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Economic Model for Cost Effective Network Selection Strategy in Service Oriented Heterogeneous Wireless Network Environment Olga Ormond Student Member IEEE, Gabriel-Miro Muntean and John Murphy Members IEEE
Abstract— This paper describes and formalises the Service Oriented Heterogeneous Wireless Network Environment (SOHWNE), the future service provision and delivery environment that will support ubiquitous user access anywhere at any time from diverse devices to a broad range of services. These services can be offered by third parties and can be accessed via one of many available networks. This paper also proposes and describes a novel algorithm for intelligent cost-oriented and performance-aware selection between available networks. This user-centric strategy focuses on the maximisation of consumer surplus when selecting the best available connection for transferring non real-time data, with user specified time constraints, in a user-centric SOHWNE. Index Terms—Consumer Surplus, Decision-making Strategies, Multi-provider Network Environment, Non real-time Data.
I. INTRODUCTION
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ext generation mobile networks will see the opening up of the telecommunications marketplace into an environment with competitive network and service providers. These providers will offer multiple network access methods to many existing and innovative services for multi-homed handsets, in an effort to maximise their revenues by meeting increased user demand for services available anywhere at any time. They will operate under the careful watch of regulators, who will ensure radio spectrum is used efficiently for maximum public benefit. Section II below depicts a Service Oriented Heterogeneous Wireless Network Environment (SOHWNE), and different stances on the future system structure. Section III presents an overview of the main 4G players, their game plans, challenges and interdependencies. A discussion on multi-access network selection decision research is presented in section IV. Section V introduces a novel user-centric cost effective network selection strategy for transmission of non real-time data and evaluates this against a simplistic always cheapest network selection strategy. Conclusions are drawn in section VI.
Manuscript received August 14, 2005. This work is part funded by a research grant from Enterprise Ireland’s Informatics Research Initiative. O. Ormond and J. Murphy are with the School of Computer Science and Informatics, University College Dublin, Ireland (phone: +353-1-716-1934; fax: +353-1-269-7262; e-mails:
[email protected],
[email protected]). G. Muntean is with the School of Electronic Engineering, Dublin City University, Dublin 9, Ireland (e-mail:
[email protected]).
II. SOHWNE Many factors will influence the shape of the future wireless landscape. Evolution of cellular standards, the increasing popularity of wireless LAN technology and the developments in multi-homed handsets have provided a multitude of different wireless access technologies, each with its own characteristics. A multi-mode terminal in this heterogeneous Radio Access Network (RAN) environment may be exposed to any combination of RANs (GPRS, UMTS, WLAN, WIMAX, etc.). Deregulation, expensive capital investments, and the threat of intense competition from cheaper to setup hotspot operators, have driven traditional cellular network operators to change their business models. Many operators will include different levels of network sharing as part of their plan to survive in the tightly regulated and newly competitive marketplace [1]. Electronic portable devices are being merged into multi-functioning hybrid devices (e.g. MP3-phones). As users grow more technically competent they will expect more from these devices. This together with the popularity of the internet and busy lifestyles is driving a growth in demand for access to online services anywhere at any time. Banking
Browsing
Gaming
Data Access
Shopping
SERVICES Private:
Private:
Public:
Public:
Public:
Public:
Home
Office
GSM
GPRS
UMTS
CDMA
WLAN
WLAN
Public: Hotspot
WLAN
RANS
USER DEVICES Fig. 1. Service Oriented Heterogeneous Wireless Network Environment
The market is evolving from a two player technology-driven environment, where traditional monopolies dictate usage conditions, to a three-player user-centric service-oriented environment. Satisfying the user service expectations by offering a desirable, quality user experience is the key to maximising revenue potential. Network access is being decoupled from service provision, introducing service
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providers as a third party in the wireless arena. The resulting Service Oriented Heterogeneous Wireless Network Environment (SOHWNE) is depicted in fig. 1. It is expected that an extensive collection of novel and attractive services will be produced by an array of service providers [2]. Both access and service providers are under pressure to supply the demand for user perceived quality services at the right price, meeting elevated bandwidth demands and supplying flexible services. These services need to be dynamically adaptable to the current context, such as the user preferences, terminal capabilities, and available network characteristics. There is no doubt that success for next generation networks hinges on the deployment of a wide range of popular services. There are however two conflicting stances on the resulting 4G network structure: the network-centric and the user-centric views. The network-centric vision supports a continuation of today’s scheme with users tied to a single operator subscription. Operators must strive to meet users’ service expectations, but may profit from user tolerance to occasional poor network performance. They aim to maximise revenue and avoid user churn, maintaining tight control over user behaviour to make the most profitable use of their spectrum. The usercentric view is a SOHWNE envisioned as a setting where users and service providers will be free from subscription to any one operator and can instead choose the most suitable transport offering from the available access providers for their terminal and application requirements [4]. This is often referred to as the Always Best Connected (ABC) concept, and it is this viewpoint which is supported by the ANWIRE1 partners [5]. Hotspots in public places, private homes and offices already encourage users with dual-mode terminals to avail of the choice of their subscribed network or another RAN in areas of overlapping coverage. III. THREE KEY PLAYERS A. The Users Spoilt by the broadband networks with flat-rate pricing– users have grown to expect a high quality broad service range available at a low price. Depending on their profile and current context the users’ outlook on the desired service priority, perceived quality, budget requirements, security and other networking concerns will change. A user may be in a café, with time for long downloads, or alternatively rushing down a busy street requiring only minimal data presentation. In common users will seek value for money and have a patience limitation on their willingness-to-wait for service delivery. As part of their 4G survival suite mobile consumers will need inbuilt intelligence in their terminal to aid their decision of which RAN to select for each session call. Users will rely on the adaptation capabilities of the application server and the terminal to dynamically adapt the application session to the available network and terminal resources. Customers are only 1 ANWIRE Academic Network on Wireless Internet Research in Europe. European workgroup which examines 4G concepts and requirements.
willing-to-pay for applications that they deem worthwhile. If a user considers a particular application as expensive they will be discouraged from using it. Users may avoid networks that continually let them down. Preference combinations of service and network metrics depend on many tradeoffs, which will vary for each user. These tradeoffs rely on many dynamics including previous experiences; new and experienced users’ views may differ on perceived quality of service (QoS). B. The Access Network Providers Available radio access networks will be provided by some mix of 2/2.5/3G operators, MVNOs, hotspot providers, and home and company networks. Established operators have a strategic advantage over other access providers in that they have built up consumer profiles and relationships, and have billing systems, authentication, and tight security measures already in place. Also cellular technologies provide for users travelling at speed. As no one network is the best for all applications in all scenarios large operators may look to a number of technologies to enhance their access provision. With the demand for bandwidth intensive services, operators must efficiently use allocated spectrum to cater for capacity needs, while satisfying user cost-performance expectations to avoid loosing customers to competing RANs. Operators close to key user information could leverage this to their advantage in collaborations with service providers. Yet a strong services portfolio is vital in drawing in customers’ expendable income. It is therefore in the best interest of the transport providers to cooperate with the service application and content suppliers. Most research favours technology advancements focused on network providers, improving how they can provide near seamless mobility in this multi-technology multi-network multi-application multi-user environment. Access providers will endevour to predict user usage patterns [6] in an effort to design the best charging and admission schemes. They aim to minimise unnecessary handovers and to maintain a certain level of quality for each customer’s call while attracting user loyalty and willingness-to-pay, maximising their revenue opportunities. They need to balance network pricing and load to maintain satisfactory network performance at an acceptable price to all current and potential users in their network(s) [3]. User behaviour is strongly affected by the charging scheme employed, the QoS on offer and the other access choices open to them. Charging schemes may have to be adapted to different geographical locations in the operator’s domain, especially where large grouping of homogenous users (i.e. users with similar behaviour traits) frequent a specific area. Customers would favour receipt of a single bill for transport, service and content. Charging could be placed in a number of network entities: the access networks, a trusted third party, or in some centralised service provision platform. C. The Service Providers The opening of the service provision market will see the emergence of a number of new service providers all keen to maximise their potential share of the profits, by offering
13666 services which appeal to the users and the ever increasing technical abilities of their terminals. Research in the area of service providers tackles the challenge of offering integrated service management platforms to support the rapid creation, deployment and management of services in the SOHWNE. This is not an easy task given the complexity of catering for many different entities all seeking fast efficient deployment of their applications on multiple networks while also supporting flexible billing and service maintenance schemes, customer registration, configuration and support [2]. There is also a need to advertise new services to end-users. Other research optimises service provider offerings, dynamically adapting applications to variations in individual user preferences, terminal capabilities and the current RAN conditions. While the operators concentrate on offering quality connection establishment, the service providers are concerned with the best service delivery and presentation to their client, the user. IV. RESEARCH APPROACHES TO NETWORK SELECTION DECISIONS Much of the work in RAN selection policy design investigates the access network selection problem as part of the seamless handover venture. Ylitalo et al in [7] look at how to facilitate a user making a network interface selection decision. They concentrate on a possible architecture for the end terminal and not on any particular strategy but do mention an Always Cheapest (AC) network selection strategy. Bircher and Braun [9] propose an agent-based architecture with a user agent decision function. Customers compare and select services with the best performance/price ratio, negotiate with providers and pre-reserve resources for an agreed price. Details of the exact negotiation terms are not covered but they are based on differently weighted QoS parameters. We focus on the user-centric decision problem of which available network to choose for data transfer. Decision metrics and need for decision policy design are outlined by McNair and Zhu [8] in the context of vertical handoff for mobile users running multiple communication sessions. This is relevant to the initial network selection decision for users with a choice of RANs. In [10] H. J. Wang et al describe a handover ‘policy’ for heterogeneous wireless networks, which is used to select the ‘best’ available network and time for handover initiation. They consider the cost of using a particular network to be in terms of the sum of weighted functions of bandwidth, cost and power consumption. Bandwidth is determined either by using an agent in each RAN which estimates and broadcasts current network load, or in the case of commercial networks, by the ‘typical’ value of bandwidth advertised by these networks. The network which is consistently calculated to have lowest cost is chosen. A number of papers, all of which reference [10] use similar cost functions. Chen et al focus on a smart decision model for vertical handoff in [11]. The bandwidth in this case is measured using a probing tool. In the access discovery phase of handover the wireless device seeks information on available access networks. The amount of information supplied should
3 be minimal. Receiving current network condition information may be resource consuming and wasteful in the variable wireless environment. We use previous file delays from a particular network to predict transfer completion time for that network. The HTTP handoff decision model depicted by Kammann et al in [12] and the work in [8] also use a cost function which is used to compare available networks and establish the candidate RAN according to importance weightings associated with different metrics. Utility-based functions are commonly used to describe user preference rating relationship for a number of metrics. X. Wang et al [13] consider user preferences to be represented quantitatively through a utility function when comparing two congestion pricing schemes. In [14] Das et al consider users to choose a pricing plan based on their data delay considerations, described by a user utility function. They use their knowledge of user behaviour to network-centric gain, reducing customer churn and maximizing operator revenue. Network selection decision strategies will be influenced by the pricing scheme employed in the available networks. Le Bodic et al [15] select between networks which use auction based pricing schemes and employ two different strategies based on user preference for low service charge, or for networks with a good reputation. Gazis et al [16] look at the complexity of being ‘Always Best Connected’. The network selection decision is user-centric. It involves identifying the network or network-combination which will best satisfy the current user requirements in their current circumstance. The complexity of the network selection problem is mapped to an NP-hard optimisation problem. V. CS NETWORK SELECTION STRATEGY A. Scenario In our work we endeavour to find an optimal solution to the NP-hard RAN selection decision problem for non real-time data services. In the case of large data transfers, we believe that an initial correct decision may save the user the effort of handoff later in the call session. As stated above this decision will rely heavily on the charging schemes implemented by the candidate networks, as users make every effort to get the best value for their money. This paper considers the scenario where a user wishing to send non real-time data (a file) has a choice of several RANs, each of which employ a fixed price per byte pricing scheme but each charging different prices and subject to different varying background traffic patterns. B. The Proposed Strategy Every user wants timely delivery of their data at the lowest price. It is assumed that every user has a patience limit and will only be willing to wait so long for completed transfer of their data before they become dissatisfied. The more delay a user experiences the less s/he is willing to pay. Consumers want to choose the goods and services that will give them the most satisfaction. In the world of economics utility functions are used to describe this user satisfaction. Users want the best obtainable value for their budget allowance. The difference
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between the value of the data to the user and the actual price charged is known in microeconomic terms as the Consumer Surplus (CS). We propose a utility-based algorithm that accounts for user time constraints, estimates complete file delivery time (for each available network) and selects the most promising access network based on CS difference. We use a piece-wise linear user utility function to describe the relationship between the user’s time and budget limitations for each application. The function shape remains the same for all file sizes but the threshold values change. For the initial threshold values we took into account a large range of possible throughput rates available in existing and emerging RANs and a range of likely file sizes (10 to 200 Kbytes). We decided that delays greater than 30 seconds for 200 Kbyte (or smaller) file transfer breach end-users expectation for good service and therefore pick a bit-rate of 212 kbps as the threshold for user acceptability. Rates higher than 840 kbps are considered the desirable or expected rates for which the user is willing to pay their highest price. The budget was taken as between 0.04 and 0.01 cent per Kbyte for file delivery depending on the transfer completion time, with files arriving after the specified deadline worth 0 cent to the frustrated user. In the case of each available RAN, the user terminal must predict the current offered rate and use those estimated rates, together with the provided utility function for the current application, to select the network that is predicted to yield the greatest CS while meeting the transfer completion time deadline. Further strategy details can be found in [17]. C. Evaluation Against an Always Cheapest Strategy Our intelligent mobile user requires FTP data transfer in a user-centric SOHWNE. A model with two overlapping IEEE 802.11b wireless LANs was developed in Network Simulator version 2.27 [18]. Each WLAN contains a number of associated background traffic generating mobile nodes. Both LANs are connected to a wired network which hosts the sink for all application data. The wired links have high bandwidth such that end-to-end delay is mainly dependent on the performance of the chosen wireless network. During successive tests, the file size is varied from 10 to 200 Kbytes. For each transmission our intelligent terminal located in the overlap area has the option of either of the two WLANs. Two decision strategies are supported: ! Always Cheapest network selection (AC) ! Consumer Surplus network selection (CS) It was found that for CS network selection strategy users the average transfer completion time was significantly lower than for those users employing the AC selection strategy. The proposed CS strategy is highly effective in meeting the delay deadline, while with the AC strategy the user exceeds the completion deadline more often, costing an expense to the frustrated user with no return. Initial results indicate that our CS strategy produces significantly better results for the user.
players in the next generation SOHWNE. The introduction of service providers will boost the number and range of appealing services, while the access providers can concentrate on providing reliable high bandwidth transport. Our ultimate goal is to find the best user-centric network selection strategy for non real-time data transfer in the next generation environment. Our proposed solution is a RAN selection strategy based on maximizing consumer surplus subject to meeting user defined constraints in terms of transfer completion time. This solution is compared to a more simplistic Always Cheapest (AC) strategy. The simulation results presented show that the proposed strategy (CS) achieves superior results in terms of average transfer completion times. In relation to the time deadline of the file transfer, the CS strategy can be an order of magnitude better than the AC strategy. It remains to explore how it compares with other more complex strategies. REFERENCES [1] [2]
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VI. CONCLUSIONS This paper presents an overview of the key 4G market
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