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National Institute of Technology,Karnataka

Major Project Report

Dynamic Pricing of call rates

Submitted By

Amit Rajawat

Anuj Jain

Chetan Dugar

10CO11

10CO32

10CO106

Submitted toDr. Swapan Bhattacharya

Department of Computer Science and Engineering

Department of Computer Science and Engineering

National Institute of Technology Karnataka, Surathkal

Srinivasnagar, India 575025

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Contents

1 An overview of Dynamic pricing for call rates 21.1 What is Dynamic Pricing? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2 An overview of Prior Work 3

3 Scope of our work 5

4 Problem Statement 6

5 Problem Addressing 75.1 Game Theory Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75.2 Challenges faced . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

6 Evaluation Matrix 9

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1 An overview of Dynamic pricing for call rates

In the current scenario we come across flat-rates for calls made using any cellular network. This is clearlyneither the best choice for the user nor for the network provider.

The network provider wants to make the most efficient use of the bandwidth and make the maximumprofit out of it. So, he wants more users to use his network when it is under-utilized. Similarly at the

time of congestion, he knows that the demand for his service has increased but he doesnt have any meansto make extra profit out of it.

Similarly, the user wants to have the best possible service at the minimum possible price. But in staticpricing mechanism there is no incentive given to user to make calls when the network is underutilized.And at the peak hours of the network, he faces a degradation of the quality of service in-spite of payingat the same call rate.

So, we propose a solution to this problem using the mechanism of dynamic pricing. This is profitableboth to the user as well as the service provider. We show the use of the dynamic pricing at the extremeends of the congestion in the network (over-congested and under-utilized). Profit of this mechanism isobviously witnessed even in the average congestion hours.

At the peak hours of congestion, users face a degradation of QoS. The network providers even haveto block calls to end the congestion. Using dynamic pricing, the users shall be guaranteed asked level of

service and the providers will be benefitted with larger revenue due to increase in call rates coming fromhigher demand.When the network is under-utilized, the bandwidth of the network is wasted. So, the reduce in call

rates due to the reduce in demand will increase the willingness of the users to pay. Thus bandwidth shallbe efficiently utilized and network provider shall earn larger profit.

1.1 What is Dynamic Pricing?

Dynamic pricing is an approach which facilitates network provider to regulate the network usage bycontrolling the Quality of Service to the exising users on network and varying the call rates to the newcalls to ensure optimal usage of network resources. From subscriber point of view it provides optionsof switching among different networks available which best matches with users needs thus avoiding

monopoly rates of any single network provider.

Dynamic pricing strategy

Allows providers to recover their operating costs and finance future capacity expansions

It can lead to a more efficient use of the network resources by providing sufficient incentives tousers, and

Moreover, enables the creation of a healthy market environment, where new network services canbe introduced and sustained.

Several authors have applied economical concepts in the development of algorithms for mobile andwireless networks . Service providers consider factors like simplicity, scalability and the feasibility ofimplementation, in the selection of a pricing schema . On the other hand, users demand pricing andcharging schemes more adaptable and accurate.

In this way, the users behavior can be regulated and the network management is significantly im-proved.

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2 An overview of Prior Work

Lot of work has been done on dynamic pricing of call rates taking different scenarios into consideration.A major part of the research in [1],[4],[5] has been to create a pricing scheme taking only one networkprovider into consideration.

The paper [5] by Dat Vo and Matthew Sorell presents a good approach on network provider which

offers subscribers a continuous non-interrupting service by altering QoS of the call depending upon thenetwork congestion. It states the idea of Willing to Pay(WTP) and Willing to Bid(WTB) method toimprove current QoS.

If the subscriber is willing to pay higher, the QoS would be increased to 1 quantum or if he can bidto particular value then according to that the best QoS will be provided. When a subscriber is admittedto the network, a timer for the guaranteed period for the admission QoS and price is set. When thistimer expires, if the overall load of the cell exceeds allowed load threshold (Lcit), degradation in QoS ofthe call will take place. This applies to all active subscribers whose guaranteed QoS period has expired.The reduced QoS level will be calculated based on three factors: the number of active subscriberswho are still in the guaranteed period, the thresholdLcit, and the number of active subscribers whoseguaranteedQoSperiod has expired. Active subscribers whose guaranteed period has expired can bid upfor an increase in QoS for their call. This behavior reflects the characteristics of thecurrent user group

in the cell.Reference [4] proposes scheme which employs dynamic pricing during all network conditions. Itworks as follows. The CAC component continuously monitors the amount of available bandwidth (i.e.,unutilized bandwidth). When the amount of available bandwidth changes due to call completion or newadmitted calls, the CAC component then computes the optimal arrival rate for each QoS class in order tomaximize the utilization of the new available bandwidth in the system and achieve certain fairness levelsbetween QoS classes. The actual arrival rates for the QoS classes are, however, different from the optimalrates determined by the CAC component. In this case, the dynamic pricing component dynamicallydetermines the prices of units of bandwidth for each class based on the users demands in order to forcethe actual arrival rates to be less than or equal to the optimal ones. The dynamic prices are computedindependently from the optimal arrival rates, hence, simplifying the implementation of our scheme andproviding the network operators the flexibility to use any CAC and users demands functions withoutaffecting the computation of prices.Described scheme is able to maximize the utilization of Broadband

Wireless Access Systems (BWASs) when these networks are underutilized as well as preventing congestionwhen they are over utilized. Moreover, scheme supports multiple QoS classes with calls having multiplebandwidth requirements, which makes it more suitable for BWASs.

Reference [1] considers both (service providers and users satisfaction) by using a flexible QoS basedpricing approach for heterogeneous wireless access networks. It includes a selection mechanism thatallows users to choose the wireless access network that best adapt to their needs, and preserves theQoS level during the time the service is being used. Additionally, QoSDPA algorithm defines a dynamicpricing strategy that is applied when the QoS level is degraded. This strategy attempts to maintain ahigh level of users satisfaction, while maximizing the profits obtained by service providers.

In [6], a pricing policy for multiple competing ISPs (Internet Service Providers) using a threat strategyis presented. Ref. [7] shows a proposal in which authors use game theory to analyze the impact in thecost based on the economic interests of a wireless access point owner and his/her paying client. In Ref. [8]

several problems for resource allocation and base-station assignment in CDMA (Code division multipleaccess) networks are studied.But our area of concern is an oligopoly market having multiple network providers serving multiple

users. Reference [3] proposes the above mentioned scenario. It takes into consideration two levels ofservice: Voice service and Data service. They show that making the decision of pricing unchangeablefor voice service after the initial setting will lead to malicious use of the network by users. Similarlykeeping the option of price change at later change can lead to malicious network provider strategies.Using a non-cooperative game model, they proposed differentiated pricing strategies for the WSPs andusers. They also propose an optimal strategy for access network selection by the service providerswhichminimizes the effect on QoS of the existing users and increases self co-existence. They showed that inorder to achieve Nash equilibrium, it is in the best interest of both WSPsand users to adhere to theproposed strategies.

But the problem with their strategy is that they have made the decision of creating sub-games as

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static i.e. we need to decide before itself as to how frequently the pricing decision needs to be made.There is no adaptable solution that is provided to it. Besides this, there is no mention as to how canwe approximate the competitor network providers pricing decision. They have also assumed that everyprovider shall provide the same service and this shows that every provider shall end up putting the sameprice by the end in order to avoid losing the game.

We would like to incorporate, dynamic way of deciding as to how long a user must be given assured

service without interruption. We would also approximate the contending providers pricing strategy whiletaking the pricing decision.

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3 Scope of our work

Users are willing to play a more central role by modeling and reshaping the service experiences upontheir needs. As mentioned, our model is user-centric which improves the service offering profitable,value-added services faster and cheaper than ever before. In this scenario we assume that end-users havemore freedom connecting dynamically to any WSP for any service through their multi-mode devices and

can disconnect at anytime, which means they do not have any strong relationship with service providers.So WSPs and end-user only interact on a per-service or per-session basis. From a users perspective, theobjective is to select the WSP that best adapts to his/her needs. From WSP perspective the objectiveis to define dynamic pricing strategies for each service and the requested QoS level; this is important toconsider because if prices are low, WSP would attract too many users leading to degradation in perfor-mance due to resource sharing. At the same time, if advertised prices are too high, it will lead to usersturning down that WSP.

For the analysis we have considered that only voice service is provided by the network serviceproviders. Every service provider offers the information of call rates per bandwidth, but the call ratesper bandwidth would also depends on how much time the user wants the assured quality of service. Therate obviously shall be more if user bids it for larger time. The network service provider shall decide thischange on call rates for different time period of assured service on the basis of congestion in the network.

Development in wireless access technologies and sophisticated personal user devices are driving theway towards a heterogeneous wireless access network (HWAN) environment which will enable anytimeand anywhere communication.This new market will promote the generation of a multitude of wirelessservice providers (WSPs), which can use a mix of wireless access technologies to provide these new ser-vices to end-users with competitive prices. HWAN will change the traditional vision of service modelfrom network-centric to user-centric.Unlike traditional service model where all management functions arecontrolled according to the service providers perspectives, the usercentric vision for the future considersend-users in a heterogeneous wireless network environment where they will be free to shop around notonly for the service they need, but also for the available wireless access network which meets their currentservice needs. Users will take advantage of this competition scenario and they will always connect tothe network that can best satisfy their needs and preferences for the current application using novelalgorithms to select the optimal access network in an intelligent way according to their needs. In thissituation, heterogeneous wireless networks look for the market evolution from traditional monopolies

that dictate usage conditions, to a user-centric service-oriented environment. However, within the cur-rent service pricing and provisioning model, there is still a strong relationship between end-users witha single wireless service provider. End-users get services from one provider for a period of time basedon the contractual agreement. Prices for the various services are based on the charging model for voiceservices (i.e. free, flat rate or any of their variations). In the flat-rate model price are fixed and donot fluctuate according to changes in network conditions. The price is paid monthly for the usage ofa certain facility. However, the current demands of quality of services into wireless service access havedemonstrated the need for more sophisticated pricing strategies .We want to present a QoS-based dy-namic pricing approach for service provisioning in a heterogeneous wireless network environment whererequirements of new services demand efficient and flexible pricing strategies and charging mechanisms.We want to propose an adaptable scheme to changeable environments, which satisfies users demandswhile maximizing service providers revenue. We want the users to have the flexibility of even deciding

the time for which the quality of service is assured.

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4 Problem Statement

In our problem statement we have determined following input parameters to our system to calculate theoutput value for concerned network service provider. We assume that every network service providerwould be provided with certain input parameters (number of users in each network) to precise come upwith most optimal output.

Input parameters:

Number of network providers N,

Number of existing users M,

Congestion of concerned network at time t,

Number of users in each network.

For each user we investigate the following information:

if he is using any network

amount of bandwidth consumed by user time period of promised service usage

call rate per bandwidth

Output:

Call rate per bandwidth of concerned network.

It is a sequential system which uses previous output value as one of the current input value to calculatethe current output. The call rates shall be decided using a game theoretic approach. We shall fix on apricing scheme which is the maximum price that our concerned service provider can impose and still endup as the winner of the game involving other service providers.

The game consists of N service providers such that each has a particular strategy Si and a utilityfunction U. The utility function takes into consideration both the Service Providers revenue along withthe cost of effort or resources to get that revenue. So it is his net utility. But a very important concernwith our suggested system is that in any case the user must also be having the maximum utility so thatit sticks to our Service Provider.

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5 Problem Addressing

To come up a solution to the problem we would use a non-co-operative game theoretic approach. Sowe would like to define terms like utility function for the user, revenue for the network provider, usersatisfaction, change in quality of service as per the equation in [3]

US(b) = 1

1 + e

bmax bmin2

b

(1)

where US(b) is the user satisfaction perceived for bandwidth b,bmin is the minimum bandwidthrequired to maintain the service and bmaxis the maximum bandwidth above which the user perceives nosignificant improvement in the QoS.

Similarly, we can define user utility, US(b)= user satisfaction price paid Service provider utility =price paid by the user.

QoS affect ratio is the ratio of bandwidth allocated Ba under a network to the total bandwidthcapacity B of the network denoted as from [3],

Qx=

Ba

B (2)

Now the challenge will be to find the sub-games involving users and network providers followingnon-co-operative strategy so that Nash equilibrium could be established between them. For applyinggame theory we referred [2]. So we suggest the following approach for our solution:

5.1 Game Theory Approach

While the Quality of Service (QoS) offered to users may be enhanced through innovative protocolsand new technologies, future trends should take into account the efficiency of resource allocation andnetwork/terminal cooperation as well. Game theory techniques have widely been applied to variousengineering design problems in which the action of one component has impact on (and perhaps conflictswith) that of any other component. Therefore, game formulations are used, and a stable solution for

the players is obtained through the concept of equilibrium. Two types of games are distinguished: innon-cooperative games, each player selects strategies without coordination with others. On the otherhand, in a cooperative game, the players cooperatively try to come to an agreement, and the playershave a choice to bargain with each other so that they can gain maximum benefit, which is higher thanwhat they could have obtained by playing the game without cooperation. Our concerned game is clearlya Non-Cooperative Game.

Nash Equilibrium: In game theory, the nash equilibrium is a solution concept of a game involving twoor more players, in which no player has anything to gain by changing only his own strategy unilaterally.If each player has chosen a strategy and no player can benefit by changing his strategy while the otherplayers keep theirs unchanged, then the current set of strategy choices and the corresponding payoffsconstitute a nash equilibrium. So our goal is to reach to this Nash Equilibrium.

Mixed Strategy: When a player makes a decision, he can use either a pure or a mixed strategy. If theactions of the player are deterministic, he is considered to use a pure strategy. If probability distributionsare defined to describe the actions of the player, a mixed strategy is used. We need to work for thisscheme as the oppositions strategy is not known.

Repeated games: In strategic or static games, the players make their decisions simultaneously at thebeginning of the game. On the contrary, the model of an extensive game defines the possible ordersof the events. The players can make decisions during the game and they can react to other playersdecisions. Extensive games can be finite or infinite. A class of extensive games is repeated games, inwhich a game is played numerous times and the players can observe the outcome of the previous gamebefore attending the next repetition. Our game is divided into numerous sub games. So, repeated gamesis clearly required. Even if we make profit in the current move, its not assured in the next move.

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Clearly we apply our strategy only in the Transport Layer for:Call Admission Control, Load Control,Cell Selection. But we would be concerned only with Call Admission Control and Load Control. Thereare two games that we care about: Provider Vs Provider which can result in just one winner andCustomer Vs Provider in which we are more concerned in the comparison of utility achieved by theuser when compared to unavailability of our proposed system. The proposed game is non-cooperativebecause, on the one hand, the service providers wish to maximize their revenue and, on the other hand,

the users wish to maximize the quality of service received, keeping at the same time the expenses aslow as possible. Since these two goals are obviously contradictory, the players do not have the slightestmotivation to cooperate. The game is also nonzero-sum, since an increase in one players payoff does notimply a decrease in the other players payoff.

5.2 Challenges faced

Assumption of rationality Game theory is founded on the hypothesis that each player plays ra-tionally and thus seeks his best interest in a rational manner. When dealing with nodes or terminalshowever this behavior cannot be always guaranteed.

Assumption of willingness to cooperate In cooperative games it is assumed that players will

collaborate in order to maximize their profits. A significant problem is that players sometimes choose tobehave selfishly or even cheat in order to optimize their own profit. For this reason, in certain occasions,incentive mechanisms for cooperation, as well as disincentives against cheating need to be formulated.

Choice of utility functions/ payoff calculation This is unquestionably the most challenging partof a game-theoretic framework, since the utility function interprets the players perception of performanceand satisfaction. Utility functions also show the trade-offs the player is willing to make, usually betweenacquiring more resources and saving money.

Not guaranteed existence of equilibrium In game-theoretic formulations an analysis is often re-quired to check if they reach a nash equilibrium. Even if an equilibrium is reached however, the existenceof multiple equilibria is not always excluded. In such case the most efficient and stable one has to be

sought.

Cooperation Incentives: In several cases, optimization issues require collaboration among nodes.Nevertheless, cooperation cannot be taken for granted; even though in most cases players do obtain theoptimal result by sharing resources with others, in certain cases it is not clear enough for them whythey should not act selfishly or even not try to cheat. Nodes exhibiting such behavior are termed selfishand malicious correspondingly. The basic idea for node punishment is that nodes should be rewardedor penalized based on their behavior. Nodes that offer resources should be aided. On the other hand,selfish nodes should be gradually isolated from the network.

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6 Evaluation Matrix

In this section we compare dynamic pricing versus static pricing from the perspective of utility obtainedby the users and the network providers. Then the improvement in user satisfaction due to guaranteedtime period for bandwidth demanded.

Our approach specifies three policy classes: access, negotiation, and pricing and charging. The main

objective of the policies is to satisfy users service demands by firstly, maximizing the provided QoS level;and secondly, when the QoS level cannot be provided, by applying policies with fairer tariffs attemptingto recover the users satisfaction level as soon as possible. These measures should transcend in a set ofbenefits for the service providers.

Utility Function: The category of service parameters refers to information of service requirements; thecategory of network parameters is related to the current network conditions; and finally, the last categoryof users preferences represents relevant values for some factors as price and QoS level. Users may specifythe importance or weights of each parameter which sum to 1, i.e., if the price is more important than thelevel of quality of service, then the user might define the values for price and QoS level as 60 % and 40% respectively. Service parameters used in the utility function is mostly bandwidth. But we would alsolike to incorporate other parameters like congestion, delay, jitter, error rate etc. The system is clearlycapable of including other such service parameters.

Our entire result shall be based on the utility obtained through our proposed scheme. The net utilityobtained by the users must be maximum. Even the concerned provider must be the most profitableprovider amongst the concerned competitors. For showing that the user has maximum utility, we wouldtry to come up with a formidable proof when its compared to static pricing scheme.

For the multiple network environment, we would like to show through simulation that the concernedservice provider always wins. If we come up with a optimum solution then we can even guarantee ourresult even without any simulations.

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References

[1] A QoS-based dynamic pricing approach for services provisioning in heterogeneous wireless accessnetworks by Antonio Guerrero-Ibez, Juan Contreras-Castillo, Antoni Barba , Anglica ReyesDigital Object Identifier: 10.1109/IE.2010.49 Publication Year: 2010 Page(s): 231 - 236

[2] A survey on game theory applications in wireless networks by Dimitris E. Charilas, Athanasios D.PanagopoulosComputer Networks Volume 54, Issue 18, 20 December 2010, Pages 3421 - 3430

[3] Dynamic pricing for service provisioning and network selection in by heterogeneous networks by S.Sengupta, S. Ananda, M. Chatterjee , R. ChandramouliaPhysical Communication Volume 2, Issues 12, MarchJune 2009, Pages 138 - 150

[4] Efficient Bandwidth Management in Broadband Wireless Access Systems Using CAC-based DynamicPricing by Bader Al-Manthari, Nidal Nasser, Najah Abu Ali, Hossam HassaneinLocal Computer Networks, 2008. LCN 2008. 33rd IEEE Conference on Digital Object Identifier:10.1109/LCN.2008.4664208 Publication Year: 2008 , Page(s): 484 - 491

[5] Signaling Requirements for Smart Dynamic Pricing in Cellular Networks by Dat Vo and Matthew

SorellCommunications and Networking in China, 2006. ChinaCom 06. First International Conference onDigital Object Identifier: 10.1109/CHINACOM.2006.344750 Publication Year: 2006 , Page(s): 1 - 5

[6] Economics of network pricing with multiple ISPs by S. Shakkottai, R. SrikantProc. of IEEE INFOCOM, vol. 1, 2005, pp. 184-194.

[7] J. Musacchio, J. Walrand, WiFi access point pricing as a dynamic game,IEEE/ACM Transactions on Networking 14 (2) (2006) 289-301

[8] S.C.M. Lee, J.W.J. Jiang, J.C.S. Lui, C. Dah-Ming, Interplay of ISPs: distributed resource allocationand revenue maximization, in:Proc. 26th IEEE International Conference on Distributed Computing Systems, vol. 19 issue 2, 2006,

pp. 204 -218

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