Computer Standards & Interfaces 31 (2009) 685–692

Contents lists available at ScienceDirect

Computer Standards & Interfaces

j ourna l homepage: www.e lsev ie r.com/ locate /cs i

Price aggregation in an end-to-end QoS provisioning

Tomaž Turk ⁎University of Ljubljana, Faculty of Economics, Kardeljeva pl. 17, 1000 Ljubljana, Slovenia

⁎ Tel.: +386 1 5892 400; fax: +386 1 5892 698.E-mail address: tomaz.turk@ef.uni-lj.si.

0920-5489/$ – see front matter © 2008 Elsevier B.V. Adoi:10.1016/j.csi.2008.09.010

a b s t r a c t

a r t i c l e i n f o

Article history:

In this paper we are focusin Received 23 August 2006Received in revised form 11 August 2008Accepted 28 September 2008Available online 26 October 2008

Keywords:PricingCongestion controlQuality of serviceSmart MarketDifferentiated services

g on the possibility that information about end users' willingness to pay for datatransfer in the end-to-end manner is propagated through the communication network to achieve certainlevel of QoS. We propose a scheme where price information is propagated along the network path, whereeach network resource subtracts its share of value from packet's price. The proposed scheme establishes adirect relationship between expressed willingness to pay and gained QoS level. The scheme could be furtherdeveloped and implemented with compliancy to QoS standards, like Differentiated and Integrated Services.

© 2008 Elsevier B.V. All rights reserved.

1. Introduction

Telecommunication services such as videoconferencing, MPEGstreaming and VoIP impose relatively high demands to the Internetbackbones. A considerable amount of research has been done into thesuccessful QoS provisioning. New techniques, protocols and theore-tical investigations have been discussed and examined in the researchcommunity. New ideas range from better overprovision management[1], resource reservation techniques [15,5], to different pricingstrategies [7].

One of the core questions regarding QoS in packet-switchednetworks is the issue of traffic congestion. The congestion in packet-switched networks arises from the fact that the available capacity of agiven network resource is not sufficient to serve the total demand. Inthis paper, we treat the congestion as one of the main factors thatgreatly influence the QoS level as it is perceived by network end users.The traffic congestion influences all the categories of QoS, such asthroughput, delay and jitter. The congestion, as perceived by endusers, can have its origins in different parts of telecommunicationnetwork.

The issue of traffic congestion has been examined from differentaspects. Generally, the research on this phenomenon can be classifiedinto two main groups. One group of proposals examines technologicalapproaches (e.g. overprovisioning, the development of well-behavedprotocols, better management), while others are investigatingeconomic dimension and lead to different proposals for implementingpricing mechanisms. The review on different approaches is given by

ll rights reserved.

[4,6], while an example of their evaluation can be found in [9,10]. Thediscussions about economic ways for solving the congestion problemwere in recent years somehow superseded with technologicalsolutions, the most important being Differentiated Services (DiffServ)[2], Integrated Services (IntServ), RSVP and MPLS (for an example ofadvances in this field please see [6]). However, there are still someopen issues with this approaches, like problems of provisioningproper QoS levels (traffic engineering requires constant measurementand prediction), loss of granularity (bandwidth of 100 Mbps might beassigned to a HTTP traffic, but there is no inherent mechanism toensure that a single flow within it does not use up total allocatedbandwidth), and routing (QoS provisioning is solved separately fromrouting) [1]. Besides this, it is difficult to predict end-to-end behaviorwithout enforcing standardized policies across networks involvingseveral network operators with DiffServ approach, as in [5].

As for economic mechanisms solving the congestion problem,Smart Market received much of attention of the research commu-nity, since it has some interesting properties. One of the mostimportant ones is the total market clearance (the price of datatransfer is dynamic and equals the demand and supply at anymoment, so congestion is avoided). Besides, Smart Market operateson the finest granularity. This makes Smart Market capable ofmaking ideal congestion pricing. However, Smart Market is regardedas not deployable because of its excessive overhead and its manyrequirements from routers [19]. One of the reasons for this overheadis the price information — the price that a user is willing to pay for anetwork resource should be known to every resource along thepath, and each resource has its own price. Since the usage ofnetwork resources is normally not known in advance, this is evenharder to achieve [11]. Until now, the Smart Market approach wasdeveloped to be operational only on one network resource [16],

686 T. Turk / Computer Standards & Interfaces 31 (2009) 685–692

without any solution for a successful price aggregation in the end-to-end sense.

In this paper we are focusing on solving the problem of end-to-endQoS provision and pricing information flow, which we found to be acommon issue of technological approaches (e.g. DiffServ) and one ofthe economic approaches (Smart Market). The study presented in thispaper is conceptual in the sense that we are exploring a possiblesolution for price aggregation and pricing information flow for SmartMarket approach, where a limited network resource (which is apotential source of congestion) can be a network link, a group of links,or even a DiffServ domain. On the one hand, if the end-to-end priceaggregation that we are proposing would be used together withdynamic pricing (with Vickrey auctions for each network resource[13] or, better, using the users' responsiveness approach [16]), thiswould be a “pure” Smart Market, where the problem of informationoverhead is solved, since there is no need to encapsulate all the pricesfor each network resource used along the path in each data packet orflow. On the other hand, the price aggregation could be used withprices for network resources usage, where prices are set by theprovider and are changing less frequently. This would not be a “pure”Smart Market, since there would be no market clearance present in ashort run. (That is, at least until network providers set the prices in theway that the average provision equals with the demand for transfer.)

The stability of the proposed systemwith dynamic pricing and theprice levels vs. provided end-to-end QoS regime was tested with thesimulation [3,17]. The main objective was to find out whether thesolution leads to higher levels of QoS for users with higher willingnessto pay for their traffic. A discussion about the implementationpossibilities is also provided in the paper.

2. Smart Market as a congestion pricing scheme

From economic point of view several pricing schemes wereproposed in the literature, the most popular among them the SmartMarket approach [9] and Paris Metro Pricing [12,14]. Both schemeswere further explored, the former for instance in [16], the latter in[18]. The Smart Market approach introduced the idea of Vickreyauctions, which give the cut-off price of a network resource. This is theprice where the resource's capacity is exhausted, but no excessdemand is present, so no congestion occurs. The price is informative tothe user (the source of traffic demand) and to the network provider.The important fact of the Smart Market is that the price of traffic overthe network resource would be greater than zero only if the demandfor traffic over the network resource is greater than its capacity, so itimposes at least a two-part tariff.

The reasoning behind congestion pricing is the internalization ofnegative external effects. Namely, in congested network a user withhis traffic imposes costs to other users, since they don't receive theexpected utility, as it would result from the use of uncongestednetwork. Costs are incurred through denied services, longer delaytimes, etc. The internalization means that the individual sources ofnegative external effects should bear the costs of them. The concept ofSmart Market (which is the basis of our research) was proven to beeffective regarding its economic properties. Since each user is payingaccording to his willingness to pay, this leads to effective marketwhere consumer surplus is fully realized [10].

Examination of this scheme regarding incentives for users andproviders is given by [10], whereas further steps towards theimplementation are presented in [16], where the price for a congestednetwork resource is technically not established by an auction, but it'sestimated according to users' responsiveness, namely the priceelasticity. (Using the auction would mean that it is necessary forrouters to sort packets according to their bids, so the delay isinherently present.) Price elasticity can give the necessary informationabout the price/demand relationship. If the demand is too high, theprice can be changed accordingly.

The above solutionwas in [16] examined for one network resourceonly. However, the Smart Market approach needs one step further,specifically:

▪ users are perceiving the network as a whole and not as a series ofindividual nodes and links, so the congestion price should beaggregated along the path of the imposed traffic flow,

▪ away to efficiently distribute the information about users' willingnessto pay for their traffic through the network should be developed.

In this regard, it has been estimated that “classic” Smart Marketmechanism introduces very high accounting and communicationoverhead [11].

A possibleway to “distribute” this information along the path iswithresource price subtraction from the bid price, where the bid price is theprice a user is willing to pay not only for one network resource but alsofor all the resources along the path (i.e. from end to end). This“willingness to pay” could be marked in a special field within each datapacket. This solutionwas stated in [16], but it was not further examined.A similarapproach is given in recent studies from[13],where theprice ofevery network resource is being added to a field (and not subtractedfrom it). The approach has been described as “charging for actualpreferential service delivered” in [11], and it differs from true SmartMarket mechanism in a few characteristics, the most important onebeing the fact that prices are composed from three different parts andare not (pure) congestionprices in a sense ofmarket clearance (demandfor traffic at some price level could be higher than network resource'scapacity). Moreover, prices are set at some arbitrary level, which doesnot necessarily mean they can prevent congestion.

3. End-to-end pricing mechanism

Firstly we will present the way a congestion price for an individualnetwork resource is established, and then describe a possible solutionto price aggregation and information distribution issue. We areassuming throughout this paper that the network resource is anetwork link with a given capacity.

3.1. Congestion price for individual network resources

As already mentioned, Smart Market establishes cut-off price withthe concept of Vickrey auctions [9]. If a network resource were anetwork link, this would mean that the node (a router) should firstlysort the packets, which are to be routed along the network link,according to their bid price. The capacity of the link determines thenumber of packets for which it is possible to use the link. In that way,the price of the link is established, and packets with higherwillingness to pay would be admitted to use the link.

The above reasoning has some inherited technical drawbacks [16]:

▪ The arriving packets are collected in a queue at the networkgateway until the new price is established. The gateway admitspackets in descending order according to their bid, which causes adelay in providing the service. The auction algorithm requires aspecific time interval to collect packets and then it can transmit thepackets with the appropriate bids and reject all the other packets.Only then can it proceed to the next packet collecting time interval.

▪ Packets that are dropped by a network resource present anadditional load to other network resources. The pricing mechan-ism should avoid the traffic of these packets, which would berejected later in the network. Thus, it is essential to provide meansfor sending the price information back to end systems.

The first issue can be solved with considering the concept of priceelasticity. Price elasticity measures how much the demandedquantity of a good changes when its price changes. If we knew the

Fig. 1. User at node A initiates a data stream from node A to node C. (w denotes user's willingness to pay, p's are connection prices.)

687T. Turk / Computer Standards & Interfaces 31 (2009) 685–692

price elasticity for a network resource, then the resource's price forthe next period can be established in relatively simple manner. Theway the coefficient of elasticity could be estimated in practice, byeach network resource, is examined in [16]. Besides this, thecoefficient has a nice property, namely its stability is higher thanthe stability of demand. Unfortunately, its value cannot be estimatedin general, since it depends from “local” characteristics of everynetwork resource, for instance from demand on network resources inthe neighborhood (its complementary and competitive networkresources). On the other hand, a constant value of elasticity can beused for price calculation (such as −1), and if price calculation isperformed in short time intervals (e.g. a second) then the need tobuffer excess packets is greatly reduced.

The price elasticity of a network resource can be defined in twodimensions, regarding the definition of the network resource:

▪ On a micro level, the network resource can represent one networklink with a particular router with its characteristics (buffer,throughput capacities, etc.). On the other hand, a resource can bea group of network nodes and connections, like a DS domain.

▪ The network resources can be defined at some quality of servicelevel.

In combination, a network provider can define a network resourcewith its price as a specific QoS class of service provided by a group ofnetwork devices.

3.2. Price aggregation and information distribution

It is reasonable to accept the idea that if the pricing mechanism forthe data transfer is deployed, a user should pay the sum of prices ofnetwork resources along the network path, which are involved in aparticular data transfer. In most cases, it is not in a user's domain tocontrol which particular network resources are used for a service.Thus, it cannot be expected that a data packet would include one bidprice per each link along the path.

Fig. 2. User at node A initiates a data stream from node C to node A; his request for service tw2 for answer, p's are connection prices.)

The idea for the price aggregation is as follows: a user expresses hiswillingness to pay for a data transfer over the network (from end-to-end). Each network resource (node) examines this information,which should be provided in each data packet. Willingness to pay isexpressed in a monetary value per volume (e.g. cents per MB). Alongthe route, the price for a particular network resource the packet isusing is subtracted from this field. The packet is then transmitted tothe next network resource in the network path.

Fig. 1 shows a data packet traveling from end node A to end node C.B nodes are establishing congestion prices for connections, assuggested in Section 3.1. On every network node the price for thenetwork link is subtracted from packet's bid. The incentive for a trafficflow is coming from a user at the node A; in this case, his endapplication can provide the information about his willingness to payto the network, together with uploaded data stream. Fig. 2 shows thesituation where the incentive for data traffic is coming from a user atthe node A. Data traffic from node A to C represents a request forinformation service (e.g. MPEG stream) which should be served byend node C (server), so it is clear that the bid price for the requesteddata stream (from C to A) should be somehow embedded in a request,unless it can be assumed that the network service a user is utilizingneeds the same QoS level for both, requests and answers. (In this case,the remaining bid price of data transfer from A to C could be used as astarting bid price in opposite direction.)

In practice, a user would set up his willingness to pay for eachapplication he uses and he would need to change it only occasionally.The way the information about users' willingness to pay is propagatedthrough the network would depend from Internet service used. Twobasic models are presented in Figs. 1 and 2 above. These two modelscould be used in different combinations with different Internetservices. Consider for instance a “direct” videoconference of twousers (without intermediary server); each of them could pay for thetraffic he uploads (in this case his packets would contain his chosenwillingness to pay). There is also a possibility that each user pays forthe traffic he receives. If this is the case, his willingness to pay shouldtravel together with his request to the sender.

ravels from node A to node C. (w1 denotes user's willingness to pay for service request,

Fig. 3. Packet evaluation algorithm in structured language code.

688 T. Turk / Computer Standards & Interfaces 31 (2009) 685–692

We can see that in the first model the propagation of users'willingness to pay can be embedded in each data packet, to be directlyused by network nodes. The second model is more complex, since thedata stream (which should receive better QoS level) originates from aserver. It is obvious that users' willingness to pay must be included inclient's request, and that it should be properly used by a server(embedded in the data stream requested).

The way the information about users' willingness to pay is handledthus depends from a particular Internet service. For each service, thebest possible way should be implemented. There is also a possibilitythat the technology implements not one but two or more models andusers agree on themodel used on per session basis. For instance, in theabove case on videoconferences users could choose:

▪ that each user pays for their upload, or▪ that each user pays for received data stream.

3.3. Two basic QoS classes

If we return to price subtraction per se, it can happen that theremaining bid price at some B node is not high enough to pay forpacket's travel along the next hop. If that happens, it means thatpacket's bid is not high enough and it should not receive a full service.This would mean that the packet is dropped, but another possibility isthat the packet continues its travel along the path, but without anyQoS priorities, or the priorities would drop accordingly to theenvironment in question (e.g. within DiffServ domain). In practice,relatively long paths (paths with many hops between networkresources) would be more expensive for end users in comparisonwith shorter paths.

In the proposed pricing mechanism we distinguish between paidand unpaid (free) traffic. The packets at a particular network resourcewhere the remaining value is less than the price for of this resource areconsidered to be propagated in the best-effort manner through thenetwork. The pricingmechanism thus introduces two classes of traffic,namely paid traffic (this is the traffic where the remaining value ofwillingness to pay is equal or greater than the price of next hop) andbest-effort (free) traffic.

Zero-cost service class would be used in two cases:

▪ it would be used by users who are not prepared to accept any costof “per volume” data traffic for a particular Internet service,

▪ it would be used by data traffic where the expressed willingness topay is not enough to cover the total costs of transport through thenetwork path.

If users accept the current price, they get a higher level of QoS. Ifthe price is too high, they can switch to zero-cost service and use theresource in an “old fashion” way. In introducing the pricingmechanism in its first phases, the zero-cost service could also bedeployed by users, which are not yet prepared to accept the ideabehind the new approach.

If the expressed willingness to pay in not enough to cover the totalcosts of transport through the network path, the packets are notdropped but continue their way to destination in a best-effort sense.User is charged only for the packets that are regarded as paid traffic.

3.4. Packet treating algorithm on network nodes

The algorithm for packets scheduling would be implemented onnetwork nodes. These should establish a connection price for eachconnection, on regular basis. The simulation tests we performedshow that the price mechanism is feasible when the length of pricecalculation interval is shorter than 5 min. We believe that realtraffic experiments should give appropriate estimates of thisparameter.

A data packet is normally accepted at its arrival and gets into theFIFO queue, if the network node buffer is not completely occupied(Fig. 3). If this is not true, packets that could be paid for (theirwillingness to pay is greater than the price for the connection theywould be routed through) are considered to have a priority and arenot dropped. They substitute the packets with their willingness to paylower than current connection price.

When a packet is scheduled for transmission the current connec-tion price is subtracted from its willingness to pay and the packet canleave the node. A record about a value transaction could then be madefor the accounting purposes. If the packet being transmitted isconsidered to be served on best-effort basis, its willingness to pay isnot affected.

4. Performance study

Performance study was conducted by simulating the proposedpricing mechanism within a modeled communication network. Weused the simulation approach as it has been described in [17], wherethe data traffic is represented as a continuous (fluid) flow in discretetime. This approach provides a simple description of network pathsbetween nodes, but which enables to track the explicit routinginformation for each end-to-end data flow. The model can continu-ously simulate each source's traffic and its influences towards thenetwork elements. Each link on this path occupies or uses onenetwork connection between network nodes (e.g. fiber, virtual privateconnection), and network connections aggregate the traffic of severallinks, arriving from several sources. The information about actualpaths (the definition of links) represents routing information, and canbe dynamically changed during the simulation. Different trafficshaping policies can be studied with this kind of network model.The network topology and routing information is represented byrelatively simple matrix algebra.

In addition, the proposed pricing mechanism can be simulated byconstructing the simulation model in such a way that pricinginformation is represented as value flows. This is represented as aparallel information flow (parallel to data flows), and it's called thevalue flow in our model. All functionality of such computer network isembedded within the model, together with measuring tools.

For the purposes of our research, we prepared an example of thenetwork topology (Fig. 4) which can be represented by the followingmatrix notation, adopted from [17]:

S =1 0 0 01 0 0 00 0 1 0

0@

1A; L =

0 1 0 00 0 0 00 0 0 10 0 0 0

0BB@

1CCA; C =

1 00 10 11 0

0BB@

1CCA; N =

1 0 00 1 00 0 10 1 0

0BB@

1CCA :

Fig. 4. A simple computer network represented by sources, nodes, connections and links.

689T. Turk / Computer Standards & Interfaces 31 (2009) 685–692

4.1. Different pricing policies

The above framework enables the analysis of the proposed pricingmechanism from different points of view. Two general questions arise:

▪ can the proposed pricing scheme enable a proper price/perfor-mance relationship?

▪ if this is true, are there any differences in behavior of a systemwhen different pricing policies are employed?

The pricing policy in our model is a decision about the networkcapacities that are being reserved for paid traffic. On the one hand, anetwork operator can decide on a percentage of connection capacitieswhich are reserved for paid traffic. This percentage would be fixed byoperator and could be changed according to his decisions. This kind ofpolicy could be used in first stages of pricing implementation or incircumstances when there is a need to provide a best-effort regime at acertain level. On the other hand, the network capacities for paid and freetraffic could be dynamically changed in real-time, according tomeasurements on network nodes, which would give the relationshipbetween thedemand forpaid and free traffic. Thecapacities for each type(class) of traffic would be established according to this ratio. In this way,there would be no interference on the market by the network operator.

4.2. Simulations setup

A series of Monte Carlo simulations was performed for bothpolicies. The simulation time is 1 h (3600 s) of computer network

Table 1Traffic demand characteristics of data sources.

Source Willingness to pay(ws in MU)

Average data traffic for activeperiod (λs in Mbps)

Active period(in seconds)

1 10 10 0–36002 9 10 0–36003 8 10 0–36004 7 10 0–36005 6 10 0–36006 5 10 0–36007 4 10 0–36008 3 10 0–36009 2 10 0–360010 1 10 0–360011 0 10 0–360012 0 10 0–360013 0 10 0–360014 0 10 0–360015 0 10 0–360016 0 10 500–350017 0 10 500–350018 0 10 1000–300019 0 10 1000–300020 0 10 1500–250021 0 10 1500–250022 0 10 2000–250023 0 10 2000–2500

operation. We used the network setup as presented in Fig. 4, but with23 links. There are 23 data traffic sources connected to n1 generatingthe traffic directed to n3. Demand characteristics of traffic sources arepresented in Table 1. Their willingness to pay is constant through thesimulation time. Sources 1 to 10 are prepared to pay for their traffic.The differences between them enable us to analyze the dependency ofachieved QoS level on users' willingness to pay. Sources 11 to 15 arenot prepared to pay for their traffic, but are imposing the same datatraffic demand as sources 1 to 10. Other sources (16–23) producetraffic flows mostly in the middle of simulated time, thus producingcongestion (together with other sources), since the connectioncapacities for both connections are less than the traffic demand intime period from 1500–2500 s of simulated time (Table 2). Allavailable connection capacities are constant through the simulationtime at 200 Mbps per connection (c1 and c2). Buffer capacities are alsoconstant and equal for all network nodes (at 3 MB).

4.3. Price mechanism stability

Firstly, a set of simulation runs was performed where an overallstability of the systemwas studied. By employing the concept of priceelasticity [16] the price mechanism is stable as it provides prices asexpected (Fig. 5), even in the conditions when the demand for trafficis higher than the available connection capacity (in time interval1500–2500 s). The available capacity for paid traffic was fixed at 20%of all available capacity, for both connections. It can be seen that thepricing mechanism established equilibrium after the first 500 s,where the price for the first connection settled at around 6.5 MU perMbit. The price for the second connection firstly jumped toapproximately 2 MU, but because of a higher price for the firstconnection, some of the paid traffic was considered as non-paid afterleaving the first connection. That was the reason for significant pricereduction for the second connection. In the congested state, the pricesfor both connections were higher. We also noticed a significantimpact of node buffers, since they introduce inertia into the system.This was the reason for higher price for the second connection in timeinterval 1250–2500 s.

The review of price stability for the second policy (adaptiveconnection capacities for paid traffic) shows that in the conditionswhere there is relatively low amount of paid traffic, price fluctuations

Table 2Average data traffic rates originating from all sources.

Time periods (in seconds) Average data traffic rate (Σλs in Mbps)

0–500 150500–1000 1701000–1500 1901500–2000 2102000–2500 2302500–3000 1903000–3500 1703500–3600 150

Fig. 5. Price fluctuations for two network connections.

Fig. 7. Average traffic costs compared to expressed willingness to pay under differentpolicies.

690 T. Turk / Computer Standards & Interfaces 31 (2009) 685–692

can be relatively high. This can be avoided if the policywould include arule about a minimum capacity for paid traffic (e.g. “the capacity forpaid traffic is at least 10 Mbps or higher, according to the ratio paid/non-paid traffic”).

4.4. Pricing policy with fixed capacities for paid traffic

Simulation runs show that the pricing policy with fixed capacitiesis feasible. We tested the pricing policy where the capacity for paidtraffic was fixed at 20% of all available connection capacities. Therewere three sets of simulations runs, where the price mechanismdynamically established a new price for a connection in 15, 60 and120 s intervals. For each setup, 100 realizations were calculated. Fig. 6shows the average values of the percentage of dropped packets (byboth network nodes) compared to the expressed willingness to pay(bold lines, denoted by fixed in legend). It can be seen that theexpressed willingness to pay greatly influences the established QoSlevel, measured by the percentage of dropped packets. There are nosignificant differences in the effectiveness of the pricing mechanismdue to different price calculation intervals.

Fig. 7 shows that the established average costs in MU per Mbitare practically the same for each of three setups and are according to

Fig. 6. Average percentage of dropped packets compared to expressed willingness topay under different policies.

expectations higher for higher values of expressed willingness topay.

A similar set of simulation runs was performed with 50% ofconnection capacities available for paid traffic. The results show thatthere were practically no dropped packets for paid traffic— all sourceswhere the willingness to pay is greater than 0 introduce less trafficthan the available connection capacities allows. When the demand forpaid traffic gets higher than the available capacities for it (we testedthis with 45% of connection capacities available for paid traffic), thetraffic with the lowest willingness to pay is firstly affected (denoted byFixed (45%) in Figs. 6 and 7).

4.5. Pricing policy with adaptive capacities for paid traffic

Simulation runs with a similar setup were used to test the“adaptive” policy, where there is no interference from a networkoperator on the capacities available for paid traffic. Nevertheless,according to our findings about the price mechanism stability we useda policy where a minimum capacity for paid traffic was reserved. Fig. 6shows the result from three sets of simulation runs (average valuesfrom 100 realizations per set). It can be seen that there is adependency of QoS level from the expressed willingness to pay. It

Fig. 8. Connection capacities for paid traffic under the policy of adaptive capacities forpaid traffic.

691T. Turk / Computer Standards & Interfaces 31 (2009) 685–692

seems that the length of price calculation interval influences theeffectiveness of pricing mechanism. We can say that if the pricingmechanism establishes a new connection price more frequently, itresponds more appropriately to the traffic conditions. As can be seedin Fig. 8 the available capacity of the first connection for paid traffic isrigorously established at lower levels with relatively short pricecalculation intervals (15 s). Due to a lower amount of paid traffic(in comparison to free traffic) the capacity for it drops, but not belowthe minimum value (in this set of simulation runs the policy “thecapacity for paid traffic is at least 20% or higher, according to the ratiopaid/non-paid traffic”was used, with 200 Mbps total capacity). In thesimulated case longer price calculation time intervals lead to betterQoS levels and lower costs for paid traffic, but also to worse conditionsfor free traffic.

The analysis of the model has shown that there is a directrelationship between themeasuring technique and the accuracy of thepricing mechanism. Averaging the rate of paid traffic duringmeasurements (as a way to establish the capacity for paid traffic inthe next time interval) considerably affects the “rigidness” of themechanism. At this stage of our research, this remains an openquestion to be investigated in our future research, especially in theconnection with the self-similarity phenomenon [8].

5. Implementation possibilities

The implementation of the mechanism that we are proposing canbe gradual, since it doesn't exclude the best-effort traffic. Best-efforttraffic is a crucial part of the system, and it can be still used for QoSelastic services like electronic mail and for the end users which arenot sensitive to the QoS. Besides, gradual introduction of theproposed mechanism can establish a considerable level of freedomto network users and providers to try, test and possibly adopt thenew approach.

As with any pricingmechanismwhich has been proposed there areboth technical and organizational questions regarding implementa-tion possibilities. A review of necessary functions that each pricingmechanism should implement is given in [7]. This includes accountingand billing, besides core functionalities which are covered in thispaper (like measurements of network resources usage and evaluationof packets.) Furthermore, on the organizational level, a directory of IPaddresses should be available for the accounting purposes. In this way,the network operators could perform a proper billing activity. Thebasis for this would be a source or a destination data packet's address,depending on the model used, as discussed in Section 3.2. Whenimplementing the proposed pricing mechanism, the network opera-tors should agree only on the accounting level, in the same way asroaming agreements are established in mobile telephone networks.The issue that remains open is the security of pricing informationembedded in data packets, together with the genuinity of trafficorigins (to whom the traffic belongs). The proposed solution is basedon sending the information about willingness to pay over the networkand its correctness. That's why the implementation should take intoaccount appropriate security and privacy measures, so that thisinformation could not be within reach of malicious users or otherinfluences. It should be protected against reading and changingoutside some defined security scope. This issue is outside the scopeof our present focus, but nevertheless new technologies like IPv6,certificates and security tokens techniques should be a part ofimplementation.

Two new algorithms should be implemented on the networknodes which are responsible for network resources, the first one beingthe dynamic price calculation with users' responsiveness (priceelasticity) estimation algorithm (described in [16]), and the secondone the packets evaluation (presented in Fig. 3). An interestingquestion is whether this kind of pricing mechanism can be developedto be complementary and compliant to the technological approaches

when dealing with congestion, like DiffServ. For instance, theelements of DiffServ domain could be regarded as network resources(with dynamic prices). The packet evaluation algorithm (in Fig. 3)would be deployed at DS boundary (ingress) nodes, specifically by DSTraffic Conditioner Block [1]. In this way, the DiffServ issues with end-to-end behavior could be avoided without the need to enforcestandardized DiffServ policies across networks of different providers,and QoS provisioningwould be easier. Together with this, the questionof actual price encapsulation within the traffic streams should besolved.

6. Conclusion

We have shown that the proposed pricing approach is effective inthe way it establishes a direct relationship between the expressedwillingness to pay and the established QoS level. Its main achievementis that it solves the issue of cost aggregation and informationdistribution in the network. However, our intention is to furtherstudy the implementation possibilities for this approach, especiallyregarding its compliancy with state-of-the-art technical ways of QoSprovision, like DiffServ and IntServ/RSVP.

The proposed pricing mechanism has some interesting properties.It doesn't exclude the best-effort network traffic. Routers, servers andclient applications with new protocols could be gradually involvedinto a scheme, if a new practice would prove to be useful (to users andnetwork operators). It is not necessary that the transition isinstantaneous.

Future performance studies should take into account complexnetwork setups, where the influence of network operators' behaviorcan be analyzed, especially to discover whether there are possibilitiesto influence the fairness principles of pricing mechanism.

Acknowledgements

The research presented in this paper is funded by the SlovenianResearch Agency (ARRS) (Ref. no. P2-0037).

References

[1] S. Blake, An Architecture for Differentiated Services, RFC2475, 1998.[2] Cisco Systems, DiffServ— The scalable End-To-End Quality of Service Model, White

Paper, Cisco Systems Inc., 2005.[3] L. Breslau, D. Estrin, K. Fall, S. Floyd, J. Heidemann, A. Helmy, P. Huang, S. McCanne,

K. Varadhan, Y. Xu, H. Yu, Advances in network simulation, IEEE Comput 33 (2000)59–67.

[4] G. Davies, M. Hardt, F. Kelly, Come the revolution— network dimensioning, servicecosting and pricing in a packet switched environment, Telecommun. Policy 28(2004) 391–412.

[5] A. Helalia, A. Soudania, S. Nasria, T. Divouxb, An approach for end-to-end QoS andnetwork resources management, Comput. Stand. Interfaces 28 (2005) 93–108.

[6] S.-H. Jeong, H. Owen, J. Copeland, J. Sokol, QoS support for UDP/TCP basednetworks, J. Comput. Commun. 24 (2001) 64–77.

[7] M. Karsten, J. Schmitt, B. Stiller, L. Wolf, Charging for packet-switched networkcommunication — motivation and overview, Comput. Commun. 23 (2000)290–302.

[8] W.E. Leland, M.S. Taqqu, W. Willinger, D.V. Wilson, On the Self-SimilarNature of Ethernet Traffic, IEEE/ACM Transaction on Networking, vol. 2,1994, pp. 1–15.

[9] J. MacKie-Mason, A Smart Market for Resource Reservation in a Multiple Quality ofService Information Network, University of Michigan, Ann Arbor, 1997.

[10] J. MacKie-Mason, H. Varian, Pricing the Internet, in: B. Kahin, J. Keller (Eds.), PublicAccess to the Internet, MIT Press, Cambridge, 1995, pp. 269–314.

[11] T.T. Nguyen, G.J. Armitage, Evaluating Internet pricing schemes: a three-dimensionalvisual model, ETRI J. 27 (2005) 64–74.

[12] A. Odlyzko, Paris Metro Pricing: the Minimalist Differentiated Services Solution,Proceedings of the Seventh IEEE/IFIP InternationalWorkshop on Quality of Service(IWQoS'99), 1999, pp. 159–161, London, UK, June.

[13] A.J. O'Donnell, H. Sethu, Congestion control, differentiated services, and efficientcapacitymanagement througha novel pricing strategy, Comput. Commun. 26 (2003)1457–1469.

[14] D. Ros, B. Tuffin, A mathematical model of the Paris Metro Pricing scheme forcharging packet networks, Comput. Networks 46 (2004) 73–85.

692 T. Turk / Computer Standards & Interfaces 31 (2009) 685–692

[15] F. Sallabi, A. Karmouch, K. Shuaib, Design and implementation of a networkdomain agency for scaleable QoS in the Internet, Comput. Commun. 28 (2005)12–24.

[16] T. Turk, B. Jerman-Blažič, Users' responsiveness in the price-controlled best-effortQoS model, Comput. Commun. 24 (2001) 1637–1647.

[17] T. Turk, System dynamics simulation of computer networks: price-controlled QoSframework, Math. Comput. Simul. 78 (1) (2008) 27–39.

[18] M. Yuksel, S. Kalyanaraman, Distributed dynamic capacity contracting: an overlaycongestion pricing framework, Comput. Commun. 26 (2003) 1484–1503.

[19] M. Yuksel, S. Kalyanaraman, Simulating the Smart Market Pricing Scheme onDifferentiated Services Architecture, Proceedings of the GLOBECOM, 2001.

Tomaž Turk is an economist and has a PhD in information sciences. He is an assistantprofessor and researcher at the University of Ljubljana, Faculty of Economics. He teachesDevelopment of Information Systems, Economics of Information Technology, Econom-ics of Telecommunications, and Business Simulations. Currently his research workincludes themes from communication networks management, internet society issuesand economics of information systems.