers’ requirements.
• PaaS: The services deliver computing platforms
including operating system, programming language
execution environment, database, and web server onto the
Cloud infrastructure without the cloud users’ need to
allocate resources manually.
• SaaS: The services are referred to as “on-demand
software” where the Cloud takes over the infrastructure and
platform while scaling automatically.
★Internet of Things (IoT): The services represent the utility
and resource perspective capability of the Internet of
Things (IoT) which converges technologies such as cloud
and mobile, partially relying on uniquely identifiable
embedded computing devices within the existing Internet
infrastructure.
★Web of Things (WoT): The services sit at a web scale
layer above the IoT where real world objects and cloud
services interact through the web.

4. THE ANALYSIS
4.1 STATISTICAL ANALYSIS
Figure 2 shows the curves representing the amount of the
proposed “aaS” categories in each year. The growth, top,
fall and the horizon of each curve represents the different
current states of each “aaS” categories ranging from the
growth phase, the top, the falling stage to the stabilized
phase. In general we can see that “aaS” as a whole has just
experienced a sharp grow from 2007 to 2009.
After reaching the top at 2009, the general curve
experienced two stages of falling from 2009 to 2012. After
2012, the curve begins to grow again with increased
acceleration until now. This falling at 2009 and the
subsequent raises and falls surprisingly coincides with the
Global financial crisis 3 in 2009 and the subsequent
remedy effort and struggles also coincides with the Great
Recession which lasted until 2012 following the financial
crisis. Based on the observation that the application of IT
technologies is very sensitive and interactive with the social
economic development, we boldly propose that the general
curve of the “aaS” reflects the investment and strategy on
“aaS” related researches in both academic and industry with

3http://en.wikipedia.org/wiki/Global_financial_crisis_in_2009

a delay of months. If this assumption is further confirmed,
we can predict the trends of “aaS” from the financial data.

Figure 2. “aaS” proposed by year

Table 3 shows a statistical view of surveyed “aaS” in the

time order from the earliest to the newest. The labels of “a,
b, c, d, e…” are used to mark the situations where the
counting includes the situation that more than one “aaS”
appeared in a single paper. We can observe an increase of
the counted amount from the left corner down to the right
corner. The data forms a general direction that traditional
services and old IT services in the higher part of the table
are migrating to the SOA implementation and the Cloud
platform in the lower part. Therefore we raise the following
hypothesis:
Service migration hypothesis: During the year past and
in the years ahead, services are migrating from traditional
areas and old IT infrastructure to the more advanced SOA
pattern and the Cloud.
We can also observe a branch of this general trend which
directed from “aaS” of SOA to “aaS” of the Cloud. We
would like to propose that the services applying SOA
partially contribute to the forming of an ecosystem which
fuels the service migration to the Cloud. We find that only
part of SOA and Web Service based services were moved to
Cloud environment, partially because some former services
are proposed on the conceptual level and are not further
developed.

Figure 3. Various “aaS” with strength indicators

Figure 3 shows the histogram of proposed “aaS” with

Services Transactions of Cloud Computing (ISSN 2326-7550) Vol. 4, No. 2, April-June 2016

different strength of relativeness marked with E(xplicit),
proposed, M(entioned) and D(erived), which denote strong,
medium and weak of the matureness of the concept of a
group of “aaS” respectively. In every group, we see that the
extent of “aaS” of the Cloud far exceeds the other types of
“aaS”, which reflects today’s reality. For every group of
“aaS”, the amount of “aaS” marked with E well exceeds the
amount of “aaS” marked with M and D. This shows that the
“aaS” as a whole is more at a mature defined stage than at
an immature conceptualization stage, thereby reflecting the
fact that “aaS” implementations are now at a stage where
they require standardization.
Figure 4 shows the ratio of E, M, D of every group of
“aaS”. For a specific group of “aaS”, the matureness can be
reflected by the ratios of amount(D)=amount(E) ,
amount(M)=amount(E),(amount(D)+amount(M))=amount(
E), etc. In general, the smaller these ratios, the more mature
the corresponding group of “aaS”. We can see that the
Cloud group and the SOA group are the most mature
groups and the Traditional and the Programming group are
the least mature groups. Therefore a lot of effort is needed
to be invested to help the immature groups to turn mature,
probably through migrating to the Cloud. For two groups of
A and B, the ratio of (amount(E(A)) : amount(M(A)) :
amount(D(A)))=(amount(E(B)):amount(M(B)):mount(D(B)
)) can reflect the similarity of matureness between them, etc.
Based on this similarity formula, we can observe that the
matureness of the Cloud is similar to that of SOA.
By combining Figure 2 and Table 3, we identified that

services under other classifications are migrating into
Cloud environments since related “aaS” are reappearing in
the Cloud.

4.2 VISUAL ANALYSIS

Figure 4. Various”aaS” by ratio of E, M and D

Through the literature survey, we construct the conceptual
hierarchy on top of the raw data of Table 1 in Figure 5
where each “aaS” is marked as a class with attributes of the
author, time, etc., for explicitly proposed “aaS”. The
classification relationship of “is a” or Generalization

Figure 5. Hierarchy of the concepts of explicit “aaS”

Services Transactions of Cloud Computing (ISSN 2326-7550) Vol. 4, No. 2, April-June 2016

among the classes of “aaS” is decided mostly by referring
to the related description in corresponding literature.
Although it might not be fully objective and precise, we
can still identify the following interesting phenomena:
•Prevalence of the Cloud: The Cloud related “aaS” are the
most prevalent, comprising almost half of all proposed
“aaS”, as shown in Figure 5.
•Migration to the Cloud: At the right of Figure 5 we find
that there are several “aaS” which belong to more than one
core “aaS”. We abstract a pattern of the model of “aaS”
migration as that of an “aaS” changing classification from
an older to a newer classification. By referring to the year
of these happening in Table 1, we found that the “aaS” of
SOA, Internet of Thing and Web of Thing, etc, are
migrating to the Cloud. And, the “aaS” under Traditional

services are migrating to the Cloud by way of SOA,
Internet of Things and Web of Things, etc.
In Figure 6 we use Python’s networkx library’s graph
drawer to generate the relationship network of all surveyed
“aaS” as a directed graph by inputting the name concept of
the surveyed “aaS” and its categories. It contains the
mentioned and derived “aaS” besides the explicit “aaS” in
Figure 5. Intuitively the strength of a core “aaS” can be
seen in the form of the amount of the congregated lines
related to it. Visually we find that with the surveyed “aaS”
the congregations of core “aaS” including the Cloud,
raditional, IaaS, SaaS, WoT, IoT, etc., in Figure 5 and
observations on the migrations trends from Figure 5 are
strengthened instead of being weakened or blurred.

Figure 6. Network view of all surveyed “aaS” (E, M, D)

Table 3. Classfication and distribution for “* as a Service”

Class
Expl
icit

Refe
rred

Deri
ved

Tot
al

19
84

19
87

19
97

19
99

20
00

20
01

20
02

20
03

20
04

20
05

20
06

20
07

20
08

20
09

20
10

20
11

20
12

20
13

20
14

Traditional 5 4 6 15 1 1 1 3 2 1 1 1 2 1 1
Network 6 0 0 6 2 1 1 2a
Internet 1 0 1 2 1 1
Programming 1 5 3 9 2 3 1 2 1
SaaS 3 0 2 5 1 2 2

b 6 4 4 14 2 3 3 2 1 1 1 1c

SOA WebService 7 0 3 10 1 1 1 1 1 2d 1 3
Total 13 4 7 24 1 3 4 1 4 2 3 1 2 4

28 15 6 49

3 17 4 11 2 6 6

IaaS 4 4 0 8 3 2 2 1
PaaS 3 3 0 6 4 2f 2

Services Transactions of Cloud Computing (ISSN 2326-7550) Vol. 4, No. 2, April-June 2016

Cloud SaaS 16 7 0 23 1 4 7 3 1 1 6g
Total 51 29 6 86 1 7 13 5 5 7 13
SOA 2 2 0 4 1 1 2
Big Data 2 0 0 2 2
Web of Things 1 1 0 2 2
Internet of Things 5 0 0 5 5
Total 10 3 0 13 1 1 2 9

Total 87 45 25 157 1 1 1 3 2 2 4 7 3 7 3 8 6 30 20 21 5 7 26

aAlso in cloud Environment
bServices that proposed under SOA, but did not point out how to implement
cAlso provided under SOA
dOne of them is provided under SaaS
eCloud services but not classified by the proposers
fOne of them is both SaaS and PaaS
gOne of them is under SOA

4.3 ANALYSIS REFERRING TO THE GARTNER HYPE
CYCLES
Gartner Hype Cycle methodology 4 uses a graphical
presentation to show how a technology or application will
evolve over time spanning over its maturity, adoption and
social application. Each Hype Cycle models a technology’s
life cycle with the five key phases: Technology Trigger,
Peak of Inflated Expectations, Trough of Disillusionment,
Slope of Enlightenment and Plateau of Productivity. After
comparison of the Gartner Hype Cycles with the data in
Table 1, we can observe the partial conformance of the
Hype Cycle in the table. According to the Gartner Hype
Cycle of Cloud Computing and IT in general during the
years of 2011-2014, Cloud service is currently from the
trough of disillusionment to slope of enlightenment. Figure
2 confirms this by showing that the “aaS” of the Cloud
experienced a blooming in 2009 corresponding to the phase
of Peak of Inflated Expectations, and then running at a low
level in 2012 and 2013 which confirms to the phase of
Trough of Disillusionment, and active again from 2014
might indicate that parts of the “aaS” of the Cloud have
started to enter the phase of Slope of Enlightenment. We
see the positions of Trough of Disillusionment, Slope of
Enlightenment of SaaS, PaaS and IaaS in the Cycles from
2011-2014 confirming to the distribution of the amount of
the appearance count during the corresponding periods. We
also observe the conformance between the Hype Cycles’
positions of the WoT and IoT related services on the rise
and the recent increase of the appearances of related “aaS”.
Referring to Table 1, we can identify that the amount of
“aaS” of IoT is on the rise which confirms to the Gartner
Cloud Computing Hype Cycle that IoT reaches the Peak of

4http://www.gartner.com/technology/research/methodologies/hype

-cycle.

Inflated Expectations jsp in 2014.

5. IMPACT ON FUTURE INFORMATION
INFRASTRUCTURE AND SERVICE
PROVISIONING
The development of current information infrastructures
and service provisioning is facing challenges coming from
diversity in two aspects – i) a wide spectrum of computing
applications with highly diverse service requirements; and
ii) the heterogeneous infrastructure resources, including
computing, storage, and networking systems, that are
utilized for service provisioning. The XaaS paradigm offers
a promising approach to integrating homogeneous
infrastructure resources for supporting diverse service
requirements; therefore, therefore will have a significant
impact on future development of information technologies.
As indicated by the analysis results shown in previous
sections, tradition IT applications are migrating toward the
cloud platform via the SOA principle. IaaS, PaaS, and SaaS
enables service-oriented abstraction of various
computational resources, including computing capacity for
data processing, memory and disk space for data storage,
communication capabilities for data transfer. Such uniform
abstraction greatly facilitates federated control and
management of heterogeneous resources for supporting the
wide variety of Cloud services that meet diverse
requirements of different applications.
The current service model assumes that Cloud services
are mainly provisioned by Cloud data centers. However, in
most cases, especially for public Clouds, end users can only
consume Cloud services by accessing the infrastructure
resources in data centers through some kinds of wide area
networks, typically the Internet. Therefore, the services
eventually received by end users are composition of
Internet service and Cloud service. Performance of network
services greatly influences the service quality perceived by

Services Transactions of Cloud Computing (ISSN 2326-7550) Vol. 4, No. 2, April-June 2016

end users. It will be desirable to have end-to-end services,
including both Internet services and Cloud services,
delivered through a unified platform. The latest progress in
both networking and Cloud computing technologies has
indicated that the XaaS paradigm offers a promising
approach toward this objective.
As service abstraction and provisioning being widely
applied in Cloud computing environments via the IaaS,
PaaS, and SaaS paradigms, the service-oriented
architectural principle also obtains adoption in the field of
telecommunications and computer networking. Decoupling
service functions from network infrastructures to enable
independent innovations in both fields is expected to be a
key attribute in the next generation Internet. SOA and Web
services-based technologies have been applied in
telecommunications and networking systems for enhancing
service provisioning. More recently, success of Cloud
computing inspired research on applying key Cloud
technologies, including virtualization and service models of
IaaS, PaaS, and SaaS, in the networking field, which may
enable virtual network services with Cloud features, such
as multi-tenant, elastic, on-demand service provisioning. In
general, such an emerging networking paradigm that allows
network resources and functionalities to be virtualized,
abstracted, accessed, and composed as “services” by
embracing the service-orientation principle is referred to as
Network-as-a-Service (NaaS).
NaaS inherits the merit of SOA that enables flexible and
effective loose-coupling collaboration across heterogeneous
networking systems for providing services that meet
diverse application requirements. Research efforts for
enabling NaaS can be tracked back to early 2000s. For
example, Parlay X (3GPP) was jointly developed by the
Parlay Group, ETSI, and 3GPP based on Web services
technologies, which exposes underlying network
capabilities to upper layer telecom applications through
abstract Web service interfaces. The Service Delivery
Framework (TM, 2009) proposed by TM Forum also
leverages the SOA principle to enable a general service
delivery platform to overcome the “silo” mode of telecom
service development and deployment. A transport stratum
was designed based on SOA to expose network
transportation functionalities as services (Branca et al.,
2010). The Service-Oriented Network Architecture (SONA)
developed by Cisco provides a framework for
implementing the IaaS strategy in the networking domain
(Cisco).
Software-Defined Network (SDN) and Network
Function Virtualization (NFV) are to recent innovations in
networking technologies that are expected to have
significant impacts on future network service. The XaaS
notion has also started obtaining it adoption in SDN and
NFV. Service delivery is challenging for large scale SDN
networks, especially in inter-domain networking scenarios.
The NaaS paradigm has been applied in SDN to address the
challenge of inter-domain end-to-end service delivery

(Duan et al., 2014). NFV Infrastructure as a Service
(NFVIaaS) is an important usecase of NFV as specified by
ETSI, in which compute, network, and storage resources
are pooled as common infrastructure elements to support
Cloud services as well as network services. In addition,
another important NFV use case, Virtual Network Function
as a Service (VNFaaS), makes virtual network
functionalities available to upper layer applications as
services, which is comparable to the SaaS notion in Cloud
computing (ETSI NFV ISG, 2013).
Recent development in micro-service architecture
(Namiot and Sneps-Sneppe, 2014) is expected to facilitate
adoption of the XaaS paradigm in resource-constrained
networking environments, such as wireless sensor networks
and smart home networks in the Internet of Things (IoT). A
micro-service is a lightweight and independent service that
performs single functions and collaborates with other
similar services using a well-defined interface. Due to its
light weight implementation and more flexible architecture,
micro-services offers a natural fit for service development
in Machine-to-Machine (M2M) communication scenarios;
therefore, it extends application of XaaS paradigm from
traditional networks such as Internet backbone, datacenter
networks, and cellular mobile networks to the emerging IoT
environment.
The XaaS paradigm, when applied to both computing
and networking fields, offers a promising approach to
bridging this two domains that used to be separated, thus
leading to a service ecosystem in which Cloud and network
services are unified. An architectural framework of
XaaS-based unification of Network and Cloud service
provisioning is shown in Figure 7. In such a framework, the
infrastructure resources, including networking, computing,
and storage systems, can all be abstracted as
SOA-compliant services through a unified mechanism.
Then, network and Cloud services may be orchestrated to
form composite services that are provisioned to end users.
Such a trend of Cloud-network service unification enables
a new service model in which the roles of traditional
Internet service providers and Cloud service providers
merge together into one role of composite network-Cloud
service providers. This new service model may stimulate
innovations in service development and create a wide
variety of new business opportunities. Network-Cloud
convergence allows a single-point of visibility of
computing and networking operations, providing the
opportunity to manage both more effectively. Such a
convergence also presents a vital part of an overall
cost-saving solution, in which business processes are
enhanced and time-to-market is shortened.

Services Transactions of Cloud Computing (ISSN 2326-7550) Vol. 4, No. 2, April-June 2016

Figure 7. XaaS-based Network and Cloud service

unification

Unification of network and Cloud service provisioning
has attracted research attention from both academia and
industry, and become a key topic in some major research
projects. For example, Cloud networking is an important
work package in the EU-funded SAIL (Scalable and
Adaptive Internet Solution) project (SAIL, 2012), which
developed architecture that virtualizes computing,
networking, and storage resources as infrastructure services
through the IaaS paradigm to enable composition of
computing and network services in a Cloud environment.
The FP7 UNIFY project, also sponsored by EU, aims at
developing a unified platform for converged network and
Cloud service provisioning (Császár, et al, 2012). UNIFY
envisions architecture where the entire network from
customer home devices to access networks, then core
networks, and finally data centers form a unified service
delivery environment.
With the XaaS paradigm, various infrastructure resources
are abstracted by following the service-oriented principle,
exposed via standard service interface, and cooperate with
each other through loose-coupling interaction mechanisms,
thus can be used as building blocks to construct complex
information infrastructure for supporting diverse service
provisioning. However, some technical challenges must be
fully addressed in order to realize such XaaS-based future
information infrastructure. One of the key challenges lies in
composition of the services that abstract heterogeneous
resources, such as compute, storage, network, etc. across
different autonomous system domains, such as Cloud data
centers and wide area networks. Although service
composition in Cloud environments have been extensively
studied, composition of network and Cloud services across
different domains to achieve unified end-to-end service
delivery is still an open problem that deserves more
thorough investigation; therefore, offer an interesting topic
for future research.

6. CONCLUSIONS AND FUTURE WORK
The service computing field had many advances in the
last years characterized partially by the emerging of various

“as a Service (aaS)”. New classifications and definitions are
proposed in a discretionary way. Despite some attempts, we
identified that there lacks a unified view to support an
agreed understanding of “aaS”. This paper has analyzed the
related literature and practical implementations describing
the diverse existing works covering explicitly focused
investigations, mentioned works and derived topics from
implicitly mentioned works, from both traditional services
and IT services. With identified classifications, we also
analysed the trends of service development partially
referring to the Gartner Hype Cycle. We demonstrated that
the information derived from the analysis confirms to the
existing knowledge sources including the Gartner Hype
Cycle. Based on the analysis, we also discussed the impact
the XaaS paradigm may have on the latest development
trend of information infrastructure and service provisioning.
We particularly review the NaaS paradigm that is enabled
by applying the SOA principles and Web service/Cloud
technologies in networking. The adoption of XaaS in both
computing and networking fields may allow convergence
of networking and computing infrastructures and
unification of network and Cloud services.
To fully explore the information such as the road of
migration to the Cloud as per Figure 5, we need to mine
more complex situations where concepts marking certain
“aaS” may be decomposed into more than one sub-concept
and in layered manner. We will continue to explore in this
direction. The categories of “aaS” and their relationships
can be referred for related companies to identify promising
“aaS” and foresee the trends of service migration towards
the Cloud. Currently some of the empirically created
classifications lack systemic guidance which well explains
the structure and interplay among interconnected “aaS”. To
this end, we will work towards creating an ontology model
for hierarchically organizing the key natural language terms
used in the names of various “aaS” to enhance the
classification and improve the precision of service
migration prediction based on collected “aaS” data. We will
also extend current survey to cover web pages and informal
reports which reflect industry interests and compare them
with research interests to see whether they follow one
another and where they intersect.

7. ACKNOWLEDGE
The authors acknowledge the support of the National
Natural Science Foundation of China (No. 61363007 and
No. 61662021) and Hainan Natural Science Foundation
(No. 20156234 and No.20156245) the HNU Research
program (No. KYQD1242).

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2015: 621-628

Authors

Yucong Duan received the Ph.D. in
Software Engineering from Institute of
Software, Chinese Academy of
Sciences, P.R.China in 2006. He is
currently a Full Professor and vice
director of Computer Science
department at Hainan University,
P.R.China. His research interests

include software engineering, service computing, cloud
computing, and Big data. He is a member of IEEE, ACM,
SSYSF and CCF (China Computer Federation).

Qiang Duan is currently an Associate
Professor of Information Sciences and
Technology at the Pennsylvania State
University Abington College. His
general research interests include data
communications, computer networking,
and the next generation Internet. Recent
research projects focus on

Software-Defined Networking, Network Virtualization,
Network-as-a-Service, and converged network and Cloud
service provisioning.

Xiaobing Sun is an associate professor
in School of Information Engineering
at Yangzhou University. He received
his Ph.D from Southeast University in
2012. His research interests include
change comprehension, analysis and
testing, reliable software evolution,
software data analytics, etc. He
published more than 50 papers in

referred international journals (STVR, IST, JSS, IJSEKE,
ADES, etc.) and conferences (ICSE, ASE, SANER,
COMPSAC, QSIC, etc.). He is a CCF and ACM member.

Guohua Fu received the Ph.D in
Management. He is currently the vice
president of Hainan Unversity. He is
among the pioneers to propose the
value Engineering analysis in
education in China. He has published
more than 100 research papers and
teaching material in Economics and

Management in both Chinese and English languages.

Nanjangud C. Narendra is currently
Principal Engineer in Research, Ericsso,
Bengaluru Area, India. He received the
Ph.D. from Rensselaer Polytech
Institute in 1991. His research interests,
include software engineering, service
oriented computing, cloud computing,
big data analytics, and Internet of

Things. He is a Senior Member of IEEE and ACM.

Nianjun Zhou is currently working for
IBM T.J Watson Research Center, USA.
He received the Ph.D. from Rensselaer
Polytech Institute in 2004. His interest is
using computer methodologies and
technologies to innovate new ideas,
develop new infrastructure and
applications that enhance the computing

resources utilities, knowledge and information
management.

Bo Hu is currently Director of Platform
Dept. of Kingdee Cloud & Bigdata,
Kingdee International Software Group.
He received the Ph.D. from Wuhan
University, China in 2011. He served as
vice chair of SSYSF China of Services
Society from 2014-2015. His interests
include Cloud Computing, SOA, Service

Computing, Big Data and Software Engineering.

http://www.3gpp.org/DynaReport/29199-01.htm

http://www.cisco.com/web/ME/solutions/ent/sona/index.html

http://www.etsi.org/technologies-clusters/technologies/nfv

http://www.sail-project.eu/deliverables/

https://www.linkedin.com/vsearch/p?company=SSYSF+China+of+Services+Society&trk=prof-vol_exp-org_name

Services Transactions of Cloud Computing (ISSN 2326-7550) Vol. 4, No. 2, April-June 2016

Zhangbing Zhou is a professor at the
School of Information Engineering,
China University of Geosciences
(Beijing), China, and an adjunct
associate professor at the computer
science department, TELECOM
SubParis, France. His research interests
include wireless sensor networks,
spatial and temporal database, and
service-oriented computing. He has

published more than 100 referred papers.

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4.1 Statistical analysis
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4.3 Analysis referring to the Gartner Hype Cycles

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