Comprehensively context-aware approach to guaranteeing multimedia conferencing services

China Communications • September 2013 53

SERVICES COMPUTING

Comprehensively Context-Aware Approach to Guaranteeing Multimedia Conferencing Services WU Jiyan, TIAN Yue, CHENG Bo, SHANG Yanlei

State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China

Abstract: Service-Oriented Communication (SOC) is a key research issue to enable media communications using the Service-Oriented Architecture (SOA). Motivated by the neces-sity to guarantee the service quality of our web- based multimedia conferencing system, we pr-esent a Comprehensively Context-Aware (CoCA) approach in this paper. One major problem in the existing end-to-end Quality of Service (QoS) management solutions is that they analyse and exploit the relationships between the QoS me-trics and corresponding contexts in an isolated manner. In this paper, we propose a novel app-roach to leveraging such relationships in a comprehensive manner based on Bayesian net-works and the fuzzy set theory. This approach includes three phases: 1) information feedback and training, 2) QoS-to-context mapping, and 3) optimal context adaption. We implement the proposed CoCA in the real multimedia con-ferencing system and compare its performance with the existing bandwidth aware and play-back buffer aware schemes. Experimental re-sults show that the proposed CoCA outper-forms the competing approaches in improving the average video Peak Signal-to-Noise Ratio (PSNR). It also exhibits good performance in preventing the playback buffer starvation.

Key words: SOC; multimedia conferencing; context-aware

I. INTRODUCTION

Web service is an emerging trend in the indus-try today to provide distributed Internet info-

rmation services over IP-based networks [1-2]. It relies on the SOAP messages and Web Ser-vice Description Language (WSDL) to control the various services for complex transactions. Service Oriented Communications (SOC) [3-4] is a key research issue to enable media com-munications using the Service Oriented Ar-chitecture (SOA) and thereby package com-munications capabilities as services. The idea of using Web services to support multimedia communications is attractive as it can be eas-ily integrated with other end-to-end SOA solu-tions and imposes significant impact on the open service market. Recent advances in the Web services technology have made it practi-cal to enable web-based multimedia commu-nications [5].

Multimedia conferencing serves as the ba-sis of many “killer” applications, e.g., audio/ video conferencing, multi-player online games and distance learning [6]. Motivated by pro-viding the users with ubiquitous multimedia conferencing services, we have designed and developed a SOA based multimedia confer-encing system [7], which has already been deployed and used by both the campus and Internet users. However, as the current SOA does not support multimedia services well [2, 5], our work is greatly challenged with the user per-ceived service quality (e.g., the video play-back delay and the subjective quality in terms of Peak Signal-to-Noise Ratio (PSNR)). Acc-ording to the related works [8-9] and our ex-periences, the challenging problems for the end-to-end QoS guarantee in multimedia con-

Received: 2013-04-03 Revised: 2013-07-16 Editor: ZHANG Liangjie

54 China Communications • September 2013

ferencing can be summarised in the following: 1) It is difficult to trace and identify the

causes of service anomaly as the causal rela-tionships between Quality of Service (QoS) metrics and their corresponding contexts1 rep-resent time-varying characteristics.

2) The number of tunable contexts is very limited at the Internet Service Provider (ISP) side, as many parameters are under the control of telecommunication operators and hardware manufacturers.

3) It is a challenging task to carry out con-text adaption strategies due to the heterogene-ity of conferencing participants’ networks en-vironments, hardware, QoS requirements, etc.

The existing end-to-end QoS management solutions can be generally divided into two bra-nches: active probing and passive monitoring [9]. Active probing based systems (e.g., Key-note [10], Gomez [11]) place monitoring ag-ents at different locations to detect end-to- end performance. In the passive monitoring based systems (e.g., the CEM in Ref. [12]), each end user detects the end-to-end service quality issues individually. The active probing syste-ms are limited in monitoring coverage and also place additional overhead on the network. On the other hand, the passive monitoring ones require end-users to install monitoring soft-ware. Furthermore, a major problem with both the active and passive schemes is that they either focus on the network level (e.g., avail-able bandwidth, round trip time or packet loss rate) or application level (e.g., video PSNR or playback delay) metrics. Then, the tunable con-texts are adapted accordingly. This isolated working manner can lead to many problems, e.g., the bandwidth-aware strategies aim at max-imising the throughput of the applications while the upper layer application can not di-rectly benefit from that as it is a typical Con-stant Bit Rate (CBR) one. In this paper, we propose to analyse and exploit the relation-ships between the target QoS metrics and cor-responding contexts in a comprehensive way, i.e., taking the concerning QoS metrics and available contexts into account so as to find out the optimal adaption strategies. The main

motivation for the proposed approach (Com-prehensively Context-Aware (CoCA)) stems from the practically observed QoS metrics and contexts, which we will discuss in detail in Section II. Specifically, this research makes the following contributions:

1) We design and develop a SOA based multimedia conferencing system. Based on the implemented conferencing system, we conduct measurements of the QoS metrics and contexts.

2) Through the collected measurements, we study and identify the causal relationships bet-ween the QoS metrics and contexts. A CoCA approach based on Bayesian networks is pro-posed to quantify and leverage such relation-ships and it includes three key phases: i) inf-ormation feedback and training, ii) QoS to con-text mapping, and iii) optimal context adaption.

3) We evaluate the performance of the pro-posed CoCA in our real multimedia confer-encing system. Experimental results show that: i) CoCA improves the video PSNR by up to 3.13 and 4.16 dB compared to the bandwidth aware and playback buffer aware schemes respectively, and ii) CoCA exhibits good per-formance in preventing the playback buffer starvation.

The remainder of this paper is organised as follows. In Section II, we discuss the motiva-tion for the proposed CoCA. We describe the proposed solution in detail in Section III. Sec-tion IV introduces the prototype implementa-tion issues and the prototype evaluation for the proposed CoCA is provided in Section V. In Section VI, we briefly discuss some related work on service oriented communications and end-to-end QoS management solutions. The conclusion remarks and future work are given in Section VI.

II. MOTIVATION

In this section, we discuss the motivation for the proposed approach from two aspects: 1) the practically observed values of the QoS metrics and contexts, and 2) the causal relationships existed between them after the training and studying process.

A QoS guarantee stra-tegy based on Bayes-ian networks and fuzzy set theory is proposed to optimise the service quality of web-based multimedia conferenc-ing in the service orie-nted architecture. The proposed scheme aims at analysing and exp-loiting the causal rela-tionships between QoS metrics and the cont-exts in a comprehens-ive manner.

1By the term “contexts”, we mean the available sys-tem information of app-lication-level metrics (e.g., the user-perceived play-back delay and stored vi-deo in the playback buff-er) or network-level para-meters (e.g., available ba-ndwidth, packet loss rate or round trip time) obta-ined through specific mo-nitoring or sensing mech-anisms.


Fig.1 Profile of the practically observed values of video streaming rate versus: (a) average end-to-end delay (b) packet loss rate (c) average video PSNR and (d) playback buffer size 2.1 Practically observed QoS metrics and contexts

We depict the real-data measurements of the QoS metrics (video PSNR, playback delay) and contexts (video streaming rate, available bandwidth) from the real multimedia confer-encing system in Figure 1. The experimenta-tions last for 30 days and we collect the data from more than 100 users both from the cam-pus and the Internet. It can be observed from Figure 1 that the causal relationships between video streaming rate and other variables are ambiguous, as no variable changes regularly along with the video streaming rate.

2.2 Causal relationships

After studying on the continuous context sam-ples collected from the experimentations, we find that each context follows an approximate Gaussian distribution, which is a summation of Gaussian distributions. Due to the lack of space, we only present the video PSNR results in Figure 2 (a) to show that the contexts and QoS metrics in the experiments of this study approximately follow some Gaussian distribu-tion. The other is that some context/QoS met-ric was approximated or fitted by a sum of Ga-ussian distributions, instead of one Gaussian

Fig.2 The probability density and relationship: (a) the probability density of average video PSNR after Ga-ussian fitting, and (b) the causal relationship bet-ween them

distribution. This manner can reduce the fit-ting residuals as much as possible [8]. In this study, the best fitting is marked out if it has the minimum root mean square error, which is


Fig.3 Overall design and key components of the proposed CoCA

often consistent with the sum of square error. It should be noted that the probability den-

sity distributions in Figure 2 (a) are inconsis-tent with what are expected to be obtained, since the contexts/QoS metrics are rearranged according to the increment of video streaming rate in Figure 1. Figure 2 (b) presents the map-ping of the values after the Gaussian fitting process and the relationship between them can be observed. However, these causal relation-ships represent time-varying characteristics as the video sessions advance. In the next section, we will describe how to determine and exploit the relationships in the proposed solution.

III. PROPOSED SOLUTION

In this section, we outline the overall design of the proposed solution and describe the func-tionality of its major components in detail. The system design is depicted in Figure 3 and in-cludes working components in both the source and the client side. The information monitor in the client side is responsible for providing the feedback information of all QoS metrics and contexts. The QoS metrics and contexts (e.g., available bandwidth and playback buffer size) can be obtained through specific communica-tion protocols between server and clients. Es-timating network status based on end-to-end measurements has been an active research for

many years and numerous algorithms have been proposed to achieve the goal. In this paper, the pathChirp [13] algorithm is employed to esti-mate the end-to-end available bandwidth with high accuracy and efficiency. Next, we will describe the three key phases of the proposed CoCA in detail.

3.1 Information feedback and training

In order to exploit the causal relationships, the feedback QoS metrics and contexts must be properly trained. Fuzzy logic is a multi-valued logic that maps imprecise terms into concrete values. A fuzzy controller is composed of the inference system that includes a rule set, the input membership functions and the output variable. The input values go through a process of fuzzification, where they are converted in terms of the membership functions of the fuz-zy sets. These sets are defined over the range of the fuzzy input values, and linguistically describe the variable’s universe of variation. However, fuzzy set theory requires experts to subjectively determine fuzzy sets and their membership functions for a continuous conc-ept. In other words, these fuzzy sets and mem-bership functions may vary from expert to ex-pert. Hence, discretization results may be incon-sistent from expert to expert from time to time. To eliminate subjectivity in determining fuzzy


sets and membership functions and to assure consistency of discretization result, we prop-ose an adaptive method to discretise a contin-uous context in this study. Formally, the pro-bability density f(x) of an approximate Gaus-sian distribution can be defined as

( )2

1exp

ni

ii i

x bf x ac=

⎛ ⎞⎛ ⎞−⎜ ⎟= ⋅ −⎜ ⎟⎜ ⎟⎝ ⎠⎝ ⎠∑

where n is the total number of sample values, is the coefficient of the i-th term of the prob-ability density; bi and ci are expectation value and standard deviation of the i-th term, resp-ectively. In this study, a continuous context c is thought to have n discrete values if the probability density of its approximate Gaus-sian distribution has n terms because each term shows an identical numerical characteris-tic, i.e., ai, bi and ci in the above formal defini-tion. That is, the values covered by a term are more close in semantic than those covered by other terms. Once training data, a set of sam-ples of a target context is collected. The QoS metrics/contexts training algorithm (see Algo-rithm 1) is referred to make them discrete.

Algorithm 1 QoS metrics and contexts training

Input: Sample values S of QoS metrics/contexts Output: Discrete value set DS of sample S Initialize: D ⇐∅ , M ⇐∅ , DS S⇐ 1: { , } Dis- Mem( )D M S⇐ ; 2: for all sample s S∈ do 3: ( ) max{ ( ) | , }D D Df s f s d D f M⇐ ∈ ∈ ; 4: s D⇔ ; 5: s D⇐ ; 6: end for 7: return DS Procedure Dis- Mem( )S 1: K- DENSITY( )PD S⇐ ; 2: { , RMSE} GUASSIAN- FITTING( )GF PD⇐ ; 3: arg min{RMSE |RMSE RMSE,1 |RMSE|}i i IG GF ∈⇐ ≤ ≤

4: max 0a ⇐ ; 5: temp 0⇐ ; 6: for each t G∈ do 7: temp temp 1⇐ + ; 8: [temp]M t⇐ ; 9: [temp] cntD ⇐ ; 10: max temp maxmax{ , }a a a⇐ ;

11: end for 12: for each m M∈ do

13: max

mma

⇐ ;

14: return {D, M};

3.2 QoS-to-context mapping

After the available contexts have been trained to be in the right form, the QoS-to-context map-ping is performed to find out causal relation-ships between a QoS metric and these contexts in a real-time manner. A QoS metric is thought to be caused by a context if it changes along with the context, i.e., a causal relationship exists in the QoS metric and the corresponding context. The Bayesian network [14] is employed in this paper for studying and modelling these causal relationships.

In the proposed solution, the QoS metric to be studied is considered as a child node, and its contexts are regarded as ancestor nodes. Ba-yesian network structure learning algorithms are employed here to construct a directed acy-clic graph, which qualitatively models the cau-sal relationships between the QoS metrics and the corresponding contexts. Formally, a Baye-sian network is a directed acyclic graph with a conditional probability table associated with each discrete node. A conditional probability distribution is associated with a continuous variable. Node va is a parent of node vb, which in turn is a child of node va, if a directed arc from va to vb exists. G=(V, E) denotes the net-work topology: V denotes the set of nodes and E represents the set of directed arcs. By this catenation connection, the conditional probab-ility table space will be saved exponentially since only parents rather than all ancestors are considered to compute conditional probabili-ties. The conditional probabilities of vi given its ancestors have been encoded in the probability tables of its parents

ivp . For instance, we assume the mean number of discrete values of a node vi is er, the mean number of parents is eu and the mean number of ancestors is ea. Thus, Bayesian network only requires vr slots instead of er to store the conditional probabil-ity table for node vi, i.e., the space is saved about 1 u ar −− , which approximates one expo-nentially. In order to study the contexts for a target QoS metric using Bayesian network, the


Fig.4 The resulting Bayesian network structure for the QoS-to-context mapping

QoS metric to be evaluated is considered as a child node, and its corresponding contexts are considered as ancestor nodes. BN structure learning algorithms are referred to construct a direct acyclic graph, which represents the ca-usal relationships between the QoS metric and corresponding contexts. The K2 algorithm [15] is applied to learn network structure in this paper. Parents of a node v can only be chosen from the nodes before v in the order. The fol-lowing three paragraphs convey a node-ord-ering method.

The following steps are taken to leverage the causal relationships between a QoS metric and its contexts. The parent contexts are the ca-uses of a QoS metric. More specifically, par-ent nodes are direct causes of a child node wh-ile other ancestor nodes are the indirect causes. Therefore, the QoS-to-context mapping phase analyses the relationships in a systematic man-ner. The resulting Bayesian network structure for the considered QoS metrics and contexts is presented in Figure 4.

Algorithm 2 QoS-to-context mapping

Step 1. Get the node order for the K2 algorithm by the node-ordering method in Section 3.2. Step 2. Form the Bayesian network structure for the QoS metrics and contexts using the K2 algorithm. Step 3. For the QoS-to-context mapping, find the parent nodes in the Bayesian network for the QoS metric node by the directed acyclic traversal algo-rithm.

3.3 Optimal context adaption

Bayesian network parameter training is used to learn the conditional probability tables from training data after the Bayesian network stru-cture is constructed by the QoS-to-context map-ping. In this study, we use Bayesian parameter updated from complete data, i.e., the samples of contexts and QoS metrics. A Bayesian net-work is completely defined once its structure and parameters are both determined. Then, given the observed data, the Bayesian network inference can be applied to compute the mar-gin on the specified query nodes. The margin on a query node H given observed data E can be viewed as the conditional probability ( | ),P H E which is a quantitative metric. Therefore, we use the marginal on a QoS metric node given the observed contexts to guarantee the QoS metric.

The optimal context adaption aims at carry-ing out with a context value that guarantees the QoS metric to the target value with prob-ability p. When a user picks up a specific value of the QoS metric, the causal contexts of the QoS metric are tuned to appropriate values with regard to the specific QoS metric value. The optimal context adaption steps are sum-marized in Algorithm 3.

Only parent contexts will be tuned and other contexts will be left untouched. It makes sense by the theory that every set of nodes in a Bayesian network is conditionally independent of QM when conditioned on the Markov blanket QM of the node QM, which consists of its parents, children and the other parents.

Algorithm 3 Optimal context adaption

Step 1. Find the parent contexts 1 2{ , , , }NC C C=for the QoS metric, in which N is the total number of contexts. The graph is the one learned in the QoS-to-context mapping phase. Step 2. Exclude the un-tunable contexts P from

and create a set of tunable parent contexts ' =

P− . Step 3. Compute the marginal on the query node, the QoS metric, given observed data, the tunable causal contexts. Step 4. For each discrete value qm of , pick the conditional probability 1 1 2 2( | , , ,P q C c C c= = =


)m mC c= , iC ∈ with the largest value p. Step 5. Adapt the contexts 1 2, , , mC C C to their cor-responding values c1, c2,…, cm with regard to QM=qm in Step 3. Then, the QoS metric can be guaranteed to be the value of qm with probability p.

IV. PROTOTYPE IMPLEMENTATION

In this section, we describe the architecture of our multimedia conferencing system and the prototype implementation of the prop-osed solution.

4.1 Service oriented architecture for multimedia conferencing

Our multimedia conferencing system includes some real-time communication services, e.g., the multi-party call control service and short message service. The system also contains some non-real-time Web services, e.g., the user au-thentication and charging services. Therefore, the system must possess the capability to exe-cute those hybrid services. We have developed the SOA based multimedia conferencing sys-tem and the system framework is presented in Figure 5. Here, the Enterprise Service Bus (ESB) is based on Servicemix [16], which is an open source project. The Telecom service engine kernel is based on another open source project Mobicents [17], which is based on the JAIN SLEE specification and can be easily con-nected to different application platforms or network elements via a resource adaptor mec-hanism. In the system design, the BPEL is used for automatically translating the multimedia con-ferencing processes and properties into a cor-responding formal model.

4.2 Implementation issues

The corresponding hardware and software of the real system is presented in Figure 6. The IBM X3800 8866 serves as the SIP server. The AINOL HD media server and the Radisys CMS9000 are alternate in use for the media process. The conferencing system includes the audio/video services, shared contents and in-stant messages as depicted in the user inter-face. Interested readers could refer to Ref. [7] for the detailed introductions of our system.

Fig.5 Prototype of the implemented multimedia conferencing system

Our implementation includes modifications at the media server and the client. The main com-ponents of the proposed CoCA are impleme-nted on the enterprise service bus. We signifi-cantly extend and modify the system (about 4 000 lines of JAVA code and 2 000 lines of C++ code) to realise the CoCA’s components. The function of video streaming rate adaption is realised by the JAVA interface provided by the media server manufacturer.


Table I Parameters of background traffic Parameter FTP CBR Pareto

Start time/second Rand [0, 100] Rand [0, 50] Rand [50, 150]

Duration/second Rand [0, 300] Rand [50, 200] Rand [200, 300]

Packet size/Bytes 1 500 500 1 000

Traffic rate/(Mb·s-1) N/A Rand [1, 1.5] Rand [0.5, 1]

Data size Rand [10, 1500] KB Rand [5, 50] MB N/A

Fig.6 Hardware and user interface of the real multimedia conferencing system

Fig.7 Emulation topology used in the experimentations

V. PROTOTYPE EVALUATION

In this section, we evaluate the efficacy of the proposed solution on our real multimedia con-ferencing system by comparing it with exist-ing QoS guarantee approaches. First, we des-cribe the evaluation methodology that includes the experimental setup, performance metrics and compared approaches. Then, we show and

discuss the evaluation results in detail.

5.1 Evaluation methodology

1) Experimental setup We adopt the Exata [18] as the network

emulator. Exata is the advanced edition of Qua-lNet [19] in which we can perform semi-ph-ysical emulations. In order to implement the realistic emulations of practical network envi-ronments, we set the number of nodes and their locations based on the network topology as depicted in Figure 7. The real computers are connected to the emulation server through the Exata 2.1 connection manager. The param-eters of the injected background traffic are lis-ted in Table I. The video streaming rate is ada-pted every 2.5 seconds in all the experiments.

2) Performance metrics We use the following metrics to evaluate

the performance of the proposed CoCA. i) PSNR is a standard metric of video qual-

ity and is a function of the mean square error between the original and the received video frames. If a video frame is dropped or past the deadline, it is considered to be lost and may be concealed by copying from the last received frame before it.

ii) Playback delay. The playback delay pro-cess corresponds to the time left to playback the video data at the tail of the playback buffer or, equivalently, the buffered video length plus the time left to start video playback.

3) Compared approaches i) Bandwidth aware approach [20-21]. The

Bandwidth Aware (BA) adaption approaches aim at maximising the video application’s thr-oughput by adapting the video streaming rate. In this work, we assume that the BA scheme adjusts the video encoding rate according to the average “loss-free” bandwidth of all the participants.

ii) Playback Buffer Aware (PBA) approach [22-23]. This scheme adjusts the video stream-ing rate based on the feedback info of play-back buffer size from the clients. Playback bu-ffer starvation occurs when a video frame is requested by the application layer for playback before being fully received. From a user QoS


perspective, playback buffer starvation is und-esirable because it results in video frame fre-ezes and re-buffering. Therefore, the constra-int for the video streaming rate is equal to pre-vent the buffer underflow.

4) Evaluation scenario In order to fully evaluate the performance

of the compared approaches, we conduct ex-periments with different numbers of participa-nts: 4, 8, 12 and 16. For the confidence results, we collected the experimental results from mu-ltiple runs and obtain the average values as the final results.

5.2 Evaluation results

First, we depict the instantaneous available bandwidth in Figure 8. It can be observed that the available bandwidth frequently fluctuates due to the injected background traffic. We also plot the effective video streaming rate in the same interval in Figure 9.

1) PSNR As depicted in Figure 10, CoCA achieves

higher average PSNR values and lower devia-tions than the BA and PBA schemes in all the evaluation scenarios. It can be observed that the superiority of CoCA over the competing schemes becomes more distinct as the number of participants increases. The results indicate the relationships between the QoS metrics and corresponding contexts become more complex in case of many online users. The average video quality degrades with the increase in the num-ber of users for all the approaches. Indeed, due to the capability limitations of the media ser-ver, the user-perceived video quality is a chal-lenging problem to be tackled in our future work. In general, CoCA improves the average video PSNR by up to 3.03 and 4.16 dB com-pared to the BA and PBA scheme. The video per frame PSNR is shown in Figure 11 in or-der to have a microscopic view.

2) Playback delay The playback delay is defined as the time

left to playback the video data at the tail of the playback buffer. In Figure 12, we plot the av-erage playback delay and PBA maintains the lowest latency of all the competing approaches.

Fig.8 Available bandwidth of the fourth destination user during the video session

Fig.9 Video streaming rate during the interval of [0, 250] second for the competing approaches

Fig.10 Average PSNR values for all the competing approaches in different experimental scenarios

Fig.11 Instantaneous PSNR value of all the compet-ing approaches for each video frame indexed from 1 to 300


Fig.12 Average playback delay of all the partici-pants in the experimental scenarios

Fig.13 Instantaneous value of playback delays during the interval of [0, 250] second

In order to have a microscopic view of the results, we show the playback delay process of [0, 250] seconds in Figure 13. It can be ob-served from Figure 13 that the PBA is able to regulate the playback buffer contents based on its size by adjusting the video streaming rate to maintain enough data to compensate for the time-varying networks status. Furthermore, it avoids the playback buffer starvation and re-buffering. The buffering behaviour is also correlated with the available bandwidth as depicted in Figure 8. Although the perform-ance of CoCA is generally inferior to that of PBA, it still exhibits good performance in pre-venting the playback buffer starvation.

VI. RELATED WORK

ZHANG et al. [24] proposed a SOAP-oriented

component based framework for multimedia Web service. In this work, a multimedia Web service is separated into a control flow and a data flow. The control flow is transmitted via ordinary Web services whereas the data flow is managed by the existing multimedia infra-structure. The study proposed a SOAP enhan-cement so that multimedia data can be alterna-tively transmitted using SOAP. The enhance-ment includes the use of “boxcarring” and bat-ching of messages. However, since the trans-fer of each part of a big chunk of multimedia data requires a separate Web service request, the transfer may be too costly and inefficient when streaming multimedia is transmitted using one separate request per packet. There is also extensive prior work on the end-to-end QoS management. Active approaches require the injection of probe packets into the network. The pioneering active approach [25] tracerou-ted between 37 participating sites are collected and analysed to characterize the end-to-end performance issues. The authors of Ref. [26] proposed to detect path outage among hosts using ping and to localise the observed path outage with traceroute. PlanetSeer [27] relied on active probes to diagnose the root cause of Internet path failures that are detected by pas-sively monitoring the end-users of a content distribution network service deployed on Pl-anetLab. Commercial systems, e.g., the Key-note [10] and Gomez [11] were also available to detect issues from the end-user’s perspec-tive by active probing. All these works employ active probing while “Argus” purely depends on passive monitoring. Although the DiffServ architecture [28] supports end-to-end QoS gua-rantee, only a limited number of static QoS cla-sses are provided. Obviously, it cannot meet the fine-grained QoS demands of diverse ser-vices. Moreover, end-to-end QoS guarantee by DiffServ may require the supports of underly-ing hardware, which will introduce overhead to those core devices and result in low effici-ency. In addition, fine-grained QoS quantita-tive guarantee is difficult to be implemented because these fine-grained service anomalies are hard to be traced among ISPs due to the


commercial security problem. We introduced the SOA into the real mul-

timedia conferencing system and proposed the CoCA to address the QoS guarantee issues. This feature distinguishes our work from previous researches.

ACKNOWLEDGEMENT

This research was supported by the National Basic Research Program of China (973 Pro-gram) under Grants No. 2011CB302506, No. 2011CB302704, No. 2012CB315802; the Na-tional Key Technologies Research and Devel-opment Program of China “Research on the Mobile Community Cultural Service Aggrega-tion Supporting Technology” under Grant No. 2012BAH94F02; the Novel Mobile Service Control Network Architecture and Key Tech-nologies under Grant No. 2010ZX03004- 001-01; the National High Technical Research and Development Program of China (863 Pro-gram) under Grant No. 2013AA102301; the National Natural Science Foundation of China under Grants No. 61003067, No. 61171102, No. 61001118, No. 61132001; Program for New Century Excellent Talents in University under Grant No. NCET-11-0592; the Project of New Generation Broadband Wireless Network un-der Grant No. 2011ZX03002-002-01; and the Beijing Nova Program under Grant No. 2008B50.

References [1] TONNIES S, KOHNCKE B, HENING P, et al. A

Service Oriented Architecture for Personalized Rich Media Delivery[C]// Proceedings of IEEE International Conference on Services Com-puting 2009 (SCC’09): September 21-25, 2009. Bangalore, India. IEEE Press, 2009: 340-347.

[2] HEINZL S, SEILER D, JUHNKE E, et al. A Scal-able Service-Oriented Architecture for Mul-timedia Analysis, Synthesis and Consump-tion[J]. International Journal of Web and Grid Services, 2009, 5(3): 219-260.

[3] CHOU W, LI Li, LIU Feng, et al. Web Services for Communication over IP[J]. IEEE Commu-nications Magazine, 2008, 46(3): 136-143.

[4] CHOU W, LI Li, LIU Feng, et al. Web Services Methods for Communication over IP[C]// Pro-ceedings of IEEE International Conference on Web Services (ICWC 2008): September 23-26,

2008. Beijing, China. IEEE Press, 2008: 372-379. [5] LAM G, ROSSITER D. A Web Service Frame-

work Supporting Multimedia Streaming[J]. IEEE Transactions on Services Computing, 2013.

[6] FU Chunyan, KHENDEK F, GLITHO R. Signaling for Multimedia Conferencing in 4G: The Case of Integrated 3G/MANETs[J]. IEEE Communi-cations Magazine, 2006, 44(8): 90-99.

[7] User Manual for Multimedia Conferencing System[EB/OL]. http://211.68.70.180:8888/Med-iaConf/.

[8] LIN Xiangtao, CHENG Bo, CHEN Junliang. Con-text-Aware End-to-End QoS Qualitative Guarantee and Quantitative Guarantee Based on Bayesian Networks[J]. Computer Commu-nications, 2010, 33: 2132-2144.

[9] YAN He, FLAVEL A, GE Zihui, et al. Argus: End-to-End Service Anomaly Detection and Localization From an ISP’s Point of View[C]// Proceedings of IEEE International Conference on Computer Communications (INFOCOM 2012): March 25-30, 2012. Orlando, Florida, USA. IEEE Press, 2012: 2756-2760.

[10] Keynote System.[EB/OL]. http://www.keynote. com/.

[11] Gomez System.[EB/OL]. http://www.gomez. com/. [12] CHOFFNES D R, BUSTAMANTE F E, GE Zihui.

Crowdsourcing Service-Level Network Event Monitoring[J]. ACM SIGCOMM Computer Communication Review, 2010, 40(4): 387-398.

[13] RIBEIRO V, RIEDI R, BARANIUK R et al. path-Chirp: Efficient Available Bandwidth Estima-tion for Network Paths[C]// Proceedings of Pa-ssive and Active Measurement Workshop: April, 2003. San Diego, California, USA. SLAC Press, 2003:1-11.

[14] PEARL J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference[M]. Morgan Kaufmann Press, 1988.

[15] COOPER G. A Bayesian Method for the Induc-tion of Probabilistic Networks from Data[M]. Springer Press, 1992.

[16] ServiceMix Project[EB/OL]. http://servicemix.org. [17] Mobicents Project[EB/OL]. https://mobicents.

dev.java.net. [18] Exata User Manual[EB/OL]. http://www.scalable-

networks/exata. [19] QualNet User Manual[EB/OL]. http://www.sca-

lable-networks/qualnet. [20] XIANG Siyuan, CAI Lin, PAN Jianping. Adap-

tive Scalable Video Streaming in Wireless Net-works[C]// Proceedings of the 3rd Multimedia Systems Conference (MMSys’12): February 22- 24, 2012. Chapel Hill, North Carolina, USA. ACM Press, 2012, 167-172.

[21] MILLER K, QUACCHIO E, GENNARI G, et al.


Adaptation Algorithm for Adaptive Streaming over HTTP[C]// Proceedings of IEEE Interna-tional Packet Video Workshop: March 10-11, 2012. Munich, Germany. IEEE Press, 2012, 173- 178.

[22] CICCO D, MASCOLO S, PALMISANO V. Feed-back Control for Adaptive Live Video Stream-ing[C]// Proceedings of ACM Multimedia Sy-stems Conference (MMSys’11): February 23-25, 2011. San Jose, California, USA. ACM Press, 2011: 145-156.

[23] MOK R K P, LUO Xiapu, CHAN E W W, et al. QDASH: A QoE-Aware DASH System[C]// Pro-ceedings of ACM Multimedia Systems Con-ference (MMSys’12): February 22-24, 2012. Chapel Hill, North Carolina, USA. ACM Press, 2012, 11-22.

[24] ZHANG Jia, ZHANG L J, QUEK F K H, et al. A Service-Oriented Multimedia Componentiza-tion Model[J]. International Journal of Web Services Research, 2005, 2(1): 54-76.

[25] PAXSON V. End-to-End Routing Behavior in the Internet[J]. ACM SIGCOMM Computer Communication Review, 2006, 36(5): 41-56.

[26] FEAMSTER N, ANDERSEN D G, BALAKRISH-NAN H, et al. Measuring the Effects of Internet Path Faults on Reactive Routing[C]// Proc-eedings of ACM SIGMETRICS: June 9-14, 2003. San Diego, California, USA. ACM Press, 2003, 126-137.

[27] ZHANG Ming, ZHANG Chi, PAI V, et al. Plan-etSeer: Internet Path Failure Monitoring and Characterization in Wide-Area Services[C]// Proceedings of USENIX Operation Systems De-sign and Implementation: December 5, 2004. San Francisco, California, USA. USENIX Press, 2004, 1-16.

[28] IETF RFC 2475. An Architecture for Differenti-ated Services[S], 1998.

Biographies WU Jiyan, is currently a Ph.D. candidate in the Bei-jing University of Posts and Telecommunications, China. He obtained his M.S. degree from China Uni-versity of Mining and Technology, China in 2011. His research interests include multimedia conferencing services, context-aware computing technology and wireless video streaming. Email: [email protected] TIAN Yue, is currently working towards his B.E. de-gree in computer science in Beijing University of Po-sts and Telecommunications, China. His research int-erests are in the service oriented architecture, service selection and service composition. CHENG Bo, received his Ph.D. degree in computer science and engineering from University of Electronic Science and Technology of China, China in 2006. He has been working in Beijing University of Posts and Telecommunications (BUPT), China, since 2008. Cur-rently, he is an Associate Professor of the Research Institute of Networking Technology of BUPT, China. His current research interests include network ser-vices and intelligence, Internet of Things technology, communication software and distribute computing, services oriented computing, etc. SHANG Yanlei, received his Ph.D. degree in com-puter science and technology from Beijing University of Posts and Telecommunications, China in 2006. Then he worked in the Nokia Research Center as a postdoctoral research fellow. He is currently an Asso-ciate Professor in the State Key Laboratory of Net-working and Switching Technology of BUPT, China. His research interests include cloud computing, ser-vice computing, distributed system and virtualization technology.

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Comprehensively context-aware approach to guaranteeing multimedia conferencing services