A novel pricing approach to support QoS in 3G networks

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Computer Networks 52 (2008) 1433–1450 www.elsevier.com/locate/comnet

A novel pricing approach to support QoS in 3G networks Vitalis G. Ozianyi *, Neco Ventura, Eugene Golovins Department of Electrical Engineering, University of Cape Town, Rondebosch, Western Cape 7700, South Africa Received 17 August 2006; received in revised form 28 November 2007; accepted 21 December 2007 Available online 1 February 2008 Responsible Editor: W. Wang

Abstract Pricing in 3G and other communication networks may control and manage the utilisation of network resources. The available network resources get strained with increased usage levels, which results in poor service to the users. Most users prefer receiving high quality services at affordable costs. This requires the provision of QoS guarantees for network services at a low cost. In a real business scenario, this relationship is hard to achieve; moreover revenue sources for network operators have been shifting from the provision of network access to provisioning of rich services, e.g. multimedia services. To attain a functional compromise, we propose a pricing scheme that relies on service profiles to manage resource utilisation in a DiffServ-enabled 3G network. The service profiles define the QoS achieved for accessing services through a common resource pool, in which resource sharing is used to maximise network resource utilisation, user satisfaction and profits for the network operators. In an NGN scenario users would select pricing profiles according to their budgets, and the network will map these profiles to a set of QoS options that may translate to the choice of an access network for service access. In this paper, we present the mathematical model of the proposed pricing scheme, the proposed design of an evaluation framework, QoS performance results, and a service provisioning scenario illustrating the applicability of the proposed pricing scheme. Ó 2008 Elsevier B.V. All rights reserved. Keywords: Pricing; Quality of service (QoS); Class of service (CoS); Profile

1. Introduction The developers of mobile network management systems often adopt the features of existing fixed communication networks. Whereas the adoption works perfectly for some services, it does not map *

Corresponding author. Tel./fax: +27 216505296. E-mail addresses: [email protected] (V.G. Ozianyi), [email protected] (N. Ventura), [email protected] (E. Golovins).

well when it comes to pricing and billing. The services offered on 1G and 2G networks used circuit switched technology, hence they hardly faced any QoS constraints. 2.5G and 3G networks are characterised by non-channelised environments, where resources are shared by traffic from several applications and users. The characteristics of different applications leads to a situation where different types and levels of network performance guarantees are needed. As an example, real-time services (e.g. VoIP) require low end-to-end delay guarantees,

1389-1286/$ - see front matter Ó 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.comnet.2007.12.011

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not exceeding 150 ms, which are hard to sustain in a best effort environment. Effective provisioning of network performance guarantees must be accompanied by new pricing and charging mechanisms. Although the adequacy of existing charging schemes used for 2G networks may not be challenged, it is clear that the QoS aspect associated with 3G and future generation networks renders the legacy pricing schemes inadequate. The tariff rates used for charging communication services by network operators determine the competitive advantage they get over other operators in their region. Network operators set tariffs for different services depending on outcomes of market surveys [1], and by considering the revenue required to offset the capital investment and operational costs. As compared to 2G networks, the resources available in 3G networks (e.g. UMTS) are considered sufficient for the simultaneous delivery of QoS sensitive services to several users [3]. In contrast with basic Internet services, extra bandwidth, minimum delay and low jitter are required for the transmission of multimedia traffic. In GPRS and UMTS these QoS parameters are normally specified during the packet data protocol (PDP) context setup. The network would create the PDP context if the values of the requested QoS parameters fall within the authorised values, or the requested QoS parameters would be downgraded by the network to suit the authorised values [4]. Downgrading and enforcement of QoS values is done by the GGSN in the core network, and optionally by the IP bearer service manager in the UE. Session-based (e.g. SIP) applications would be signalled to adapt changes in the PS bearer service, while an agent is required in UE or the network to perform adaptation for legacy applications. The tariff rate used for charging the PDP session will generally depend on the effective QoS values. The convergence of the Internet and the cellular world (3G) is seeing mobile operators and other telecommunication operators introducing new services, e.g. VoIP, on their networks. VoIP, IPTV and other multimedia services have gained great popularity amongst users. Conventionally these services would be accessed using fixed broadband Internet access technologies like ADSL, LAN and WLAN for fixed-mobile access. These access schemes do not support the kind of mobility users would find on mobile networks like 3G. However, the comparative low cost of Internet access using these technologies has resulted in tremendous

growth in the use of Internet telephony as an alternative to traditional fixed and mobile telephony. Examples of this include the popularity and growth of subscriptions on Skype. New paradigms like IPTV are causing the use of the Internet for multimedia communications to rise to new levels. In order to retain the market share of revenue from telephony services and also to exploit the revenue potential of services like IPTV 3G network operators are integrating the IP Multimedia Subsystem (IMS) as a service delivery platform into their networks. Considering the amount of bandwidth and other resources that are specified in the design of 3G and Next Generation Networks, it is possible for a user to request and be assigned data rates that would be a hundred times greater than average data rates achievable in 2G networks. The challenging question is: should the user be charged a hundred times more for this PDP context? When compared to other broadband Internet access schemes, e.g., WLAN, advanced QoS resource allocation and admission control on a 3G network makes it better suited for IMS services like real-time voice and video. On the other hand, the main challenge to the growth of IMS on 3G is the high charges associated with Internet access on 3G. Some 3G operators have lowered the cost for packet data access, e.g. for High Speed Downlink Packet Access (HSDPA). Charging for HSDPA often uses flat pricing, with a data volume cap. Flat pricing would deny operators a chance to explore revenue opportunities stemming from new IMS services [2]. Customer oriented tariff models are needed to exploit these opportunities. VoIP and video telephony are examples of services that would deny revenue if charged on flat rate. Achieving a balance between using flat pricing for basic packet data access and retaining traditional tariff structures for voice and video telephony can be met by employing a structured pricing scheme. The amount of resources available for packet data in mobile networks are not constant. They vary with propagation characteristics of the radio interface and the utilisation of radio channels by other services (e.g. packetized voice). On the other hand, the demand for resources is also dynamic and depends on the number of communication sessions that are being served. When the number of sessions increases, network congestion is bound to occur. Despite this, in 3G networks there are bound to be periods when the network will have surplus

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resources. In this paper, we discuss a proposal on the criteria that could be used to allocate these extra resources at a discounted tariff rate to network users. The result of network congestion is the degradation of QoS for the active sessions. Pricing has been used as a tool for controlling congestion in communication networks. A good illustration is the timeof-day (ToD) pricing where daytime tariff rates are higher than night time. This exploits some users’ sensitivity to prices whereby they prefer to use network services when the tariff rates are low. The argument presented in this paper aims at combining the discounting of abundant resources in communication networks and the use of pricing for network congestion control so as to improve the service and economic efficiency of the network. To achieve this, we introduce network service profiles that influence the QoS vs. cost relationship of the service delivered to the user. A platinum profile that guarantees bandwidth during times of congestion and a gold profile that ensures constant network prices are explored alongside a silver profile. This architecture is designed for IP services in 3G networks. From a consumer perspective, the platinum profile assures delivery of services at the requested bandwidth. The advertised service prices can be lowered during periods of little or no network congestion. The gold profile, on the other hand, provides a predictable per unit constant service charge, but with the possibility of encountering degradable bandwidth. No bandwidth guarantee is offered by the silver profile and is suitable only for applications that can tolerate intermittent connectivity and large delays. The 3GPP has developed standards for an all-IP mobile network. This makes it possible to use QoS provisioning schemes that have been tested on other IP networks [5]. The DiffServ architecture is of particular interest to this project. Since DiffServ does not guarantee QoS for individual flows in a class of service (CoS), the inter-working of DiffServ and connection admission control at the edge of the access network (AN) and the core network (CN) is explored. The objective is to develop a scalable system in which pricing and QoS achieve equilibrium and the user is provided with friendly interfaces for influencing this relationship. The pricing scheme presented in this paper would provide mobile operators with the opportunity to allow users to specify preferences on tariff structures according to their spending budgets. It

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is noted that in order to provide VoIP and other multimedia services at the quality that is comparable to traditional packet switched GSM voice adequate resources should be allocated to each call session, and admission control would be enforced to prevent congestion. Pricing would be done in proportion to the guaranteed and achieved QoS. If the charges are high price-sensitive users would find them prohibitive as compared to costs of basic Internet telephony on fixed networks. Hence they need freedom to choose to pay more for enhanced QoS or to use a fixed or fixed-mobile Internet access solution that charges on flat rate without QoS guarantee; it was noted earlier that mobility is a characteristic of 3G that users cherish a lot. By providing pricing profiles that translate to decisions on whether to use strict QoS guarantees (platinum), or loose QoS guarantees (gold) or no QoS guarantee (silver) users would have options to choose. Silver would be set as a default profile for services like FTP, while gold may be set as default for streaming and real-time multimedia. These profiles would be supported on Next Generation Networks (NGN) that enable operators to support multiple access technologies for access to their service delivery platforms (SDP). In this paper we present the details of a proposed profile-based pricing scheme, we discuss the provisioning of QoS in 3G networks and present some results to illustrate the need for QoS in communication networks and how profile-influenced bandwidth allocations affect the performance of packet transport sessions. Using a multimedia service delivery scenario we discuss the applicability of the proposed pricing scheme in charging of 3G and Next Generation Network services. 2. Charging and QoS in communication networks Simplicity and the maximising of revenue for network operators are major factors in the choice of a pricing scheme. In terms of simplicity, the flat rate pricing (or flat pricing) scheme is at the top of the list [1]. Flat pricing has a close to zero network overhead, and users and service providers can predict their expenses and incomes, respectively. Despite these aspects, flat pricing does not support individual QoS guarantees to users as it lacks a balance between heavy and light utility users. To overcome the disadvantages of flat pricing, other pricing models have been proposed. The Paris Metro Pricing (PMP) [6] concept is based on a net-

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work that is partitioned into logical sub-networks that are allocated different levels of non-sharable network resources. Each logical sub-network operates on a best effort basis and has different price levels. Flat pricing is used in each sub-network; however, users would choose one of the logical sub-networks for the transmission of their traffic, which would implicitly define the service level to be achieved. In PMP, it is expected that few users would use the higher priced logical sub-networks; hence they would hopefully provide better service than the low priced sub-networks. Although PMP would allow users to select subnetworks with prices that favour them, it still has shortfalls in terms of adapting to congestion in individual sub-networks, and it does not consider the network transport characteristics of different applications. Responsive pricing achieves congestion control by increasing network prices when the level of network utilisation is high [1]. When this happens, price sensitive users are forced to reduce their traffic. The reverse is done when there is low network utilisation. Responsive pricing assumes that users are rational with regard to price signals that are relayed to them. Responsive pricing still does not address the issue of traffic differentiation for different applications. Priority pricing introduces priority classes, which are priced according to traffic handling precedence at congested network nodes [7]. Traffic belonging to high priority classes is charged at a higher price and receives higher forwarding preference at router queues. Users are expected to indicate their preferred QoS class by choosing the appropriate priority level. The performance penalty received for using a less than-optimal class is offset by the reduced cost of the service, whereas the monetary penalty incurred for using higher cost, higher quality service classes is offset by the improved performance. The PMP, responsive pricing and priority pricing schemes aim at supporting some form of QoS provisioning in communication networks. In IP networks, various proposals for QoS provisioning have been developed. The integrated services (IntServ) architecture was designed to provide QoS guarantees through reserving adequate resources for each flow along the path of the source to the destination of the communication [8]. Although IntServ would be the true way of providing resource guarantees in a multi-service network, it poses scalability problems for large networks, since

the state of each flow would have to be maintained at each network node. The complexity and scalability problems on IntServ have been addressed by the differentiated services (DiffServ) architecture, which is based on aggregating traffic from applications with similar network characteristics into classes of service (CoS) [9]. The complex task of filtering traffic into CoS, marking the DiffServ code point (DSCP) and policing the traffic is accomplished at the edge routers of the DiffServ network. Simple forwarding of the aggregate traffic for a CoS is done by the core routers based on the value of the DSCP that is mapped onto specific per-hopbehaviours (PHB). The DiffServ architecture does not have an inbuilt mechanism of achieving resource guarantees for individual flows in a CoS; however it can be combined with connection admission control (CAC), which helps in controlling congestion in communication networks. In terms of resource management admission control refers to a set of measures taken by the network to balance between the QoS requirements of new connections and the current network utilisation without affecting the grade of service of existing connections [10–12]. As being done in the IntServ architecture, admission control is traditionally performed on a hopby-hop basis. However, adding admission control functionality to all the core elements violates the DiffServ principle of leaving the core simple. Edge admission control that pushes the admission control functionality to the edge of the network is more suitable in this case. By exploiting the scalability aspect of the DiffServ architecture, the features of CAC, and network pricing schemes that influence user behaviour, a network pricing system that meets the QoS requirements of different applications and users can be developed. Tianshu et al. [13] investigate the integration of congestion-sensitive QoS-pricing schemes and admission control for multi-service networks. They use the time-of-day (ToD) pricing concept in setting prices for the access networks and a dynamic pricing strategy is used in the core network. In our previous work [14,15] we presented the idea behind a dynamic pricing approach for charging 3G network services. In the approach, we introduced profiles with different characteristics to suit the requirements of different users. In this paper, we give a further analysis of the proposed pricing scheme, and discuss ways of overcoming the challenges facing its successful deployment.

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3. Architectural model of the proposed pricing scheme The pricing scheme is modelled by introducing three profiles (i.e. platinum, gold and silver) into DiffServ classes of service. DiffServ and CAC are used to provide bandwidth guarantee for the admitted sessions. Network congestion is estimated by the number of active sessions in a particular profile. The CAC strategy is to limit the number of admitted sessions to a maximum of N. 3.1. Platinum profile The platinum profile of the dynamic QoS-based charging model (DQBCM) has the following characteristics: it offers a guaranteed QoS level, even during periods of network congestion and the prices for network resources vary with the demand for the resources. In a practical commercial network, platinum would correspond to a premium profile in terms of the priority, reliability and throughput received by the packets; however, in this paper the definition of QoS is confined to throughput, thus bandwidth is the only parameter of interest. Since the tariff rates will vary with network congestion, users of this profile are considered to be less sensitive to variations in network prices. The network resource pricing algorithm for this profile works as follows: the admission control function is configured to admit new PDP sessions until the number of active sessions becomes equal to Np. Np represents the allowed maximum number of active sessions in the platinum profile. This scheme works fine with session-based traffic, and the values of QoS parameters for a typical session can be pre-defined. Authorised QoS parameter values for PDP contexts associated with a session can be expressed as a fraction of the pre-defined values. A PDP session is established when the network opens the gate for packet filters associated with the authorised PDP context; the policy enforcement point (PEP), which resides in the gateway GPRS Support Node (GGSN) in 3G networks performs this function at the direction of the policy decision function (PDF). The tariff rates to be used for a given period are affected by the demand of network resources, which is represented by the number of active sessions (Ip). In a practical implementation (e.g. the UMTS) the amount of network resources allocated to different PDP sessions would be different. For this reason, the value of Ip is a hypothetical figure that could

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represent the actual number of connections being served, or the relative amount of network resources assigned to the active PDP sessions. Similarly, Np could represent the maximum number of connections that can be served by the platinum profile or the maximum amount of network resources that can be allocated to PDP sessions in the platinum profile. Eq. (1) presents the formula for varying the price of network resources in this profile n

T p ¼ aðI p Þ ;

ð1Þ

a is a pricing constant for the platinum profile, and it relates to the value of Np and factors affecting revenue targets for the network (e.g. capital and operational expenses). n specifies how fast the tariff rate increases. The value of n would be selected by the service provider. The prices for resources in the platinum profile will also be affected by the number of active sessions (I) that have been admitted to the specific CoS, and ideally it would depend on the number of active sessions on the network. Since the platinum profile offers higher QoS than the other profiles, the price for using this profile would be relatively higher than the other profiles. There is a level of congestion below which the prices for network resources in the platinum profile would be equal to those of the gold profile. This considers the fact that when the level of congestion in the network is low, traffic from the platinum profile would not get preferential treatment over traffic in the gold profile. Fig. 1 gives a graphical representation of the variation of platinum profile tariffs with the value of Ip. The differential pricing becomes effective when the value of Ip equals Sp. At this point it is assumed

Fig. 1. Platinum profile tariff rates.

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that the active flows in the platinum profile are receiving preferential treatment on the network. This implies that tariff rates for the platinum profile will be at the minimum (Tmin) for Ip 6 Sp, and at the maximum (Tmax) for Ip = Np. Thus, Eq. (1) becomes: n

T p ¼ T min þ aðI p  S p Þ ;

ð2Þ

which is applicable for Sp 6 Ip 6 Np. a can be determined by considering the maximum tariff level for the platinum profile as given in the following equation: T max ¼ T min þ aðN p  S p Þn :

ð3Þ

T min Hence a ¼ TðNmaxp S n . After substituting a, the forpÞ mula for charging communication sessions in the platinum profile is given by Eq. (4). It is applicable for all values of Ip 2 [Sp;Np]. Tp = Tmin for Ip < Sp and Tp = Tmax for Ip = Np

T max  T min n T p ¼ T min þ n ðI p  S p Þ : ðN p  S p Þ

ð4Þ

The platinum profile has great advantages to the network, for example it would enable the network operators to maximise profits when the number of subscribers using this profile is high. It is expected that with the popularity of multimedia applications, most of the users of real-time multimedia applications will prefer to use the platinum profile due to the guaranteed QoS. The guaranteed QoS is simply expressed as an equal partition of the total resource pool (Rp) distributed among the defined maximum admissible platinum profile sessions (Np), i.e. Qp ¼ Rp , and it is constant. By lowering network prices Np during periods of low congestion, the platinum profile would give incentives for users to use the network, hence improving network efficiency.

support low data rates. Streaming multimedia applications may use a combination of codecs supporting lower data rates and longer delays in playback buffers. The QoS received by active flows in the gold profile is inversely proportional to the value of Ig (Ig is the instantaneous value of the flows admitted in the gold profile), and it would be at the minimum (Qmin) when Ig = Ng. The instantaneous QoS value for the active flows can be written as Qg ¼ Qmax  b  ðI g  1Þm ;

ð5Þ

R

where Qmax ¼ 1g , i.e. the whole gold resource pool is used by one session and m defines how rapid the QoS level degrades with congestion. The minimum resource allocation per flow is represented by Eq. (6), hence the constant b is given in Eq. (7) m

Qmin ¼ Rg  b  ðN g  1Þ ¼ b¼

Rg ; Ng

N g  Rg  Rg Rg : m ¼ m1 N g ðN g  1Þ N g ðN g  1Þ

ð7Þ

Substituting b into Eq. (5), the instantaneous QoS formula for each gold session becomes: " # 1 m Qg ¼ R g 1   ðI g  1Þ  k Q : ð8Þ m1 N g ðN g  1Þ Here we introduce the correction coefficient kQ, as a decreasing function of the number of active sessions Ig, used to prevent the occurrence of congestion. This simply means that the bandwidth allocations for the gold profile sessions will decrease with an increase in the number of active sessions. Fig. 2 illustrates the variation in the gold profile QoS. The

3.2. Gold profile The characteristics of the gold profile are as follows: it uses a constant tariff rate for network services, i.e. the tariff rate is independent of the level of network congestion. Network congestion affects the offered QoS level, i.e. when congestion sets in the QoS parameters of each flow would be downgraded to achieve a limited level of QoS for all flows. Users of the gold profile are considered to be sensitive to variations in the price of network services, but they are tolerant to changes in the level of QoS. When QoS parameters are downgraded realtime applications would adapt by using codecs that

ð6Þ

Fig. 2. Gold profile QoS variation.

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optimal constant tariff is found by assuming an equal price for a resource unit for both the gold T T and platinum profiles; Q g ¼ pmax money/resource, Qp gmin where Qgmin is the minimum QoS achieved by the gold profile flows and TPmax is the maximum tariff for the platinum profile flows. Substituting R R Qgmin ¼ Ngg  jðN g Þ and Qp ¼ Npp , the tariff rate for the gold profile would be: T g ¼ T pmax

Rg N P jðN g Þ: Rp N g

ð9Þ

It is assumed that the users of the platinum profile have sharing priority over the gold profile adherents. However, the expansion of the platinum profile resources is done as long it does not affect the minimum required QoS for the gold profile flows. For the platinum profile we always have fixed R level of the QoS (Qp) equal to Npp . Substituting Np = N  Ng, and Rp = R  Rgmax = R  Qmin  Ng, the resource allocations for each flow in the platinum profile becomes Qp ¼

3.3. Silver profile The silver profile is designed to cater for pricesensitive users, whose basic aim is to stay connected to the network for long periods. It uses flat-rate pricing and provides network services using best-effort transport, hence it does not offer QoS guarantee to its users. This profile essentially accommodates the current trend in Internet communications, where best-effort transport offers no QoS guarantee to the users [16]. The use of flat-rate pricing in the silver profile will attract high service usage levels, which means that the network utilisation efficiency would be high. Lack of QoS guarantee for this profile makes it suitable for applications that tolerate intermittent connectivity (e.g. extreme bandwidth fluctuations and large delays). 3.4. Resource sharing between profiles During certain periods, some profiles would have excess resources that can be used by other profiles experiencing congestion. The platinum and gold profiles can be used to illustrate the resource sharing algorithm. The profiles are considered to share a common pool of resources equal to R. If the platinum profile is the recipient of the relocated resources, the effective value of Np, i.e. N 0p will be set higher. This would lead to a condition where Np < Ip and N p 6 N 0p , which means that the platinum profile would admit extra communication sessions. When this occurs, the network operators and independent service providers will be able to maximise on revenue generation by serving more users. On the other hand, when a low priority profile receives the relocated resources, the QoS of its active flows will be boosted and improved network performance will increase user satisfaction.

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R  Qmin N g ; N  Ng

ð10Þ

where R is the total resource amount; Qmin is the minimum required resource allocation per gold profile session; Ng is the maximum number of the sessions supported by the gold profile and N is the total supported sessions number for the whole resource pool. All these parameters are specified as input values for the pricing algorithm. The following inequality must be met: Ng < N, Qmin < Qp. The tariff rate for charging sessions in the platinum profile would become T p ¼ T min þ

T max  T min n n  ðI p  S p Þ : ðN  N g  S p Þ

ð11Þ

A graphical representation of the platinum profile tariffs with resource sharing is given in Fig. 3. There are also several restrictions for admitting DIp extra sessions in the platinum profile over the guaranteed number of sessions (N  Ng). If RI Q Ip + DIp > N  Ng, then I p þ DI p 6 Qg min . Substip tuting in Eq. (10), we obtain

Fig. 3. Platinum profile tariffs – with resource sharing considerations.

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DI p 6

ðR  I g  Qmin Þ  ðN  N g Þ  I p; R  N g  Qmin

ð12Þ

i.e. extra sessions can be admitted in the platinum profile if the bandwidth allocated to current active sessions in the gold profile is satisfactory. Once the resources of the platinum service profile have been appropriately allocated to the active sessions, the remaining part of the bandwidth pool can be shared between the active flows of the gold profile. The bandwidth allocated to each flow can be represented as Qg ¼

RðN  N g Þ þ I p ðQmin  N g  RÞ  kQ; I g  ðN  N g Þ

ð13Þ

which is valid for 1 6 Ig 6 Ng; the value of kQ may be determined experimentally. The graph in Fig. 4 illustrates the available bandwidth for the gold profile in respect to the number of sessions Ip and Ig. From Eq. (9), the tariff rate for the gold profile with resource sharing considerations becomes, T g ¼ T pmax

Qmin ðN  N g ÞjðN g Þ : R  Qmin N g

ð14Þ

3.5. Admission control The dynamic QoS-based charging model (DQBCM) admission control policy defines the algorithm for managing the admission of new sessions to the network. Admission control is per-

Fig. 4. Gold profile QoS – with resource sharing.

formed at the service profile level to ensure that the maximum admissible number of sessions (N) is not exceeded. The admission control function (ACF) is in charge of all session admission operations, i.e. monitoring the number of active sessions (I) in each profile and reporting to various entities that require this information. The ACF mainly deals with new connection attempts; however, when users change their service usage profiles during service delivery, it ensures that the request is handled without causing unexpected effect to the QoS received by flows in the affected profiles. This involves verifying that the condition I < N is satisfied for the requested profile, and that after the profile change, the relation I 6 N must be satisfied. To meet the QoS specifications of different profiles, admission control will be done for each profile, i.e. platinum, gold and silver in every CoS. By defining the maximum number of communication sessions each profile should handle, the CoS will in effect achieve the desired QoS levels. This would translate to the whole system when the effect each CoS has on the others is taken into account. The admission control strategy guarantees a minimum level of QoS performance for active flows in the gold and silver profiles; flows in the platinum profile are guaranteed a steady high QoS at all times. In the admission control strategy, when I = N, new session establishment attempts will be rejected. New session activation requests would be allowed after existing sessions terminate or get deactivated. This requires monitoring of the activity of all active sessions and defining conditions when an active session would be considered inactive; hence the admission control and traffic management function would close it to free network resources. The freed resources would be used to admit a new session request in the profile, or another profile would be allocated the resources so as to boost the QoS for its flows. In the UMTS network, the DQBCM admission control function would be located in the GGSN. The DQBCM ACF would co-ordinate with the policy decision function (PDF) and the application function (AF) to ensure the authorisation of packet data protocol (PDP) context requests is done according to availability of network resources. When a PDP context activation request is received by the ACF it is relayed to the UMTS PDF. The PDF will process the request and authorise the allo-

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cation of the resources requested for the PDP context. The resources for the PDP context would be authorised when the profile it falls in has capacity to service the request. To achieve this, the PDF and the ACF would keep track of the number (I) of admitted active sessions in each profile and all CoS and the sum of the authorised PDP QoS parameter values for all sessions in the profiles and CoS. The total amount of network resources allocated in a profile will be determined from the sum of the PDP context parameter values of its sessions. If all sessions in a profile were assumed to be allocated an equal amount of resources, the product of I and the values of the QoS parameters of one session would give the total amount of the resources allocated in that profile. By using the number of active PDP context sessions to represent the demand for network resources and the congestion level in the network, the admission control policy improves the achieved congestion control in the network. With information on resources allocation for all profiles and CoS, the PDF will indicate the possibility of relocating extra (unused) resources from one profile to another.

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incurred when Ip P Np. When content is purchased from independent service providers, the credit reservation would include the additional charge for accessing an application after credit reservation for the cost of the content has been done. 4.2. User involvement

The challenges facing the practical implementation of the DQBCM can be classified as user involvement and system integration [17]. The system integration challenges include support for current charging practices (e.g. prepaid billing). User involvement includes profile modification and the notification of network price changes.

Mobile units or user equipments (UE) are becoming advanced in terms of memory, processor speed, screen resolution, etc. Software for interpreting network management decisions can be stored on the UE, hence reducing the use of network resources in the transmission of control information between network entities and the UE. The software would have definitions for control and error codes that are exchanged between network entities and the UE. For example, when a user’s request for services in a given profile cannot be granted, the system would send an error code to the UE. The software will interpret the error code and display a message that is understood by the user. The UE should provide the users with appropriate interfaces for profile modification. Menu driven or web-based interfaces can be used for this purpose. As a way of encouraging users to optimise on the benefits of different profiles, profile modification should not incur major charges. Advanced optimisation of the benefits of different service profiles can be achieved by providing users with information on the congestion state of different profiles and the varying tariff patterns. Users of the platinum profile would find information on the tariff patterns to be useful in cases where cost minimisation is necessary.

4.1. Support for prepaid services

5. Design of the emulation framework

Prepaid billing is facilitated by online charging, hence charging information affects the real-time delivery of the service. For example when the user’s credit balance gets exhausted, service delivery would be terminated. Credit control mechanisms (e.g. Diameter credit control [18,19]) are used to facilitate the online charging process. In the DQBCM design, credit control measures like the Diameter application would be used to facilitate online charging and support prepaid billing. For the platinum profile the credit reservation would be based on the maximum possible charge that may be incurred for transporting the application over a given period (e.g. 1 min). The maximum charge would be

Various functions of the DQBCM architecture can be evaluated on a framework. In the evaluation framework used for this paper, the classes of service (CoS) are defined based on the network performance requirements of the traffic they would handle. In real testbeds and networks packets belonging to a given CoS would be identified from session description protocol (SDP) information in the session initiation protocol (SIP) signalling between the UE and the application function. In our QoS performance evaluation framework we consider the port numbers in the IP packet headers. Peer-to-server (p2s) and server-to-server (s2s) communications generally use standard port numbers

4. Technical considerations of the architecture

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that are issued by the Internet Assigned Numbers Authority (IANA), hence classifying their traffic is simpler, and can be done in a static fashion; however, peer-to-peer (p2p) sessions use ports that are randomly assigned by the end nodes. For this reason, the classification of traffic from p2p applications must be done dynamically. The user profiles are stored in a database on the network management system (refer to Fig. 5), and will be matched against the user’s static network ID. The network access identifier (NAI) for each UE is dynamically assigned. However, mapping of the NAI to the network ID makes it possible to identify the service profile applicable to the user. The UE will be mobile, hence access to the network is required and can be achieved by configuring a media access gateway (AG). In practical implementations, the AG would normally be remotely located, and a network can have many AGs that are managed centrally using a network access controller (AC). The service profiles selected by the users, plus other information related to the users should be stored in a database close to the AC. The AC would handle the connection admission control functions, traffic management and tariff generation, as illustrated in Fig. 5. The DQBCM system manages the radio access network resources, e.g. the total bandwidth Bt. Bt is a limited resource that would be allocated to the active flows that are admitted to each profile. Since the traffic control is based on the DiffServ architecture

[19], the assumption made is that traffic is handled according to the appropriate PHBs on the external networks. The main entities of the network system are as follows. 5.1. User equipment The user equipment enables the user to connect to the network, and also provides the profile modification interface. The interface can be menu-driven or web-based. In many networks, authentication and authorisation (AA) of the UE is required prior to gaining access to the network. In this framework, the AA procedure facilitates the functioning of the traffic control and the service profile management agents. The Ethernet media access control (MAC) address was used to uniquely identify every UE that was registered on the testbed network. The mobile units were used only to test the profile modification interface and the AA procedure. The MUs were issued with a NAI during the registration process. The NAI was an IP address that was dynamically issued by the AG, and its validity was achieved through binding it to the MAC address of the MU. Users would change their service usage profiles through a standard web page hosted on the AC. On the page they would view and change their current profiles. Fig. 6 shows the profile modification interface for one CoS; silver is the current profile for the user whose NAI is 10.130.8.20.

Fig. 5. Network management layout of the evaluation framework.

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Fig. 6. Profile modification page layout.

Since the MUs did not exhibit the flexibility that was required for evaluating the traffic control and tariff management functions of the AC, network traffic generators [22] were introduced. 5.2. Media access gateway The media access gateway (AG) assigns a NAI to the UE, and enforces the network access control decisions that are made by the AC. The AG performed the registration of the MUs by assigning them unique IP addresses from a predefined pool of addresses. Each IP address was bound to the respective MU’s MAC address, and the pair was sent as an AA binding message to the AC for authentication and authorisation. The transmission of the binding message was done through a UNIX socket that was established between the AG and AC whenever a new MU was detected. An AA reply was received by the AG after the binding information had been processed, and this determined if the MU would be authorised to use the network or not. Authorisation of the MU was done by enabling its IP and MAC addresses in the access list of the firewall on the AG. Fig. 7 illustrates the signalling information flow during the MU registration process. 5.3. Network access controller The network access controller (AC) performs the following functions: authentication and authorisation of MUs, connection admission control, traffic management and control (e.g. bandwidth management), user profile management and tariff generation. These functions are either done within the AC, as shown in Fig. 5, or the AC would authorise

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another network entity e.g. the AG to perform the function. The AC performs user authentication and authorisation (AA) upon receiving a request from the AG to which the UE is connected. The AC attempts to authorise the MU by verifying its MAC address and the corresponding enabled status in a database. The AC performs a two-step authorisation procedure before granting access to the user. In the first step, the UE gets authorisation to request network services. The second authorisation step is performed upon receiving service requests from the user. This step would only succeed if the user profile in which the requested service falls has sufficient network resources; this is done by the admission control function. When a communication session establishment attempt is made, the admission control function (ACF) initiates a session authorisation process. It relies on the CoS and profile information that is defined in the traffic control architecture of the system. In the AC block, the ACF was implemented by monitoring the number of TCP connections established between the MUs and servers on the LAN. The ACF operates at the profile level in each CoS. The CoS is identified by the destination and source port numbers in the TCP headers. The profile for the affected user is identified by the source or destination IP address (NAI) in the packet header. Packet classification was done using the IPtables package in Linux. A profile management function on the AC keeps track of profile changes made by users. A profile trigger function (PTF) responds to profile change requests from users, and it sends the user’s identity plus the profile information to the profile management function, which would update the profile database. The information sent by the PTF includes: the user’s NAI (IP address of the MU), the current (old) service profile, the requested (new) service profile and the CoS affected by the profile change. Tariff generation for the platinum profile is done as described in Section 3.1. The pricing function was implemented on the AC by configuring the ACF to write to a pricing file where the pricing function read the values of Ip. Once the tariff rates have been calculated, accounting of network resource usage is done to determine the usage for which the user should be charged. In the testbed, a portion of the required accounting procedure was implemented. This involved the initiation of a Linux IP accounting session at the time the mobile unit was granted global

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Fig. 7. Signalling information flow in the testbed.

authorisation. It should be noted that, for the platinum profile, a new accounting session must be initiated whenever a price update occurs. This would correspond to the initiation of a new CDR sessions in 3G systems as a result of change in QoS parameters, tariff periods and other factors. 6. Implementation of the emulation framework A framework for evaluating some functions of the DQBCM scheme was setup using wireless LAN and Ethernet infrastructure. The entities of the framework (e.g. the AG and the AC) were configured on Linux computers. Three Classes of service (CoS) were created based on standard port numbers of network applications (e.g. port 25 for SMTP and port 80 for HTTP traffic). Details on implementing DiffServ on Linux may be found in [20,21]. The profile selected by each user was identified by binding the NAI (i.e. UE’s IP address) to the corresponding profile entry that matched the user’s unique network ID (i.e. UE’s MAC address) in the profile database. The UEs were emulated using laptops with wireless connection to an access point. The various entities of the testbed worked as follows: each UE was issued with a NAI by a dynamic host configuration protocol (DHCP) process on the AG. The NAI and UE’s MAC address were then transmitted to the AC for processing. On successful authentication, the AG was instructed to perform

the first authorisation step. This was done by adding the NAI and the MAC address to an IP-TABLES firewall list on the AG. The second authorisation step was evaluated using TCP traffic. The TCP connection setup process uses Sync packets, which were only permitted through the AC’s traffic control system when I < N for the profile affected by the request. Admission control for TCP traffic was achieved using the connection tracking module that is available in Linux. It was used to count the number of active connections (I) in each profile. Connections were uniquely identified using their source/ destination IP address and port number combinations. In conducting a performance evaluation of the QoS mechanism, UDP traffic generators [22] were used to send packets in a controlled fashion. Three traffic generators sent packets to a traffic sink through the AC, on which bandwidth allocation and queue priority for each CoS and profile were specified. The traffic control at the AC was in effect responsible for varying the QoS parameters of the network. The network layout used in the QoS performance evaluation is given in Fig. 8. UDP traffic was used in these evaluations. To emulate traffic belonging to real-time, interactive and background CoS, packets of different sizes were used. Small sized packets, i.e. 50 bytes were used for real-time traffic, interactive CoS traffic was emulated by packets of variable length between 200 and 500 bytes, and

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Fig. 8. QoS performance evaluation layout.

background CoS traffic was emulated by packets of 1000 bytes. This introduced different amounts of packet processing times required at the network access controller.

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relied on the use of SYN packets, which are characteristic of TCP traffic. The performance of different profiles and CoS is expressed as a performance index function, which represents the network reliability, i.e. the ratio of the number of packets received at the traffic sink to the number transmitted from the source. The graphs in Fig. 10 and Fig. 11 show the difference in the QoS performance that is achieved by the real-time, interactive and background CoS under diverse resource availability conditions. In Fig. 10, where 170 Kbps of bandwidth is allocated to traffic in each CoS, all the CoS perform better due to availability of adequate resources. However, in Fig. 11 poor performance is experienced by the interactive and background CoS-each CoS is allocated 18 Kbps of bandwidth. This is attributed to the scarcity of network resources and the use a lower queue priority for the two CoS. The real-time CoS has a higher

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queue priority; hence it performs better under the two resource availability conditions. The performance index results are averaged over a period of 2 min. When bandwidth allocation amongst the three CoS is different, the CoS with greater bandwidth perform better than those with less bandwidth. In the evaluations the real-time CoS was allocated 28 Kbps of bandwidth, while the interactive CoS was allocated 16 Kbps and the background CoS was allocated 10 Kbps. In Fig. 12, when bandwidth sharing is not enabled, the real-time CoS performs better than the interactive and the background CoS. When bandwidth sharing is enabled, the average performance index of the interactive and background CoS improves before congestion sets in. This is because some of the unused bandwidth from

the real-time CoS is allocated to these CoS hence boosting their throughput. This trend is shown by the graph in Fig. 13. In Figs. 14 and 15, a comparison of the network performance for three profiles (i.e. platinum, gold and silver) when resource sharing is disabled and enabled, respectively, is given. The evaluations were done under the following conditions: the platinum profile was allocated 28 Kbps of bandwidth, the gold profile was allocated 16 Kbps, while the silver profile was allocated 10 Kbps. In the first set of tests, bandwidth sharing amongst the three profiles was disabled, while in the second case bandwidth sharing was enabled. The performance of the profiles when bandwidth sharing is disabled is given in Fig. 14. Fig. 15 shows the performance of the profiles when bandwidth sharing is enabled. The average performance improvement that arises due

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to bandwidth sharing can be attributed to the improved utilisation of network resources by all profiles when averaged over some period of time. In networks with greater magnitudes of bandwidth, e.g. WiFi and Wi-Max, bandwidth control would be required at bottle-neck points. These networks can support applications with higher bandwidth demand, hence higher bandwidth allocations can be done per profile. System stability will depend on the ratio of bandwidth allocations and the bandwidth sharing levels. 8. Multimedia service provisioning scenario In this section some IP Multimedia Subsystem (IMS) concepts are used to illustrate the applicability and benefits of the proposed pricing scheme to users with different tariff preferences. Access to the IMS can be provided using networks with different QoS and mobility characteristics. Users of NGN mobile devices can experience handovers to different access technologies depending on various factors. The preferred pricing profile may be used as a triggering factor for the handover. Using characteristics of pricing profiles given in Section 3 a service provisioning scenario involving three users of services falling in the real-time CoS, e.g., VoIP and the streaming CoS, e.g., IPTV is used to illustrate practical benefits of the proposed pricing scheme (refer to Fig. 16). The scenario involves a network supporting 3G and WLAN access to an operator’s

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SDP, i.e. the IMS. The users; James, Peter and Joe are accessing network services using dual mode devices (with 3G and WLAN support). James selects the platinum profile for VoIP and IPTV; Peter selects the gold profile for VoIP and IPTV; while Joe uses the platinum profile for VoIP and silver for IPTV. The table in Fig. 16 summarises the profile selections. In the network pricing profiles translate to QoS and cost preferences for services; moreover 3G access enforces tight QoS control and the trend is to use higher charges when compared to WLAN access. WLAN may offer some level of QoS provisioning through traffic differentiation on the IEEE 802.11e technology, but limited capacity on WLAN is a challenge to achieving sustainable QoS guarantees. However, higher data rates can be achieved though the supported speed of a mobile terminal is limited and would influence the service usage experience for a mobile user. It is clear that 3G would be the best technology to support the platinum profile. 3G and WLAN would be suitable for the gold profile, while the silver profile can be provisioned on WLAN and 3G as well; both access technologies can service the three profiles depending on network conditions and the characteristics of the user (moving/stationary). Network utilisation levels on 3G would be low during off-peak hours. James’ and Joe’s VoIP sessions would achieve good performance in terms of delay and media playback quality. This is a result of

Fig. 16. 3G and WLAN access to IMS services.

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abundant bandwidth and low congestion. Peter’s VoIP session will achieve the same performance as the platinum profile sessions. The per-volume of data charges incurred for the VoIP sessions would be equal for all users. The IPTV sessions for James and Peter would achieve good performance in terms of minimising the need to buffer packets for playback. Joe’s IPTV session would receive good performance as well. James’ and Peter’s IPTV sessions would be charged per-volume of data, while Joe’s IPTV session would be charged on flat rate basis. Some services can be provided over the WLAN network. Silver profile services that are charged at flat-rate, i.e. IPTV for Joe as well as gold profile services, i.e. IPTV and VoIP for Peter are good candidates for WLAN transport. When congestion level on 3G rises several events would be triggered on the network. James’s VoIP and IPTV and Joe’s VoIP sessions would be allocated the requested network bandwidth and priority levels to achieve high performance experience. Peter’s VoIP and IPTV sessions would experience noticeable degradation in performance due to reduced bandwidth. Context adaptation through the use of codecs that use higher voice compression and fewer video frames will be activated. If the speed of a given user terminal is low enough to support WLAN access the gold sessions would be handed over to WLAN to achieve higher data rates; this assumes that WLAN is not congested. In terms of charging VoIP and IPTV sessions for James’ and Joe’s VoIP session would be charged at higher prices. The higher charges reflect the QoS advantage gained for using 3G access. Note that there would be a cap on the level to which the charges can rise. This may relate to the charges that are incurred when using traditional packet switched voice and video calling. We note that charging for Internet communications does not include the distance component between the communicating parties. Thus, unlike conventional voice or video telephony whose charges include the distance component these services only incur increased congestion related charges while achieving high QoS. Peter’s sessions would be faced with the same charges as before. Joe’s IPTV session would experience considerable service degradation and may require long periods in video playback for re-buffering to occur. Charging for this session would be done on flat-rate basis. As congestion increases the experience of the silver sessions will continue to deteriorate. As pro-

posed earlier the network may handover the session to WLAN access. WLAN access would be charged using various tariff modes as specified in different pricing profiles. In the long run James would decide which calls or services to assign the platinum profile, i.e. premium services for which he is willing to pay higher charges during congested periods, but have them delivered. Peter may tolerate degraded performance for his VoIP and IPTV sessions during congested periods or he may consider using the platinum profile for VoIP for important calls, especially when he is on the move. Joe, in the long run, would make a decision similar to James regarding the IPTV session or he may find it only possible to use the service when stationary in WLAN coverage; he would be admitted on 3G only during extreme off-peak periods. The platinum profile guarantees resource availability on 3G and WLAN for mobile and stationary users, respectively. Platinum sessions on WLAN get higher priority for admission to 3G when the user becomes mobile. Gold and silver profile sessions may be handed over to WLAN if resources are needed for platinum sessions on 3G; or performance degradation would be experienced if the handover cannot occur. 8.1. Comparative evaluation of the DQBCM Using the evaluation criteria given in [1], the DQBCM scheme compares as follows: it is compliant with IP networks, hence it targets 3G and Next Generation Networks. Network resources are needed for resource usage accounting in the gold and platinum profiles. Connection admission control achieves congestion control in the network, which facilitates the achievement of individual QoS guarantee in the platinum and gold profiles. Achievement of high network efficiency is possible due to the incentives that come with the lowering of tariffs for services in the platinum profile during periods when the network has abundant resources. The characteristics of different profiles enhance flexibility in the system since users can select the profile that suits their needs and this encourages social fairness. Flat-rate pricing for the silver profile introduces economic fairness for users who cannot afford the higher charges of the gold and platinum profiles. The traffic control strategy works on short time frames, which reflects the characteristic nature of congestion in communication networks.

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9. Conclusions and recommendations The DQBCM scheme introduces service usage profiles, and formulates an architecture for using policy-based admission control and DiffServ to achieve the QoS and charging requirements of communication networks, i.e. improving of network efficiency, controlling congestion, providing social and economic fairness among users and maximising profits for the network operators and service providers. The network operators can configure the systems so that the actual change in tariff for the platinum profile occurs at certain interval of I  S, as opposed to a linear tariff change format. This will reduce the rate at which new charging detail record sessions need to be created. The application scenarios for the DQBCM scheme include: 3G networks, Next Generation Networks, Internet service providers and telecom operators who enforce SLAs with their users. From the results given in Section 7, the value of N (or maximum number of sessions) that would suit the desired network performance for each profile can be selected. The value will also influence the projected revenue. The proposed pricing scheme could be extended to other networks where service level agreements are enforced, and its effectiveness would be proven through market analysis after it is deployed on a commercial system. References [1] M. Falkner, M. Devetsikiotis, I. Lambadaris, An overview of pricing concepts for broadband IP networks, IEEE Communication Surveys 3 (2000) 2–13. [2] Ralph Kuhne et al., Charging in the IP multimedia subsystem: a tutorial, IEEE Communications Magazine (2007). [3] J. Suvanto, Multimedia messaging service for GPRS and UMTS, in: Proceedings of IEEE WCNC, September 1998, pp. 1422–1426. [4] 3GPP, Technical specification group core network; end-toend quality of service (QoS) signalling flows (Release 6), Tech. Spec. 3GPP TS 29.208 V6.3.0, 2005. [5] J. Diederich, L. Wolf, M. Zitterbart, A mobile differentiated services QoS model, Computer Communications 27 (2004) 1106–1114. [6] A. Odlyzko, Paris metro pricing for the Internet, in: Proceedings of ACM Conference on Electronic Commerce, 1999, pp. 140–147. [7] R. Cocchi, D. Estrin, S. Shenker, Lixia Zhang, A study of priority pricing in multiple service class networks, ACM, Communications Architecture and Protocols, 1991, pp. 123– 130.

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[8] R. Blake, D. Clark, S. Shenker, Integrated services in the Internet architecture: an overview, IETF RFC 1633, 1994. [9] S. Blake, D. Black, M. Carlson, E. Davies, Z. Wang, W. Weiss, An architecture for differentiated services, IETF RFC 2475, 1998. [10] 3GPP, Vocabulary for 3GPP specifications (Release 5), Tech. Ref. 3GPP TR 21.905 V5.8.0, 2003. [11] S. Malomsoky, S. Racz, Szilvester Nadas, Connection admission control in UMTS radio access networks, Computer Communications 26 (17) (2003) 2011–2023. [12] M.H. Ahmed, Call admission control in wireless networks: a comprehensive survey, IEEE Communications Surveys Tutorials, 1Q 7 (1) (2005). [13] L. Tianshu, Y. Iraqi, Raouf Boutaba, Pricing and admission control for QoS-enabled Internet, Computer Networks 46 (1) (2004) 87–110. [14] Vitalis G. Ozianyi, Neco Ventura, A novel pricing approach for QoS enabled 3G networks, in: Proceedings of the IEEE LCN Conference, 2005, pp. 578–586. [15] Vitalis G. Ozianyi, Neco Ventura, Dynamic pricing for 3G NETWORKS using admission control and traffic differentiation, in: Proceedings of the IEEE ICON Conference, 2005, pp. 838–842. [16] S. Shin, H. Weiss, H. Correa, A progressive analysis of Internet market: from best effort to quality of service, Telecommunications Policy 5–6 (2004) 363–389. [17] Vitalis G. Ozianyi, A novel pricing approach to support QoS in 3G networks, Master’s Thesis, University of Cape Town, Private Bag, 7701 Rondebosch, South Africa, 2006. [18] 3GPP, Technical specification group services and system aspects; telecommunication management; charging management; diameter charging applications (Release 6), Tech. Spec. TS 32.299 V6.2.0, 2005. [19] H. Hakala, L. Mattila, J. Koskinen, M. Sutra, John Loughney, Diameter credit-control application, IETF RFC 4006, 2005. [20] M. Lin, H. Luo, L.F. Chang, A Linux-based EGPRS realtime testbed software for wireless QoS and differentiated service studies, IEEE-ICC 25 (1) (2002) 1039–1044. [21] B. Hubert, Linux Advanced Routing and Traffic Control HOWTO, 2002. [22] R. Sandilands, Network traffic generator, Internet traffic, 2002.

Vitalis G. Ozianyi received the BTech. degree in Electrical Engineering from Moi University, Kenya in 2002, and the Msc. degree in Electrical Engineering from the University of Cape Town, South Africa in 2006. Currently he is a Ph.D. student in Electrical Engineering at the University of Cape town. Previously he conducted research on Pricing and Billing in Mobile Networks. His research interests include: QoS, pricing and billing, and IMS services.

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V.G. Ozianyi et al. / Computer Networks 52 (2008) 1433–1450 Neco Ventura is the Head of the Centre for Broadband Networks and the Director of the Communications Research Group in the Department of Electrical Engineering at the University of Cape Town. His current research interests are centered on Next Generation architectures, infrastructures, specifically in QoS and mobility support across heterogeneous networks.

Eugene Golovins received B.S. and M.S. degrees with distinction in electrical engineering from Riga Technical University, Latvia, in 2002 and 2004, respectively. He is currently working towards a Ph.D. degree in electrical engineering at the University of Cape Town, South Africa. During 2002–2004 he was a laboratory and teaching assistant at the Railway Transport Institute of Riga Technical University. At present he is a research assistant at the Department of Electrical Engineering, University of Cape Town. His research interests are in the area of wireless communication systems and networks, as well as fiber-optical communications.