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Converged Network-Cloud Service Compositionwith End-to-End Performance GuaranteeJun Huang,Member, IEEE, Qiang Duan,Member, IEEE, Song Guo, Senior Member, IEEE,

Yuhong Yan,Member, IEEE, and Shui Yu, Senior Member, IEEE

Abstract—The crucial role of networking in cloud computing calls for federated management of both computing and networking

resources for end-to-end service provisioning. Application of the Service-Oriented Architecture (SOA) in both cloud computing and

networking enables a convergence of network and cloud service provisioning. One of the key challenges to high performance

converged network-cloud service provisioning lies in composition of network and cloud services with end-to-end performance

guarantee. In this paper, we propose a QoS-aware service composition approach to tackling this challenging issue. We first present a

system model for network-cloud service composition and formulate the service composition problem as a variant of Multi-Constrained

Optimal Path (MCOP) problem. We then propose an approximation algorithm to solve the problem and give theoretical analysis on

properties of the algorithm to show its effectiveness and efficiency for QoS-aware network-cloud service composition. Performance of

the proposed algorithm is evaluated through extensive experiments and the obtained results indicate that the proposed method

achieves better performance in service composition than the best current MCOP approaches.

Index Terms—Cloud computing, service-oriented architecture (SOA), network-as-a-service (NaaS), service composition, quality-of-service

(QoS)

Ç

1 INTRODUCTION

CLOUD computing has emerged as a novel distributedcomputing paradigm in which a pool of abstracted, vir-

tualized, dynamically scalable computing functions andservices are delivered on demand to external customersover the Internet [1]. In the cloud environment, cloud serv-ices normally represent remote delivery of computingresources and users obtain services from a cloud providervia networks (most often the Internet). Services received byend users consist of not only computing functions providedby cloud infrastructure but also communication functionsoffered by networks [2]. Recent research results have indi-cated that networking performance has a significant impacton cloud service quality [3], [4], [5]. The end-to-end perfor-mance evaluation between network and cloud systems con-ducted in [6] showed that networks may form a bottleneckthat limits cloud service performance. In addition research

reported in [7] found that networking system is an impor-tant element for improving cloud service reliability.

With increased differentiation in cloud providers’ offer-ings, allocation of application components on multiple pub-lic clouds become an attractive option. Research reported in[8] shows that such allocation provides significant cost sav-ings in various cloud use scenarios and also indicates thatthe delay and cost introduced by inter-cloud communica-tions have a strong impact on application performance.Recent development in cloud federation requires high-per-formance inter-cloud communications [9]. Hybrid cloudsthat compose resources in both private and public cloudinfrastructures are also widely adopted for cloud serviceprovisioning. In such a scenario, communication demandsand available bandwidth on the network links between pri-vate and public cloud infrastructures must be consideredfor scheduling components of workflows onto availableresources [10].

Therefore, networking plays a crucial role in cloud com-puting and becomes an indispensable ingredient for single,hybrid, and inter-cloud service provisioning [11], [12], [13],[14]. The important role that networking plays in cloudcomputing calls for federated control and management ofboth computing and networking resources in order toachieve high performance end-to-end service provisioning.To illustrate the impact of networking on cloud service per-formance, let us consider an example scenario in which aresearch lab creates 100 GB of raw data that will be proc-essed in Amazon EC2 cloud [15]. Suppose that the labobtains 10 EC2 virtual machine instances and each instancecan process 20 GB data per hour. Then the total processingtime of the cloud service is only 30 minutes. However, if thelab uses a network service that offers 200 Mb/s throughputfor data transmission to the EC2 server, then just the single-

� J. Huang is with the Institute of Electronic Information and Networking,Chongqing University of Posts and Telecommunications, Chongqing400065, China. E-mail: [email protected].

� Q. Duan is with the Information Science and Technology Department, ThePennsylvania State University, Abington, PA 19001.E-mail: [email protected].

� S. Guo is with the School of Computer Science and Engineering, The Uni-versity of Aizu, Tsuruga, Ikki-machi Aizu-Wakamatsu City, Fukushima965-8580, Japan. E-mail: [email protected].

� Y. Yan is with the Department of Computer Science and Software Engi-neering, Concordia University, Montreal, QC H3G 1M8, Canada.E-mail: [email protected].

� S. Yu is with the School of Information Technology, Deakin University,Burwood, Vic. 3125, Australia. E-mail: [email protected].

Manuscript received 11 May 2015; revised 7 Aug. 2015; accepted 7 Oct. 2015.Date of publication 0 . 0000; date of current version 0 . 0000.Recommended for acceptance by A. Walid.For information on obtaining reprints of this article, please send e-mail to:[email protected], and reference the Digital Object Identifier below.Digital Object Identifier no. 10.1109/TCC.2015.2491939

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2168-7161� 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

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trip delay for data transmission from the lab to EC2servers will be more than an hour. In this case networkdelay for round-trip data transmission contributes morethan 80 percent of the total service delay. This exampleshows that end-to-end services provided to cloud usersare typically combination of both network services andcloud services; therefore network QoS often contributes asignificant fraction of cloud service performance.

Until recent there exists a gap between the demands ofcloud computing for data communications and the servicesthat can be offered by traditional networking systems.High-performance cloud computing requires predictabilityin networking performance, coordination of both comput-ing and networking resources, and application-driven net-work control and management [16], [17]. However,traditional networks are designed specifically to support anarrow range of precisely defined communication services,which are implemented on fairly rigid infrastructure withminimal capabilities for ad hoc reconfiguration. Recentadvances in networking technologies, especially networkvirtualization, Software Defined Network (SDN), and appli-cation of the Service-Oriented Architecture in networking,has significantly enhanced network control and manage-ment for supporting cloud service provisioning [18].

Network virtualization, which decouples network ser-vice provisioning from data transport infrastructure, isexpected to be a key attribute of future networking [19]. Theemerging Network Function Virtualization (NFV) technol-ogy allows network functions to be virtualized and imple-mented on general purpose data center hardware [20]. Inthe NFV architectural framework recently published byETSI Industry Specification Group (ISG) [21] infrastructureresources of computing, storage, and network are allabstracted through a uniform virtualization layer. The vir-tual computing, storage, and network resources then aremanaged by a common orchestrator for end-to-end serviceprovisioning. As a potential enabler of profound changes inboth computing and networking, virtualization may bridgethe gap between these two fields; thus supporting federatedmanagement of computing and networking resources forcomposite service provisioning.

The SOA, which provides an effective architecturalprinciple for heterogeneous system integration, has beenwidely adopted in cloud service provisioning via the para-digms of Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS). Applyingthe SOA principle in network virtualization enables a Net-work-as-a-Service paradigm in which network resources areexposed and utilized as SOA-compliant services. TheNaaS paradigm enables networking functionalities to beutilized on-demand by end users through standard serviceinterfaces; much like computing capacities in clouds beingaccessed as services by users. Therefore, virtualizationcombined with service-orientation, is making transform-ing tradition network service provisioning toward a cloudIaaS service model. Such transform enables network serv-ices to be composed with computing services in a cloudenvironment; thus allowing a convergence of network-cloud service provisioning [22].

A key challenge to realizing notion of converged net-work-cloud service provisioning lies in QoS-aware service

composition across the networking and computingdomains. The objective of network-cloud service composi-tion is to select an appropriate sequence of network andcloud services to meet end-to-end service performancerequirements while optimizing networking and computingresource utilization. For example in the above lab data proc-essing case, if the user requires a maximum service delay(waiting time from sending data out to receiving resultsback), then the services for storage, computing, and net-working must be selected with a holistic vision to guaranteethe end-to-end service delay. A composite service for thisexample is illustrated in Fig. 1. In this figure cloud services1 and 2 are respectively Amazon S3 for storage and EC2 forcomputing. Network services 1, 2, and 3 respectively pro-vide data communications from the user to cloud service 1,between cloud services 1 and 2, and from cloud service 2back to the user. Selection of these cloud and network serv-ices must consider their delay performance in order to guar-antee that the end-to-end data transmission, storage, andprocessing delay meets user’s requirement.

In a general sense, service composition can be formulatedas the Multi-Constrained Optimal Path (MCOP) problem,which is known to be NP-hard. Although service com-position has been extensively studied, most of currentapproaches were mainly focused on web services or cloudservices instead of composite network-cloud services; there-fore may not achieve end-to-end QoS guarantee across net-working and computing domains. Due to the real-timerequirements of various network protocols, network-cloudservices typically need much faster composition algorithmsto achieve shorter response time. In contrast, web/cloudservice composition, though might have QoS requirementson composite services, typically allows best-effort responsetime of composition algorithms. Therefore, service com-position across network and cloud domains particularlyrequires more efficient and effective MCOP algorithms thatcan not only meet diverse QoS constraints but also achievemuch shorter response time.

Toward addressing this challenging issue, we investigateQoS-aware service composition in the converged network-cloud context from a general service provisioning perspec-tive. The main contributions made in this paper are summa-rized as follows.

� We present a model for QoS-aware service composi-tion for converged network-cloud service provision-ing. This model allows multiple QoS metrics to beconsidered in network-cloud services compositionwith guaranteed end-to-end service performance.

Fig. 1. An example of composite network-cloud service provisioning.

2 IEEE TRANSACTIONS ON CLOUD COMPUTING, VOL. 3, NO. X, XXXXX 2015

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� We formulate the problem of QoS-aware network-cloud service composition as a variant of MCOPproblem and develop an approximation algorithm tosolve the problem.

� We analyze the theoretical properties of the pro-posed algorithm. Analytical results show that ouralgorithm is effective and resilient in selecting serv-ices for network-cloud service composition andguarantees end-to-end performance for compositeservice provisioning.

� We conduct thorough experiments to evaluate per-formance of the proposed algorithm and comparethe algorithm against variations of current best-known MCOP algorithms. Obtained numericalresults indicate that the proposed algorithm is effi-cient and accurate for service composition.

The rest of this paper is organized as follows. In Section 2we first introduce a service-oriented framework for net-work-cloud service convergence, and then present a modelfor network-cloud service composition. We also formulatethe QoS-aware service composition problem in this section.Section 3 presents an approximation algorithm to solve theservice composition problem and gives theoretical analysison the proposed algorithm. In Section 4 we evaluate perfor-mance of the proposed algorithm and compare it with cur-rently available algorithms through numerical experiments.Section 5 briefly reviews the related research progress onservice composition and compares our research with relatedwork. Section 6 draws conclusions.

2 MODELING QOS-AWARE NETWORK-CLOUD

SERVICE COMPOSITION

2.1 A Framework for Network-Cloud ServiceConvergence

Network Function virtualization leverages some key tech-nologies developed for cloud computing to deliver networkservices upon data transportation infrastructure. Such keytechnologies include virtualization, which abstracts andpartitions computing as well as networking infrastructureby means of hypervisors, and orchestration mechanism thatcoordinates virtualized resources for service provisioning tomeet application requirements. SOA has been widelyadopted as the main model for cloud service provisioning,

in which various virtualized computing resources and soft-ware applications can be delivered as services to users.Applying the SOA principle in networking allows virtuali-zation of network resources and functionalities in the formof SOA-compliant services that can be composed with com-puting services in a cloud environment.

A service-oriented framework for network-cloud serviceconvergence is illustrated in Fig. 2. At the bottom of thisframework is a resource layer consisting of physical infra-structure for both networking and computing. Above theresource layer is the virtualization layer in which both net-working and computing resources are virtualized andabstracted as SOA-complaint services. The virtualizationlayer offers an abstraction of the underlying networking andcomputing resources through a standard service interface,which allows cloud service provisioning to be realized acrossheterogeneous infrastructure domains including both net-working and computing systems. Such an abstraction alsodecouples underlying infrastructure from upper layer ser-vice provisioning functions; thus enabling more flexible ser-vice development and provisioning. The service provisioninglayer above the virtualization layer discovers and selectsboth network and cloud services and orchestrates them intocomposite network-cloud services that meet the require-ments of end users. In this framework the SOA-based NaaSparadigm enables networking resources to be virtualizedand exposed as services and composed with cloud comput-ing resources into composite network-cloud services. Thisframework offers a uniform mechanism to access both net-working and computing resources; thus significantly facili-tating federated management across network and clouddomains for end-to-end service provisioning.

Again take the lab data processing scenario as an exam-ple, this convergence framework enables networking sys-tems of various providers, such as Verizon, AT&T, andComcast, to be virtualized and offered as SOA-compliantnetwork services through the NaaS paradigm, just like com-puting and storage resources in cloud to be offered throughthe IaaS paradigm (e.g. Amazon EC2 and S3 services). Thenappropriate network services as well as cloud services areselected and composed for meeting the performancerequirement for lab data processing.

The above SOA-based framework of network-cloud ser-vice composition enables a much wider range of communi-cation services with more attributes than what can beoffered by traditional networking technologies. Networkingresources, virtualized and encapsulated in SOA-compliantservices, may be combined in almost limitless ways withother service components that abstract both computationaland networking resources; thus greatly expanding the serv-ices that can be offered by co mposite network-cloudsystems. NaaS also offers the ability to match cloud require-ments to communication functions through discovering andselecting the appropriate network services and composingthem with cloud services.

2.2 System Model

A typical end-to-end provisioning system for compositenetwork-cloud services consists of both computing infra-structure that offers cloud services and network infrastruc-ture that provides services for cloud access and inter-cloud

Fig. 2. The layered structure of a service-oriented framework for net-work-cloud service convergence.

HUANG ET AL.: CONVERGED NETWORK-CLOUD SERVICE COMPOSITION WITH END-TO-END PERFORMANCE GUARANTEE 3

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communications. In order to achieve service performanceguarantee to an end user, the end-to-end service systemmust expect a certain level QoS from each cloud service andnetwork service involved in service provisioning. In gen-eral, such QoS expectation can be specified in form of Ser-vice Level Agreements (SLAs) between the end-to-endservice provisioning system and the providers of cloud andnetwork services.

When a user submits a service request to the service pro-visioning system, it will specify information of the followingtwo aspects: a) functional requirements including requiredfunctionalities and the inter-relationship between the func-tionalities; b) performance requirements for example theminimum service capacity and the maximum service delay.Functional requirement can be specified in form of a work-flow that gives a set of service component (each has certainfunctionality) and the interconnections between the servicecomponents. In a converged networking and cloud com-puting environment, the functionality of each service com-ponent may be realized by multiple available services,referred to as candidate services for the service componentin this paper, which may offer different level of perfor-mance (e.g. latency and capacity). Therefore, for each ser-vice request, a service broker will invoke a compositionprocess to select a series of available service candidates toform an end-to-end composite service that can meet the per-formance requirements specified by the user.

Fig. 3 shows a model for network-cloud service composi-tion. The end-to-end service has H service components, Si,i ¼ 1; 2; . . . ; H, where the number and sequence of servicecomponents are decided by the user’s request. Each servicecomponent Si has li candidate services Sij, j ¼ 1; 2; . . . ; li;that is, the functions of component Si may be realized byany candidate service Sij. Available candidate services for aservice component are pre-selected based on the functionalrequirements for the component specified in the userrequest and service descriptions published by available net-work and cloud services. A service component in the modelcould either perform computing functions for data processand/or storage or networking functions for data communi-cations. As illustrated in Fig. 3, the candidate services of aservice component are connected to the candidate servicesfor the next-stage service component through links, whichrepresent data transmissions provided by network services.Then all the candidate services together with the data

transmissions between them form a graph. Each path tra-versing through this graph from S0 to Sl represents one pos-sible composition of candidate services. In this paper, werefer such a path a service path, which represents a particularway for realizing the workflow specified by the end user.

This model allows network-cloud service composition totake QoS requirements into account naturally. Performancemetrics can be assigned to each service candidate and to thelinks connecting service candidates as well. Then the metricof a path traversing a set of candidate services representsthe QoS attributes of a composite service comprising the setof candidate services. For example in the model shown inFig. 3 multiple paths exist from S0 to Sl, which representsdifferent realization of the composite end-to-end serviceconsisting components S1; S2; . . . ; SH . If we assign delay asthe metric to each candidate service, then the path with theminimum total metric value, say the path S0 ! S11 !S22 ! � � � ! SHlH ! Sl, will give a composite service that

achieves the optimal delay performance. Therefore, select-ing an optimal service path from a pool of network andcloud candidate services while satisfying various QoSrequirements is the main optimization focus for convergednetwork-cloud service provisioning.

In thismodelwe assume that service providers always col-laborate with each other; that is, there is no contention amongall network and cloud services. We also assume a standardinterface between the service broker and the available atomicservices for service orchestration. Such an interface offers ser-vice abstraction that makes implementation details of theseservices transparent to the service broker and service con-sumers; thus overcoming ”lock-in” issue of cloud computing.

As an illustrative example we still consider the lab dataprocessing case. Suppose the end-to-end service requestedby the user include cloud data storage, cloud data process,as well as data transmission from user to storage, from stor-age to the process server, and from the server back to theuser. The path of the end-to-end composite service is shownin the upper part of Fig. 4. This path consists of five servicecomponents. Two cloud service CS1 and CS2 for data stor-age and data processing respectively. Network service NS1

transmits raw data to cloud storage, NS2 transports datafrom storage to cloud server for processing, then NS3 trans-mits process results back to the user.

There are multiple candidate services that may be used torealize each service component in the path, as illustrated in

Fig. 3. Modeling for network-cloud service composition.Fig. 4. An example of service composition for network-cloud serviceprovisioning.


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the lower part of Fig. 4. For example in this case Amazon S3,Google Drive, and Microsoft Skydrive are all available forcloud storage service. Similarly Amazon EC2, GoGridServer, and Google Compute Engine are candidate servicesfor cloud data process. Available service candidates for real-izing network service components (NSi; i ¼ 1; 2; 3) are repre-sented by the connection links in the lower part of Fig. 4. Inthis example network services provided by AT&T network,Verizon network, and Comcast network are available candi-date services that can be selected to realizeNSi; i ¼ 1; 2; 3.

Each path from S0 to Sl in the graph shown in the lowerpart of Fig. 4 gives one possible realization; i.e., a workflow,for the composite service. In order to achieve end-to-endQoS provisioning, a service path that meets the QoS require-ments specified by the user must be chosen. If we assignmetrics to each node (e.g. capacity and latency for cloudstorage and process) and each link (e.g., bandwidth anddelay for data transmission) in the graph, then a composi-tion algorithm may select an optimal path for meeting therequirements. For example the user requires a maximumservice delay for getting data process results back and thenthe highlighted path is selected for meeting such a delayrequirement. The selected service path uses Comcast net-work for transmitting data to Amazon S3, using AT&T net-work for communications between Amazon S3 andAmazon EC2, then using Verizon network for transmittingprocess results back to the user. The total delay on the ser-vice path, including latency introduced by Amazon S3 andEC2 and transmission delay in Comcast, AT&T, and Veri-zon networks, is less than the required end-to-end servicedelay. Therefore, the QoS-aware composition mechanismshould support federated service composition across net-work and cloud domains in order to achieve optimal end-to-end service performance.

2.3 Problem Formulation

Given the converged service provisioning model, we canformulate the problem of QoS-aware network-cloud servicecomposition as follows

A service graph formed by interconnecting candidateservices of a series of service components can be modeledas a directed graph GðV;EÞ with n vertices and m edges.Each vertex represents a cloud service that is associatedwith two QoS metrics: capacity c and processing latency d.Each edge represent a network link for data transmission,which is associated with K QoS parameters w ¼ ðw1;w2; . . . ; wKÞ. Note that QoS parameters can be either positive(quality increases as parameter value increases, e.g., reliabil-ity) or negative (quality decreases as parameter valueincreases, e.g., delay), the latter type are considered in themodel because positive ones can be transformed to be nega-tive by taking reciprocal. In addition, QoS parameters oneach link are supposed to be additive since any concave met-ric (e.g. bandwidth) can be pre-processed via eliminatingthe corresponding vertices and edges that do not satisfy theQoS constraints [23]. That is, we focus on the negative andadditive QoS parameters in the model.

Let R ¼ ðC;D;W1;W2; . . . ;WKÞ denote the performancerequirement specified in a user’s request, where C is the ser-vice capacity constraint, D is the processing latency con-straint, and ðW1; . . . ;WKÞ areK end-to-end QoS constraints.

A series of selected services can be represented as a path pin the service graph.

Definition 1. Feasible Service Composition. A composed service,i.e., a path p from service entrance portal to service exit portalin the service graph, is said to be feasible if 8v 2 p,1

cðvÞ � 1C ;

Pv2p dðvÞ � D;wkðpÞ �Wk for all 1 � k � K.

Denote fpfg as all feasible service compositions in

GðV;EÞ. For each pfi 2 fpfg, there exists a smallest value

hi 2 ð0; 1� such that wkðpfi Þ � hi �Wk, 1 � k � K.By the above definition, we now formulate the QoS-

aware service composition problem.

Problem 1. QoS-aware Service Composition (QSC). QSC is tofind an optimal path popt among feasible service compositionsin GðV;EÞ and the corresponding smallest value hopt amongall hi such that wkðpoptÞ � hopt �Wk, k 2 ½1; K� whereK � 2.

It has been proved that problem QSC is NP-hard [24] asthe solution of QSC in the absence of vertex constraints,namely, capacity and processing latency, maps directly thatof MCOP, which is a well-known NP-hard problem. Toattack it, approximation algorithm has been realized as apowerful tool. Formally, an algorithm is a b-approximationalgorithm (or simply, an approximation algorithm) for QSCif the algorithm generates a service path p such thatwkðpÞ � b � hopt �Wk, and the running time of the algorithmis bounded by a polynomial in the input size of the instanceas well as in 1=b.

Table 1 lists the frequently used notations in the rest ofthis paper.

Please note that although the concept of a service path pused in Definition 1 and Problem 1 can represent not only aseries of candidate services invoked in a sequential order,but also atomic services executed with other flow structuresincluding parallel, case, condition, and loop as shown inFig. 5. From a perspective of QoS-aware service composi-tion, a service path with these flow structures may be con-verted to a sequential path based on the following lemma.

Lemma 1. Computation of the service capacity, processinglatency, and end-to-end QoS constraints for service paths inparallel, case, condition, and loop flow structures can be trans-formed to computation of the corresponding constraint param-eters for a service path in the sequential structure.

Proof. Assume fS1; S2; . . . ; Shg is a set of candidate services,they are executed in parallel, case, condition, and loop

TABLE 1Notations and Symbols

n number of nodes in GðV;EÞm number of links in GðV;EÞc capacity of noded processing latency of nodeK number of QoS constraints on each linkH number of services� approximation ratio of the algorithmw ¼ ðw1; w2; � � � ; wKÞ QoS parameters on each linkR ¼ ðC;D;W1;W2; � � � ;WKÞ user’s requestC capacity constraintD processing latency constraintW1;W2; � � � ;WK K QoS constraints


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fflow structures, which are shown in Figs. 5a, 5b, 5c,and 5d, respectively. Without loss of generality, con-sider each node is associated with a capacity (denotedby c) and a processing latency (denoted by d), eachedge is associated with two weights (more weightscould be added), i.e., cost (denoted by w1) and delay(denoted by w2).

If the services execute in the parallel structure, allpaths between S0 and Sl would be executed simulta-neously. The capacity, processing latency, cost, and delayfrom S0 to Sl (denoted by c0, d0, w01, and w02 respectively)can be expressed as

c0 ¼Xhi¼1

cðSiÞ;

d0 ¼ maxh

i¼1dðSiÞf g;

w01 ¼Xhi¼1

w1ðpiÞ;

w02 ¼ maxh

i¼1w2ðpiÞf g;

(1)

where pi represents the path S0 ! Si ! Sl.If the services execute in the case structure, the process

would be terminated when one of the paths is executed,and thus we have

c0 ¼ cðSi�Þ;d0 ¼ dðSi�Þ;w01 ¼ w1ðpi� Þ;w02 ¼ w2ðpi� Þ;

(2)

where i� ¼ argminhi¼1 dðSiÞ þ w2ðpiÞf g.

For condition structure, we have

c0 ¼Xhi¼1

Probi � cðSiÞf g;

d0 ¼Xhi¼1

Probi � dðSiÞf g;

w01 ¼Xhi¼1

Probi � w1ðpiÞf g;

w02 ¼Xhi¼1

Probi � w2ðpiÞf g;

(3)

where Probi is the execution probability of Si such thatPhi¼1 Probi ¼ 1.For loop structure,

c0 ¼ T � cðSiÞ;d0 ¼ T � dðSiÞ;w01 ¼ T � w1ðpiÞ;w02 ¼ T � w2ðpiÞ;

(4)

where T is the loop count. tu

Lemma 1 indicates that service selection in a servicegraph with general flow structures can be converted toselection of a service path with a sequential order. There-fore, for a service graph with general flow structures init, we can first apply Lemma 1 to transform paths in par-allel, case, condition, and loop structures to the sequen-tial structure, then focus on selecting tandem candidateservices to form a sequential service path that meetsend-to-end QoS requirements.

For example in the illustrative case for lab data process-ing (shown in Fig. 4), if parallel network paths exist betweenAmazon S3 storage and Amazon EC2 server and the net-work management policy of the service provider (e.g.AT&T) allows parallel data transmission, then we can trans-form the set of parallel network links from S3 storage toEC2 server to one atomic network service, whose band-width is the total bandwidth of all links and delay is themaximum delay among all links.

We assume that the path specified by a user request is ina sequential structure. In this work we mainly focus onaddressing the challenges brought in by high-level servicecomposition across the networking and cloud computingdomains instead of low-level service composition withincloud systems. (The latter has been studied in variouspapers as discussed in Related Work section.) Sequentialpaths are typical, although not the only case, for network-cloud service composition. For example user data are firsttransmitted via a network service to a cloud server, thenprocessed by the cloud service, and then results are deliv-ered by a network service back to the user, as illustrated inthe lab data processing case. Please note that non-sequentialpaths are possible within either a cloud (or network) servicecomponent, for example parallel or loop data process insidethe Amazon EC2 service, but are encapsulated by a singlecloud (or network) service component.

Fig. 5. Parallel, case, condition, loop execution structures of atomic can-didate services. S0 and Sl are service entrance portal and service exitportal, respectively.


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3 AN APPROXIMATION ALGORITHM FOR NETWORK

AND CLOUD SERVICE COMPOSITION

3.1 Algorithm

To resolve the QSC problem, we in this section propose anapproximation algorithm for network-cloud service compo-sition. The algorithm addresses both node and edge con-straints in a service graph to achieve composition acrossnetwork and cloud services with end-to-end performanceguarantee. Specifically, this algorithm is developed basedupon a preprocessing procedure, named PrePro, shown inAlgorithm 1. The PrePro first constructs a new graph bysimply duplicating the original service graph, and thenapplies Lemma 1 to convert parallel, case, condition, loopstructures to the sequential ones in this new graph. Next,PrePro removes the node capacity constraint and migratesthe processing latency constraints onto the edge constraints.As a consequence, QSC problem is formulated as an MCOP.

Algorithm 1. PrePro

Input: Service Graph GðV;E; c; d; wÞ, R.Output: G0ðV 0; E0; w0Þ.1 Construct a graph G0ðV;E; c; d; wÞ by duplicatingGðV;E; c; d; wÞ;

2 Apply Lemma 1 to transform parallel, case, condition, loopstructures to the sequential ones;

3 while to each u 2 V 0 do4 if 1

cðuÞ >1C then

5 Update V 0 and E0 by removing u from V and itsconnected edges from E;

6 Continue;7 end8 To each u’s outgoing edges ðu; v1Þ; ðu; v2Þ; . . .,

w01ðu; viÞ w1ðu; viÞ; � � � ;9 w0kðu; viÞ wkðu; viÞ þ dðuÞ;10 w0kþ1ðu; viÞ wkþ1ðu; viÞ; � � � ;11 w0Kðu; viÞ wKðu; viÞ, where wk is assumed to be the

delay over the link.12 end13 return G0ðV 0; E0; w0Þ;

As shown in Algorithm 1, PrePro trims the service graphtopology according to the capacity constraints. If capacity ofa node in the service graph is smaller than the required con-straint, the node and its associated links will be eliminatedfrom the graph (lines 4 to 7 in Algorithm 1). Therefore, theremaining service graph satisfies all the capacity con-straints. While for the latency constraint, PrePro migratesthe processing latency from each node to its outgoing links(lines 8 to 11 in Algorithm 1). For example, consider a ser-vice path from the entrance portal S0 to a service node uand then to the exit portal Sl that the delay of link S0 ! u is50 ms, u has a processing latency 100 ms, and the delay oflink u! Sl is 20 ms. PrePro migrates the 100 ms latencyfrom node u onto the link u! Sl; thus getting the followingequivalent metric assignment on this path: delay of linkS0 ! u is 50 ms, processing latency of u is zero, and delayof link u! Sl is 120 ms. Such transform performed by Pre-Pro enable the QSC problem to be formulated as a MCOP.

It is straightforward to derive that the time complexity ofPrePro is Oðmþ nÞ. But one should be noticed that in the

PrePro, transferring processing latency to link delay will notviolate the QoS of parallel tasks because this step is executedin the new graph obtained by the structure conversion pro-cess (lines 1 and 2 inAlgorithm 1). Based onPrePro algorithm,we propose an approximation algorithm named SCA (showninAlgorithm 2) for network-cloud service composition.

Algorithm 2. SCA

Input: Graph GðV;E; c; d; wÞ, R,H, �.Output: Path p.1 Call PrePro to obtainG0ðV 0; E0; w0Þ, setW 0

k Wk, 2 � k � K,andW 0

k Wk þD;2 To each e 2 E0 in G0ðV 0; E0Þ, compute the new weights

wNk ðeÞ ¼

w0kðeÞ

W 0k� H�1�

� �, 2 � k � K, and set

WN1 ¼ � � � ¼WN

K ¼ D ¼ Hþ1�

� �;

3 for dk ¼ 0 to D, 2 � k � K do4 dv½d2; � � � ; dK � 1, 8v 2 V ;

pv½d2; � � � ; dK � NULL; ds½d2; � � � ; dK � 0;5 end6 for all ðd2; � � � ; dKÞ 2 f0; 1; � � � ;DgK�1 in increasinglexicographic order do

7 for each ðu; vÞ 2 E s.t. dk ¼ �k þ wkðu; vÞ � 0 do8 if dv½d2; � � � ; dK � > du½�2; � � � ; �K � þ w1ðu; vÞ

then9 dv½d2; � � � ; dK � du½�2; � � � ; �K � þ w1ðu; vÞ;

pv½d2; � � � ; dK � u;10 end11 if dv½d2; � � � ; dK � > dv½d2; � � � ; dj � 1; � � � ; dK �

then12 dv½d2; � � � ; dK � dv½d2; � � � ; dj � 1; � � � ; dK �;

pv½d2; � � � ; dK � pv½d2; � � � ; dj � 1; � � � ; dK �,2 � j � K;

13 end14 end15 end16 if dt½d2; � � � ; dK � � D then17 Find a source-destination path p in GðV;EÞ s.t.

w1ðpÞ � D and wkðpÞ � dk, 2 � k � K;18 end19 if wkðpÞ �Wk, 2 � k � K then20 return path p;21 else22 returnNo feasible path, EXIT;23 end

SCA comprises four key steps. The first step (line 1)applies PrePro to transform the QSC into a MCOP. As aresult, the capacity and processing latency on each node areremoved. In the second step (line 2), the service graph issimplified via a ceiling operation on both weights and con-straints. This ceiling operation follows the rounding methodin [25] that dramatically reduces the search space, thus facil-itating the problem to be solved.

In the third step (lines 3 to 15) we assume that nodes sand t denote the service entrance portal and the service exitportal, respectively. The notation dv½d2; � � � ; dK � representsthe least first weight, namely, w1 among path p from s to vsuch that wkðpÞ � dk; 2 � k � K. Notation pv½d2; � � � ; dK � isused to record the predecessor of v on the path p such thatw1ðpÞ ¼ dv½d2; � � � ; dK � and wkðpÞ � dk; 2 � k � K. Note that


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this step is a dynamic programming process for tacklingthree or more constraints, which follows the same philoso-phy of relaxing operation in Dijkstra.

The fourth step examines the feasibility of the path gener-ated by the second step. If the path satisfies wkðpÞ �Wk,2 � k � K, the algorithm completes with this path as an out-put; otherwise the algorithm terminateswith no feasible path.

We want to point out that the proposed model and algo-rithm for network-cloud service composition are applicableto multi-tenant service provisioning environments, whereresource contention may exist in the underlying networkand cloud infrastructures. In our model, the QoS metricsassociated with the service graph reflect the currently avail-able amount of resources. We assume that the infrastructurecontroller regularly collects such information and make itavailable to the service composition module, where the pro-posed algorithm is performed. Whenever a newly createdcomposite service is deployed, the required amount ofresources will be allocated in network and cloud infrastruc-tures. Then the infrastructure controller will update theresource availability information, based on which the ser-vice composition module will update the QoS metrics onthe service graph. When searching a service path for a newservice, the PrePro step of the proposed algorithm removesnodes and links that have insufficient capacities from theservice graph; thus avoiding using such links/nodes for thenew service. Some available network and cloud manage-ment technologies may be applied to support collecting andupdating the availability information of infrastructureresources, for example OpenDaylight platform for networkcontrol and OpenStack API for cloud management.

Now we analyze theoretical properties for SCA.

3.2 Analysis

Theorem 1. The worst-case time complexity of Algorithm SCA is

OðmðH� ÞK�1Þ time.

Proof. The first and second steps of SCA for processing thetopology spends OðnþmÞ time. The third step takes

OðmðHþ1� ÞK�1Þ time in the worst-case to calculate the

path. Checking the feasibility of the obtained path in thefourth step costs OðKÞ time. Therefore the worst-case

time complexity of SCA is Oðmþ nþmðHþ1� ÞK�1þKÞ ¼ OðmðH� ÞK�1Þ. tu

Theorem 1 indicates that the proposed SCA is faster than thebest currently avaiable MCOP algorithm, i.e., FPTAS [26].This is due to the fact that the number of hops of the final pathis known a priori in the context of service composition, whichguides the SCA to find the path in a smaller searching space.In addition, the space overhead of SCA is also much smallerthan that of FPTAS. Since the FPTAS would extend the origi-nal graph and the scale of the extended graph increases expo-nentially with the number of constraints, FPTAS needs morespace to store the extended graph. Leveraging algebraicalform of graph extending, SCA does not need much storagespace for the extended graph. Hence, SCA is more efficientthan FPTAS to address the QSC problem.

Next, we show approximation property of service path pobtained by SCA.

Theorem 2. The path p obtained by SCA satisfieswkðpÞ � ð1þ rÞ � hopt �Wk, 2 � k � K, where r ¼ �

hoptis an

approximation ratio of SCA.

Proof. For the optimal path popt, wkðpoptÞ � hopt �Wk,2 � k � K, that is

Xe2popt wkðeÞ � hopt �Wk: (5)

Since wNk ðeÞ ¼ wkðeÞ

Wk� H�1�

j k, and wkðeÞ

Wk� H�1�

j k� wkðeÞ

Wk� H�1�

then Xe2popt

wkðeÞWk

�H � 1

�

� �� hopt �H � 1

�: (6)

According to the definition of dv½d2; � � � ; dK � as well as thedynamic programming process, we have

max2�k�K

wNk ðpÞ

� � � max2�k�K

fwNk ðpoptÞg: (7)

It implies

wNk ðpÞ � hopt �H � 1

�: (8)

Since wNk ðeÞ þ 1 ¼ wkðeÞ

Wk� H�1�

j kþ 1 � wkðeÞ

Wk� H�1� , then

Xe2p

wkðeÞWk

�H � 1

�� hopt �H � 1

�þX

e2p 1: (9)

Path p hasH � 1 hops, therefore

Xe2p wkðeÞ � ðhopt þ �Þ �Wk; (10)

i.e.,

wkðpÞ � ð1þ rÞ � h �Wk; (11)

where r ¼ �hopt

. This completes the proof. tu

Theorem 2 implies that the later ðK � 1Þ weights of p isable to asymptotically approximate the optimal withapproximation ratio r ¼ �

hoptwhile the first weight of p

achieves the smallest. This shows that the proposed SCAalgorithm provides a provably performance guarantee; thusis effective and efficient for solving the QSC problem.

4 PERFORMANCE EVALUATION

4.1 Evaluation Settings

We implemented a simulator to evaluate the performance ofthe proposed algorithm. The simulator is publicly availableat [27]. we compared the performance of SCA against thatof a variant of ADAPT [28] as well as FPTAS [26] throughnumerical experiments. To the best of our knowledge,ADAPT and FPTAS [26] are two currently best-known algo-rithms for MCOP when K ¼ 2 and K > 2, respectively.Moreover, QSC maps to a special case of MCOP directlywhen the topology is pruned in advance to satisfy thenodes’ constraints. Therefore, ADAPT and FPTAS withminor modification can be applied for resolving QSC.

We are aware of some available data sets of web serviceQoS parameters, for example QWS (http://www.uoguelph.ca/ qmahmoud/qws/) and WS-Dream (http://www.wsdream.net/dataset.html), which could be used for


http://www.uoguelph.ca/

http://www.uoguelph.ca/

http://www.wsdream.net/dataset.html

http://www.wsdream.net/dataset.html

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performance evaluation of service composition schemes.However, these data sets currently contain QoS metricsmainly for computing type of services (web services fordata process and storage) and lack sufficient QoS data fornetworking services. Though adding networking QoS datainto the existing web service dataset may work for the simu-lation purpose, the simulation results generated by suchartificially synthesized dataset are not convincible as thesimulation input is controlled. Therefore, in this paper weconduct simulation experiments using randomly generatednetwork/cloud services and QoS parameters in order toevaluate performance of the proposed algorithm for servicecomposition across network and cloud domains.

In the first set of evaluations, we generated a set of randomservice graphs with node numbers ranged from 100 to 1,000.Each link in the generated networks has two QoS parametersand they are uniformly distributed. As for the second set ofevaluations, we generated three networks with 60, 80, 100nodes respectively since a larger network scale will be over-flowed in FPTAS. Each link in these three networks has threeQoS parameters and they are also uniformly distributed.Note that the uniform distribution used here is only for illus-tration, other distributions also work in a similar way. Weassume that the service components are spread in five servicecategories, i.e.,H ¼ 5, whichwe believe is reasonable for typ-ical cloud service scenarios. In order to evaluate the perfor-mance under different parameter configurations, we set� 2 0:1; 0:3½ � for both SCA and ADAPT. Also, we assumethat the requests are always feasible; that is, W1 ¼ 50, W2 22:5 10�5; 5 10�5½ �. The reported results were obtained byrunning both algorithms 100 times independently, and all ofthe experiments were conducted on IBM P4 2.6 GHz with 2Gmemory space and Linux system.

4.2 Performance Metric

The performance metrics that we use in this section for eval-uating algorithms are defined as follows.

Definition 2. Average Execution Time (AET ). AET is the aver-age execution time of an algorithm calculated from 100 inde-pendent runs. This metric is used for evaluating the time costperformance of an algorithm, i.e., the time that a user has towait for getting a service composition result.

Definition 3. Path Weights Distance (PWD). For a path preturned by an algorithm,

PWDðpÞ ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXKk¼1

1� wkðpÞWk

2

vuut :

PWD denotes the distance between the weights of a found ser-vice path and the QoS constraints specified in the servicerequest. This metric reflects the level of QoS guarantee that canbe achieved by a service composition algorithm to end users.

4.3 Evaluation Results

Our experimental results quantify performance of evaluatedalgorithms in two ways. First, we test the impact of parame-ter � variation on algorithms’ performance by setting con-

stant W2 ¼ 3:0 10�5. Second, we examine the performanceof algorithms with constant � ¼ 0:1 for differentW2 settings.

Fig. 6 gives AET for SCA (shown in Fig. 6a) and ADAPT(shown in Fig. 6b) with different � settings. The graphs in thisfigure show that AET increases with the network size, whichis intuitive because both algorithms consume more time forlarger scale networks. In addition, we observed that the AETvalues of both algorithms increase when the parameter �decreases. This is because a smaller � gives a larger pathsearching space that slows down both algorithms.

Fig. 7 compares the AET of SCA against that of ADAPTwith different � values, where Fig. 7a, Fig. 7b, and Fig. 7cplot the case when � ¼ 0:1, � ¼ 0:2, and � ¼ 0:3, respectively.The curves show that for a certain � value, SCA has smallerAET than ADAPT does, especially in large scale networks;that is, SCA runs much faster than ADAPT. This is becausethe search space of SCA relies on the number of hops inpath, which is very small and can be known in advance.ADAPT employs an approximation test procedure to gener-ate a set of upper and lower bounds for searching the opti-mal. The search space for ADAPT is determined by thedistance between the upper and lower bounds, which islarger compared to SCA. Therefore, SCA has better AETperformance than ADAPT.

The AETs (in millisecond) of SCA and ADAPT with dif-ferent � values for three network sizes, namely, 100, 500,1,000, are shown in Fig. 8. From this figure we can see that

Fig. 6. AET comparisons for SCA and ADAPT with different networksizes and � values.

Fig. 7. AET values of SCA and ADAPT for various network scales with different �.


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Fig. 9 shows the AET of SCA and ADAPT under variousW2 for three specific network sizes. The results show thatthe AET of ADAPT is impacted by not only � but also theconstraint. It can be seen from Fig. 9a that the AET ofADAPT becomes low when W2 is loose. This is due to thefact that a looser W2 makes the search space of ADAPTsmall; thus reducing its AET. On the other hand, we observethat AET of SCA stays in a relatively constant level, whichmatches our theoretical analysis result that indicates therunning time is not affected by any constraint.

One should be noticed that in the above AET compari-sons the paths found by SCA and ADAPT are the same foreach specific settings of parameters; therefore the PWD ofboth algorithms are identical. In other words, we comparethe AET under the condition that both of algorithms findthe same path. Next, we examine the PWD of our proposedalgorithm with different constraints and network sizes.

Fig. 10 presents PWD comparisons for SCA with differ-ent constraint values and network sizes. Fig. 10a gives thePWD results for different W2 for three network sizes(NS=100, 500, 1,000). Fig. 10b shows the PWD for differentnetwork scales. These two figures indicate that SCA wouldfind a path with possibly greater PWD for large scale net-works. Moreover, it can be seen that the PWD of SCAchanges little in the condition of loosing W2. The reason forthis is that SCA searches the path primarily based on thepath hops. If the path in the right hops that satisfies the con-straints, the SCA terminates immediately. Therefore, varia-tions inW2 have little impact on SCA performance.

Similar to the first set of evaluations, in the second evalu-ation we compare the performance between SCA andFPTAS in the case of three constraints on each generatedtopology. Tables 2 and 3 show the AET comparisons forSCA and FPTAS in three networks with different � and

Fig. 8. AETs of SCA and ADAPT for various � values in different network sizes.

Fig. 9. AETs of SCA and ADAPT for various constraint values in different network sizes.

Fig. 10. PWDs for SCAwith different constraint values and network sizes.

TABLE 2AET Comparisons for Three Networks with Different �

Nodes� ¼ 0:1 � ¼ 0:2

SCA FPTAS SCA FPTAS

60 38 1,216 17 91980 58 3,300 25 2,488100 82 6,727 49 5,295

TABLE 3AET Comparisons for Three Networks with DifferentW2

W2ð10�5Þ � ¼ 0:1 � ¼ 0:2

SCA FPTAS SCA FPTAS

2 82 6,727 40 5,2953 75 6,365 33 4,9624 74 6,032 33 4,643


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constraint. Likewise, the same insight can be obtained; thatis, for the same � and network size, SCA executes faster thanFPTAS while finding the same path. The loosing of con-straints and variation in � almost makes no significant con-tribution to the AET of SCA. In contrast, variation ofconstraint and � would affect the performance of FPTAS.Therefore, SCA outperforms FPTAS in terms of AET in thecondition of obtaining the same PWD.

5 RELATED WORK

Service composition has been extensively studied in thefields of web services and cloud computing. Various technol-ogies have been developed to achieve service compositionthat meets functional and/or performance requirements.Most of these technologies are based on either workflowmanagement or AI-planning approach. Surveys of recentresearch on web service composition can be found in [29]and [30]. Optimization of multiple QoS criteria in servicecomposition has been modeled as a multi-dimension multi-choice 0-1 knapsack problem and solved by integer program-ming [31] or by heuristic search methods [32]. Genetic algo-rithms offer another approach to addressing this problemwith the advantage of being able to handle nonlinear QoSconstraints [33]. In [34] cloud services are modeled as finitestate machines and a simple additive weighting technique isused to select an optimal service composition. More recently,Dastjerdi and Buyya presented an ontology-based approachto describing services and their QoS properties and devel-oped a technique to optimize service composition based onuser preferences [35]. In [36] the authors studied strategy-proof pricing of cloud service composition and proposed adynamic programming based approach for service selectionwith quasi-polynomial computational complexity. Moreresearch on QoS-aware web service composition can befound in the survey presented in [37].

The aforementioned research results focus on web/cloudservice composition instead of composition between net-work and cloud services. The modeling and optimizationapproaches proposed in this paper overcome this limit byemploying QoS metrics of both network and cloud servicesthrough NaaS paradigm in the composition algorithm; thusfinding a path that achieves optimal end-to-end QoS perfor-mance. Network-aware service composition is studied in[38], [39]. In [38] the authors first build a network model forestimating the network latency between arbitrary servicesand potential users, and then develop a selection algorithmleveraging this model to find service compositions that willresult in a low latency. Geolocation information is employedin [39] to predict the network delay between candidate serv-ices, which is then used in a proposed geolocation-awareservice selection algorithm for service composition. Theaforementioned work handles network performance andcloud service QoS constraints separately and only considersa single network QoS metric, i.e., the estimated networkdelay between candidates services, in composition of com-puting services. In contrast, we address the service compo-sition problem with a holistic vision that abstracts bothcomputing and networking resources as services. The pro-posed algorithm in this paper integrates multiple networkperformance metrics and cloud QoS constraints to achieve

optimal end-to-end performance for composite network-cloud service provisioning.

The problem of combined service composition and net-work routing is studied in [40] with a goal to find the opti-mal service composition in a network that leads to theminimum routing cost. A decision making system is devel-oped in the paper to solve this problem with AI-planningtechniques. This work is similar to our research in that ser-vice composition and network routing are combined intoone problem and solved with a unified mechanism. How-ever, only a single QoS metric (delay) for communicationsand service processing is considered in [40]. In our work, byformulating network-cloud service composition as a multi-constraint optimal path problem we develop an algorithmthat can handle multiple QoS constraints.

Some recent research efforts try to apply techniques forQoS routing, which is typically formulated as Multi-Con-straint Optimal Path problem, to tackle the service composi-tion problem. Up to date, much progress has been madetoward designing efficient MCOP algorithms for QoS rout-ing. Ergun et al. [28] give an algorithm that adopts anapproximation test procedure to generate a set of upper andlower bounds for searching the optimal. This algorithm isknown to be one of the fastest algorithms for Delay Con-strained Least Cost (DCLC). Xue et al. [41] define variationsof MCOP and present a set of approximation algorithms foreach problem. Other progress toward QoS routing algo-rithms can be found from the surveys given in [42], [43], [44].

The aforementionedMCOP techniques were initially pro-posed for network QoS routing. Further development ofthese algorithms to enable their applications in network-cloud service composition forms an interesting researchtopic. The Fully Polynomial Time Approximation Scheme(FPTAS) proposed in [26] has been proved to be currentlyone of the fastest algorithms for MCOP. However, FPTAScannot be used directly in the network-cloud service compo-sition context because the vertex in the service graph hasQoS metrics and topology of service graph is usually aDirected Acyclic Graph (DAG). In our previous work [45]we proposed an approximation algorithm to tackle the prob-lem of QoS-aware service composition by following thedesign principle of FPTAS. However, the model proposed in[45] only considers QoS metrics on the edges in a servicegraph, which limits the flexibility of the model for reflectingdifferent types of services in network and cloud domains. Inthis paper, we model a service graph with QoS metrics forboth nodes and edges entering the nodes, which can be usedto respectively represent the service capacities of cloud com-puting services andQoS parameters of networking services.

6 CONCLUSIONS

QoS-aware composition across network and cloud servicesis a key technology for realizing converged network-cloudservice provisioning. In this paper, we have addressed thischallenging and important topic by formulating QoS-awarenetwork-cloud service composition as a variant of Multi-Constrained Optimal Path problem and developed anapproximation algorithm, termed SCA, for solving the prob-lem. We have given theoretical analysis on properties ofSCA and also conducted thorough numerical experiments


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for performance evaluation. Both analytical and experimen-tal results show that SCA is effective for meeting multipleQoS constraints and efficient to achieve much shorterresponse time for network-cloud service composition.

As SCA is designed for sequential task, we plan to extendit to deal with more general tasks such as parallel ones inthe future work.

ACKNOWLEDGMENTS

This work is supported by NSFC under grant 61309031,Postdoctoral Science Foundation of China under grant2014M551740, NSF of Chongqing under grantcstc2013jcyjA40026, S&T Research Program of ChongqingMunicipal Education Commission under grant KJ130523,and CQUPT Research Fund for Young Scholars under grantA2012-79. This work is also partially supported by StrategicInternational Collaborative Research Program (SICORP)Japanese (JST) - US (NSF) Joint Research ”Big Data andDisaster Research (BDD).

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Jun Huang (M’12) obtained PhD degree in theInstitute of Network Technology, Beijing Univer-sity of Posts and Telecommunications, China, in2012. He was a visiting researcher at GlobalInformation and Telecommunication Institute,Waseda University, Tokyo, from 2010 to 2011,and a postdoctoral research fellow at Electricaland Computer Engineering, South Dakota Schoolof Mines and Technology, USA, from 2013 to2014. He was an assistant professor and anassociate professor at School of Communica-

tions and Information Engineering, Chongqing University of Posts andTelecommunications, where now he is a full professor. His currentresearch interests include Network Optimization and Control, Internet-of-Things, Cloud Computing, and Device-to-Device communications. Hehas published 70+ refereed papers, several of them are published byprestigious journals and conferences including IEEE TCC, TVT, TBC,TETC, IoTJ, SCC, IWQoS, ACM SAC etc. Dr. Huang is the recipient ofBest Paper Runner-up of ACM SAC 2014 and the Best Paper Award ofAsiaFI 2011. He has served as a vice Program Chair of ACM RACS2014, 2015, Co-chair of ACM SAC Software Platform 2015, 2016, Co-chair of EAI MobiMedia 2015 Next Generation Communications andNetworking Technology Track, and technical program committee mem-ber for numerous conferences such as IEEE GLOBECOM, ICC, ICCCN,CSS, FiCloud.

Qiang Duan received the BS degree in electricaland computer engineering and the MS degree intelecommunications and electronic systems. Hereceived the PhD degree in electrical engineeringfrom the University of Mississippi. He is an asso-ciate professor at the Pennsylvania State Univer-sity Abington College. His general researchinterest is about data communications and com-puter networking. His currently active researchareas include future internet architecture, net-work virtualization, network-as-a-service, soft-

ware defined network, and cloud computing. He has published over 70journal articles and conference papers and authored five book chapters.He is on the editorial boards for more than 10 international research jour-nals and has served on the technical program committees for numerousinternational conferences including GLOBECOM, ICC, ICCCN, AINA,WCNC, etc. He is a member of the IEEE.

Song Guo (M’02-SM’11) received the PhDdegree in computer science from the Universityof Ottawa. He is currently a full professor at theSchool of Computer Science and Engineering,University of Aizu, Japan. His research interestsare mainly in the areas of wireless communica-tion and mobile computing, cloud computing, bigdata, and cyber-physical systems. He has pub-lished more than 250 papers in refereed journalsand conferences in these areas and receivedthree IEEE/ACM best paper awards. He currently

serves as an associate editor of the IEEE Transactions on Parallel andDistributed Systems, IEEE Transactions on Emerging Topics in Comput-ing, and many other major journals. He has also been in organizing andtechnical committees of numerous international conferences. He is asenior member of the IEEE and the ACM.

Yuhong Yan has been an associate professor inthe Department of Computer Science and Soft-ware Engineering, Concordia University, Mon-treal, Canada, since June 2008. Before joiningthe Concordia, she had been a researcher officerin the Institute for Information Technology (IIT) inthe Canadian National Research Council’s (NRC)since February 2003. Her current research is inservice computing. Service computing bringsflexibility, interoperability, and composeability tosoftware systems. Service computing combining

with mobile computing and cloud computing is going to pave the newlandscape of software engineering, pervasive computing and high per-formance computing. She is the author of over 40 articles and papers.She is one of the organizers of the IEEE ICWS and SCC in recent years.Her research is funded by the NSERC Discovery Grant, NSERC EngageGrant, MITACS, Carnari, and multiple industrial companies. She is amember of the IEEE.

Shui Yu (M’05-SM’12) received the BEng andMEng degrees from the University of ElectronicScience and Technology of China, Chengdu, P.R. China, in 1993 and 1999, respectively, and thePhD degree from the Deakin University, Victoria,Australia, in 2004. He is currently a senior lec-turer with the School of Information Technology,Deakin University, Victoria, Australia. He haspublished nearly 100 peer review papers, includ-ing top journals and top conferences, such asIEEE TPDS, IEEE TIFS, IEEE TFS, IEEE TMC,

and IEEE INFOCOM. His research interests include networking theory,network security, and mathematical modeling. He actively servers hisresearch communities in various roles, which include the editorial boardsof IEEE Transactions on Parallel and Distributed Systems, IEEE Com-munications Surveys and Tutorials, and IEEE Access, IEEE INFOCOMTPC members 2012-2015, symposium co-chairs of IEEE ICC 2014,IEEE ICNC 2013-2015, and many different roles of international confer-ence organizing committees. He is a senior member of the IEEE, and amember of the AAAS.

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