[email protected],[email protected], [email protected], [email protected] 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:[email protected],

mailto:[email protected]

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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/

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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

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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

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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"

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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 e

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

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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

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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.

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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). 8. REFERENCES O. Agmon Ben-Yehuda, M. Ben-Yehuda, A. Schuster, and D. Tsafrir. The resource-as-a-service (raas) cloud. In Proceedings of the 4th USENIX conference on Hot Topics in Cloud Ccomputing, pages 12–12. USENIX Association, 2012.

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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
4.2 Visual analysis
4.3 Analysis referring to the Gartner Hype Cycles