Adriano Fiorese
email: fiorese@dei.uc.pt

Paulo Simões
email: psimoes@dei.uc.pt

Fernando Boavida
email: boavida@dei.uc.pt

On the Dimensioning of an Aggregation Service for P2P Service Overlay Networks

Keywords: Services Management, P2P, Service Aggregation

An aggregation service (AgS) is a P2P overlay-tier whose purpose is to aggregate and optimize the searching of services and service components maintained by service providers in a P2P Service Overlay Network (SON). AgS dimensioning takes into account the AgS size, in order to allow it to adequately perform the searching when compared with P2P SON native searching mechanisms. Suitable AgS dimensioning helps service providers to plan their infrastructures and services, allowing them to keep costs under control. This paper presents an assessment of the dimensioning of an AgS overlay. The assessment takes into account the searching response time as metric in both the environments: 1) P2P SON and 2) AgS. The assessment also takes into account the AgS own maintenance overhead, in order to compare it with the searching response time in the P2P SON. The simulation results show that, on average, AgSs whose sizes are lesser than 90% of the P2P SON size present better searching efficiency than the same searching operations in P2P SON.

Introduction

Service providers use the Internet connecting "fabric" to generate revenue, offering and operating a large variety of services. Particular services might be a composite of several intermediary services, which, in turn, are operated by third party service or service-component providers. Nevertheless, these services and service components need to be searched, grouped, composed and provisioned, in order to offer the final user a complete service.

In this scenario, service providers face problems in the reachability of the services they provide. A possible manner to cope with this problem is the organization of the service providers into a Service Overlay Network (SON) [START_REF] Tran | Service overlay network capacity adaptation for profit maximization[END_REF]. This approach leads to added coverage, which allows service providers to target bigger markets and, at the same time, share infrastructural costs.

Even though the services are made available by the SON, the need for service search optimization remains. To cope with this, an Aggregation Service (AgS) was proposed by the authors in [START_REF] Fiorese | Service searching based on P2P aggregation[END_REF] [START_REF] Fiorese | An aggregation scheme for the optimisation of service search in Peer-to-Peer overlays[END_REF]. AgS optimizes service and service components searching in a multi-domain environment composed of multiple service providers organized into a common P2P SON. In essence, AgS is a second P2P overlay-tier that executes on top of the P2P SON, aggregating the published services and, thus, making search processes faster and more efficient. Peers that belong to service providers and that are also part of the P2P SON constitute the AgS service. These peers play the role of aggregators and are responsible for the search optimization.

In this context, an open issue is to determine the AgS size, i.e., the number of peers making up the AgS service in order to improve the search efficiency and performance, when compared to the native searching mechanisms provided by the underlying P2P SON.

This paper addresses the problem of the AgS service dimensioning. Having in mind the stated goal and approach, this paper is organized as follows. Section 2 discusses related work. Section 3 briefly describes the AgS service. Subsequently, Section 4 describes in detail the simulated scenarios, presents the simulation results and discusses them. Finally, Section 5 summarizes the findings and discusses guidelines for further work.

Related Work

The AgS service and, consequently, its dimensioning, spans different areas in the field of network and distributed systems, briefly identified below.

Network and Services Management

Some indirect contributions from the network and services management area are relevant for the optimization of search services and their dimensioning issues.

Management by Policies is used to enhance services management [START_REF] Lymberopoulos | An adaptive police-based framework for network services management[END_REF]. Work in this area addresses managing performance service level agreements between internal service providers in a network through the enforcement of policy levels. However, these approaches depend on a series of agreements, adaptation and trust to be realized in cross-domain environments, which suggests the use of an appropriate Service Overlay Network (SON) to take care of this.

Currently, web services are the most developed approach to network and services management [START_REF] Vianna | Investigating web services composition applied to network management[END_REF]. Also, they are the most popular solution for offering service interfaces and service composition [START_REF] Dustdar | A survey on web services composition[END_REF], on which the Service Oriented Architecture (SOA) lays on [START_REF] Auer | SOA as an effective tool for the flexible management of increased service heterogeneity in converged enterprise networks[END_REF]. Therefore, the searching of web services is a recurrent challenge. Works in this area comprises how to select and represent information about web services, as well as ways to overcome the limitations of the single centralized Universal Description, Discovery and Integration (UDDI) repository. Among others, proposes in this area includes searching web services by their operations based on the similarity of the desired operation [START_REF] Dong | Similarity search for web services[END_REF].

Peer-to-Peer

P2P networks are generally classified into two categories: 1) unstructured and 2) structured. These terms relate to the topology of the P2P overlay network. When the topology is tightly controlled and content is placed at specific peers rather than at randomly chosen peers, the P2P network is said to be structured. Generally, this is accomplished using a Distributed Hash Table (DHT) as the core of the P2P network. Some examples of structured P2P overlay networks are: CHORD [START_REF] Stoica | Chord: a scalable peer-to-peer lookup protocol for internet applications[END_REF], Content Addressable Network (CAN) [START_REF] Ratnasamy | A scalable Content-Addressable network[END_REF] and Chamaleon [START_REF] Brown | Chameleon: an adaptable 2-tier variable hop overlay[END_REF]. If the topology is not tightly coupled, which means the peers join the network according to some loose rules, then the network is classified as unstructured. In this kind of P2P networks there is not a coupling between topology and the location of data. Instead, peers form a random graph in a flat or hierarchical manner. Generally, in this kind of P2P network, peers use some kind of flooding to send queries (searches) to other peers, with a limited scope. Some examples of seminal unstructured P2P overlay networks are Gnutella [START_REF] Ripeanu | Mapping the gnutella network[END_REF] and FreeNet [START_REF] Clarke | Freenet: a distributed anonymous information storage and retrieval system[END_REF]. Reference [START_REF] Lua | A survey and comparison of peer-to-peer overlay network schemes[END_REF] presents an extended discussion and comparison of structured and unstructured P2P overlay networks. Meshkova et. al. [START_REF] Meshkova | A survey on resource discovery mechanisms, peer-to-peer and service discovery frameworks[END_REF] presents a survey of the discovery techniques used in some P2P overlay networks as well as in other types of networks.

P2P overlay networks are significantly used as supporting-tier application. In addition to the traditional file sharing applications, resource discovery is commonly executed by these overlays. Michel et. al. [START_REF] Michel | Discovering and exploiting keyword and attribute-value co-occurrences to improve P2P routing indices[END_REF] proposed the exploitation of keywords and attribute-values co-occurrences for the improvement of keyword-based searching in P2P. An intelligent resource discovery mechanism based on weaving attributes into indexes, using locality sensitive hashing and performing searches based on the geographic location of the indexes in a structured P2P overlay is presented in [START_REF] Shen | A P2P-based intelligent resource discovery mechanism in internet-based distributed systems[END_REF].

Search enhancement by combining Grid and P2P was proposed in [START_REF] Brocco | Proactive information caching for efficient resource discovery in a self-structured grid[END_REF][START_REF] Trunfio | Peer-to-Peer resource discovery in grids: Models and systems[END_REF]. Some ideas on this topic concern the use of routing indexes and mechanisms to easily spread them through the Grid; the utilization of bio-inspired algorithms in order to achieve overlay self-organization and selective search flooding, exploiting particular conditions on local caches, is proposed. P2P used in the searching of web services has been addressed by several authors. For instance, approaches using semantics for web services searching are well studied [START_REF] Banaei-Kashani | WSPDS: web services peer-topeer discovery service[END_REF][START_REF] Bianchini | Service-based semantic search in P2P systems[END_REF]. All these proposals claim that the P2P approach has some advantages for the service discovery process, when compared to centralized approaches, such as UDDI.

Service Overlay Networks

A Service Overlay Network (SON), is a virtualized network composed of interconnected nodes, whose generic purpose is to provide the required Quality of Service (QoS) to applications that execute on those nodes [START_REF] Tran | Service overlay network capacity adaptation for profit maximization[END_REF]. A difference between a SON and a P2P overlay network is that the latter regards providing efficient searching and retrieval. This difference is claimed in [START_REF] Tran | Service overlay network capacity adaptation for profit maximization[END_REF]. The formation of the SON does not require its own communication infrastructure. Hence, the problem of bandwidth provisioning in a SON composed of nodes that lease links from different link providers is studied in [START_REF] Duan | Service overlay networks: SLAs, QoS, and bandwidth provisioning[END_REF].

A P2P overlay network can also provide QoS services. We claim this can be accomplished when the participants are in a consortium of service providers that establish well-defined SLAs to regulate the contribution of each participant to the network. In this sense, these particular P2P overlay networks can be considered SON. This idea is based on the work of Zhou et. al. [START_REF] Zhou | ALASA: when service overlay networks meet Peer-to-Peer networks[END_REF]. They proposed a SON platform called ALASA. This platform uses a structured P2P overlay network on the Internet to describe, discover, compose, and repute services.

Taking this into account, this work will use a particular P2P overlay in order to assume a SON among several particular service providers belonging to different network domains.

Lavinal et. al. [START_REF] Lavinal | A next-generation service overlay architecture[END_REF] also uses P2P as support for the SON architecture. In that piece of work the authors also address the discovery of services, although they consider QoS aspects in their approach whilst we take into account performance aspects.

Services Searching using Aggregation Service

The Aggregation Service (AgS) is an unstructured P2P overlay, meaning there is no tight coupling between overlay topology and information location/placement. It executes on top of a P2P SON created by service providers. AgS is composed of peers that belong to these service providers, interested in advertising their services.

The purpose of the AgS service is to aggregate service and service components. This is accomplished by concentrating the service offerings in the AgS peers (nodes), in order to facilitate and optimize the search process. The architectural design of AgS is depicted in Fig. 1.

AgS consists of a P2P overlay without coupling between its logical ring topology and the exact location of the aggregated services. Fig. 1 also shows the SON peers belonging to particular administrative domains announcing (publish) their services and service components to the aggregation peers.

The AgS service operates according to the model depicted in Fig.

Fig. 2. Aggregation Service Model

Each SON peer plays a double role. They execute the services and, in order to optimize the services searching, they also publish references to the available services in several AgS peers. A single service offering can be spread over multiple AgS peers in order to allow some redundancy and to overcome churn. The SON peers make the services indirectly available (through interfaces encapsulated in a service profile) to external entities (such as service composers and aggregators) located in the same or other network domains.

As service providers are customers as well, they can act as third party consumers of service components of other service providers. Nonetheless, to accomplish this step, first of all, the service or service component needs to be searched and found. According to the AgS model, the customer, which can be a third party service provider, uses an AgS peer to accomplish this task. The AgS service performs the search and makes the result available to the customer. Searching for a service by means of the AgS framework results in a set of references to SON peers that offer an interface to the services that match the search criteria. This preserves the internal details of the service, since the external entity is only granted with a mediated access (by means of the SON peer), which may hide sensitive information and filter undesired operations.

The two-tiered AgS architecture enables the splitting of publish and search functionality from the services and service components management functions to be carried out in the P2P SON-tier. Thus, sensitive information and configuration of the services (e.g. the existing internal service provider management services, topologies, etc.) can be protected by only making available (publishing) a previously selected set of interfaces for services and service components.

The AgS working is based on a number of operations. Table 1 presents its key operations and the corresponding messages exchanged among peers.

Table 1. AgS Table of Operations and Messages

Operation Goal

Executor Message sent Join Form the Aggregation Service. aggregation peer JoinMessage sent by the requesting peer to its successor and predecessor in the overlay.

Leave

Leave the Aggregation Service (in a normal way).

aggregation peer

LeaveMessage sent by the requesting peer to its successor and predecessor in the overlay.

Query

Look for a peer that provides a particular service/service component.

aggregation peer

QueryMessage sent by the requesting peer to its successor in the overlay ring in a clockwise manner. The message is forwarded clockwise until it arrives at its goal or until the message reaches the requesting peer. When the desired information is found, a QueryReply message containing it, is then created. This latter message is directly transmitted to the requesting peer of the Query´s operation.

Publish

Make the services to be searched available.

SON peer PublishMessage sent by the SON peer to its aggregation peer(s), which makes the service(s) public.

Dimensioning the AgS

In order to dimension the AgS service, several simulations were performed in order to determine the search response time as a function of the number of nodes in the AgS layer. The response time is the time elapsed since a Query Message is sent from the requesting AgS peer until the reception of the corresponding Query Reply Message. By determining the response time for several sizes of the P2P SON and several sizes of the AgS overlay, it is possible to decide on the number of peers that should form the AgS overlay in order to obtain a certain search performance.

Definitions

Let's consider a set P of service providers that create a consortium to provide services to a large-scale community over a multi-domain environment. In order to do this, they create a P2P SON, in which the available SON peers are responsible for providing the services. In this case let |P n | be the number of peers a service provider p n makes available as SON peers. Thus, e SON is the search efficiency in the P2P SON using internal, native searching mechanisms, which is inversely proportional to the response time, i.e., e SON = 1/rt. On the other hand, e AgS is the search efficiency in the aggregation service. However, in this case, rather than taking only into account the search performance, e AgS must take also into account the AgS overlay set-up and maintenance performance, to which we collectively refer as overhead. Hence, e AgS = 1/(rt + ovhd), where rt is the response time and ovhd is the overhead. This overhead results from the time spent by the AgS overlay in performing control operations. Each control operation, i.e. join (j), publish (h) and leave (l), takes a varying amount of time, which depends on the size of the exchanged messages and on the underlay bandwidth and latency. Thus, the objective of the AgS service is stated in equation ( 1).

( max|AgS| ) e AgS ≥ e SON (1)

Methodology

Fifteen hundred individual simulations were performed, involving a sample of thirty particular P2P SONs with different sizes, starting with 100 peers and going up to 3000 peers, at 100-peer steps.

Each particular SON executes, makes available and publishes its services, spreading them over 10 different domains. For the sake of simplicity, a particular SON peer can only publish, at most, seven services or service components, randomly chosen (using a uniform distribution) from the service set S={S1,S2,S3,S4,S5,S6,S7}. Each SON peer can only publish its service subset on, at most, 10 distinct, randomly chosen, aggregation peers (also following a uniform distribution). In the interests of simplification, the search concludes with the first match, though AgS has the ability to return all matches.

For each simulated P2P SON, four particular AgS overlays running on top of it were set up. Each one of these AgS overlays was composed of a percentage of the peers that form the P2P SON. These percentages were 10%, 50%, 80% and 90%.

Each execution simulated 50 hours of work. Each simulation performed 1,000 search operations, and they were repeated 10 times in order to get the averaged result. First, the P2P SON environment was simulated, executing the query operations. After that, each AgS overlay was simulated, over the previous simulated SON P2P, executing the same number of query operations. A configuration file with the query operations discrete-times was used to feed the simulations. Thus, the execution of the operations followed a pre-defined temporal sequence that was kept the same for all simulations.

In order to optimize the search process, caching of the search results was also taken into account. This means that when a Query Message found the desired information then a Query Reply Message containing that information was sent to the originating aggregation peer, which stored the information in its local cache. In a future query for the same service or service component started by aggregation peers located before the mentioned one in the ring, the search would then get fewer hops due to the cache hit, thus improving the search efficiency. The PeerFactSim.KOM [START_REF] Kovacevic | Benchmarking platform for peer-to-peer systems[END_REF] discrete events simulator was used in all simulations.

Results

The simulations primary result is the average response time (RT) of the search operations. When a search operation starts, the corresponding Query Message receives a time stamp (TS). Each peer along the search path forwards the Query Message in the case the service is absent from its local cache. When there is a cache hit, the time stamp of the Query Message is copied into the Query Reply Message. On reception of this message, the initiating peer can calculate the elapsed time. RT can then be calculated as the ratio between the accumulated time for all successfully accomplished search operations and the number of search messages, according to equation [START_REF] Bianchini | Service-based semantic search in P2P systems[END_REF].

As already mentioned the AgS efficiency, which is expressed in (4), also depends on the overhead time. The overhead results from the time spent in setting up and maintaining the AgS overlay, by way of join (j), publish (p) and leave (l) control operations, according to equation [START_REF] Banaei-Kashani | WSPDS: web services peer-topeer discovery service[END_REF].

ovhd = nj ∑ m=1 time(j m ) + np ∑ m=1 time(p m ) + n l ∑ m=1 time(l m ) (2) rt = query msgs ∑ m=1 (CurrentT ime -T S m ) QueryReplyM essages (3)
e AgS = 1

(rt + ovhd) (4)
Fig. 3 depicts the results concerning the response time and overhead for the simulated scenarios. It is worth mentioning the results rely on a confidence interval of 95%.

It is possible to notice that the AgS service is very efficient since, for the majority of results, AgSs whose size is up to 90% of the P2P SON size still lead to faster searches (even with overhead) than the plain P2P SON. Moreover, as one can see, the smaller the AgS overlay, the better. This can be explained by the high concentration of services in these relatively few peers, as is the case of AgSs with 10% of the P2P SON peers.

For some P2P SON and respective AgS sizes, a particular behavior can be observed. Especially in the cases of small P2P SONs (the ones whose size is smaller than 600 peers), one can observe that the search time (efficiency) of the AgS is worse than the search time for the P2P SON. All in all, the observation of this behavior in these conditions allows us to conclude that for small market niches, where service providers create small P2P SON, for the sake of searching, the AgS must not be greater than 80% of the P2P SON.

The influence of the overhead is stronger in the smaller P2P SON and respective AgSs. As services are equally heavily concentrated in the P2P SON and in the AgS overlay, the searches are fast. Thus, even a small AgS overhead has negative effects on the searching efficiency. The influence of the overhead can be seen in more detail in Fig. 4. Fig. 4.(b) shows that even when 90% of the SON peers are part of the AgS overlay, AgS not only searches faster than P2P SON but also the entire e AgS (i.e., search plus overhead) is greater than the e SON . On the other hand, Fig. 4.(a) shows situations for which e AgS < e SON . These cases highlight that, for AgS sizes starting at 80% (as a matter of fact, with less than 80%) of the P2P SON size, the overhead is responsible for degrading the AgS efficiency.

Nevertheless, it is worth mentioning that when the overhead is dismissed, e AgS is always greater than e SON . Finally, Fig. 5 shows the histogram for the arithmetic mean values for all experiments. The average response time in the P2P SON is approximately 487 ms whereas for the AgS service it is approximately 77, 270, 403 and 448 ms for AgS sizes of 10%, 50%, 80% and 90% of the P2P SON size, respectively. The overhead is quite similar for all AgS sizes, with a value close to 47 ms. Looking at Fig. 5 it is possible to conclude that the AgS service can lead to very good performance gains relative to the plain P2P SON approach. In addition, the average overhead remains stable even with the increase in the number of aggregation peers. All in all, according the obtained results it is possible to claim that the AgS approach is highly beneficial and gives service providers ample freedom to decide on the number of 2nd-level peers without jeopardizing the performance gain.

Conclusions

The Aggregation Service under evaluation in this paper aims at improving the service search efficiency in large-scale, multi-provider peer-to-peer service overlay networks. When the number of peer increases, search operations can be performed more efficiently if a second-tier overlay is established, comprising special peers that maintain information of the various services available in and published by the peers belonging to the first tier.

Specifically, this paper addressed the issue of determining the relation between the number of peers in the Aggregation Service and the efficiency of service search operations. The results, obtained by simulation, clearly show that the proposed service has very good potential to improve the overall search performance when compared to the realization of search operations in a single-tier P2P overlay, at the cost of a very small overhead. The obtained results can easily be used by cooperating ISPs in order to dimension the Aggregation Service overlay.

Further work will address optimization of search operations, data consistency assurance, and robustness of the aggregation overlay.

Fig. 1 .

 1 Fig. 1. Aggregation Service Architecture

  Thus, |SON | = ∑ |P | n=1 |P n |, and SON = {p | p ∈ P n ∈ P }. On the other hand, the AgS overlay is constituted by the subset of the SON peers. Hence, in principle, |AgS| ≤ |SON |. Let's define e as the search efficiency, which is given by the response time metric.

Fig. 3 .

 3 Fig. 3. AgS efficiency with overhead

Fig. 4 .Fig. 5 .

 45 Fig. 4. AgS efficiency x P2P SON size

⋆ This work was partially funded by Portuguese Foundation for Science and Technology (grant SFRH/BD/45683/2008).


Joon-Myung Kang

James Won-Ki Hong
email: jwkhong@postech.ac.kr

Autonomic Management for Personalized Handover Decisions in Heterogeneous Wireless Networks

Keywords:

The computation of good and optimal handover decisions is a significant problem in a heterogeneous network environment. This is exacerbated when the goal is to provide personalized services for mobile users as opposed to generic device metrics such as received signal strength. Personalized handover decisions should not only consider received signal strength, but also context information, user preferences, and other non-functional requirements. In this paper, we propose a novel autonomic management method for personalized handover decisions to satisfy end users' demands in heterogeneous wireless networks. We define two objective metrics for evaluating specific access points: access point acceptance value and access point satisfaction value.

Introduction

Growth in ubiquitous and mobile computing systems has led to the early introduction of a wide variety of new access networks and Internet-capable devices [?]. As a result, multiple heterogeneous wireless access networks can be used at the same place and a mobile device can access them simultaneously [?]. A foreseen feature of these networks and mobile devices is the support of flexible and personalized handover decisions by dedicated devices to satisfy the demands of the end user using context information [?,?].

The current handover decision methods based on Received Signal Strength (RSS) or pre-defined simple policies do not provide good solutions because they do not take into account services that satisfy the preferences of a user at a given time, location, and/or application context. Therefore, handover decisions should be based on additional considerations, such as the capacity of each network link, usage charge of each network connection, power consumption of each network interface, battery status of the mobile device, and user preferences. We call these and similar data context information.

In this paper, we propose an autonomic handover decision method for satisfying the end user's demand for different types of services in heterogeneous wireless networks by using fuzzy logic and utility functions as part of the decision-making process. We call this method AUHO, which is an abbreviation for AUtonomic HandOver . We define two objective metrics for evaluating the performance of Access Points (APs): a) Access Point Acceptance Value (APAV) and b) Access Point Satisfaction Value (APSV). Our algorithm supports the selection of the best AP (horizontal handover) as well as the best access network (vertical handover) using current user preferences and profile data, application requirements, and context information. The novelty of our approach is in using a combination of functional and non-functional requirements, filtered by the particular context.

Related Work

We present previous approaches for handover decision strategies proposed in the literature. We divide these studies into six categories based on the metrics or techniques used for handover decisions [?]: 1) RSS-based, 2) cost function-based, 3) user-centric, 4) Artificial Intelligence (AI)-based, 5) Multiple-Criteria Decision Making (MCDM), and 6) context-aware approaches. In summary, compared to our AUHO approach, none of these approaches provide sufficient flexibility in satisfying user needs, and most cannot adapt to a changing context. Additional details on each algorithm are provided in the following sub-sections.

RSS-based approaches cannot be applied to vertical handover decisions because of different characteristics of the heterogeneous wireless networks. Cost function-based approaches use a pre-defined cost function which is a measurement of the benefit obtained by handing over to a particular network [?]. Among the different criteria that a vertical handover decision takes into account, user preferences such as cost and QoS, are the most interesting policy parameter for a user-centric strategy [?]. The handover decision problem selects among a limited number of candidate networks from various service providers and technologies with respect to different criteria. This is a typical MCDM problem, which deals with choosing from a set of alternatives which are characterized by their attributes [?]. The concepts of Fuzzy Logic (FL), Neural Networks (NN), Expert Systems, and Genetic Algorithms (GA) from AI can be used to decide when handover should occur and which network to should be chosen among different available access networks [?]. The context-aware handover concept is based on knowledge of the context information of the mobile terminal and networks in order to make intelligent and better decisions [?]. Our comparative study shows different issues related to the handover decision problem: network performance, user satisfaction, flexibility, efficiency, and multi-criteria solution [?].

Proposed Approach

Our research hypothesis is that our AUHO algorithm always maximizes end user satisfaction by computing the optimal handover decision for different types of mobile services in heterogeneous wireless networks based on user preferences. Our approach is as follows. First, we define and categorize context information for handover decisions by surveying available information from mobile devices, networks, applications, and users. Second, we construct an information model to represent different data models of mobile devices, access networks, applications, policies, users, and contexts, because data from each of these entities come from different sources and are defined using different languages. Third, we develop a decision making algorithm by evaluating each access network using a weighted combination of context information, user preferences, and service requirements. Note that the weighting enables the decision method to be adjusted to better suit the needs of the end user. We define how to measure and evaluate the quality of each AP and then calculate the end user satisfaction for achieving our hypothesis. We define the concept of an "acceptance value" using a fuzzy logicbased classifier to process all relevant context information, regardless of whether different languages and formats are used. We then define the concept of a "satisfaction value" using a utility function based on user preferences. We then select the "best satisfying" AP for supporting Context-aware Always-Best-Satisfying (CABS) mobility based on a utility function that maximizes user preferences. Fourth, we evaluate the performance of the proposed method using a network simulator that we developed for testing handover decisions in heterogeneous wireless networks. Finally, we compare our method with other decision making algorithms, and show that our algorithm supports a CABS service, which other methods do not support.

We defined two objective metrics for handover decisions: an APAV and an APSV. The former represents the suitability of a particular AP for an end user based on a given set of user preferences. The latter represents how well a particular AP satisfies the needs of the end user based on his or her user profile, as used in this context. We calculate the APAVs and APSVs for all candidate APs, and determine the AP that best satisfies the current application and context requirements. If the new candidate AP is the same as the old AP, no handover is performed. In addition, if the APSV of the new AP is higher than that of the current AP, we must consider handover overheads such as latency. Otherwise, handover to the new AP is performed. This process is continuously repeated within a pre-defined timeout that is defined by profiling. This is a our algorithm's feedback control loop to achieve autonomic management. After connecting the best satisfying AP, we repeat a maintenance loop by evaluating the current connected AP. If a connected AP exists, the network selection task is stopped. We then calculate the APAVs of the current AP and calculate the APSV based on them. If the APSV of the current AP is lower than the pre-defined threshold, the handover decision task is started again and all candidate APs are evaluated to select the best satisfying AP.

Currently, we have evaluated our AUHO with two case studies: a) the same application using different user profiles, and b) different applications using the same user profile. The former tests different weighting factors for each user preference, while the latter tests different application requirements for the same user profile. We showed that our AUHO provides the best satisfying AP compared to other handover decision making algorithms [?].

Conclusions

Seamless mobility and roaming in next-generation networks is an important issue. In particular, a handover should support not only Always-Best-Connect mobility, but also Always-Best-Satisfying (ABS) mobility for providing personalized mobile services. In this paper, we have proposed novel handover decision method for supporting ABS mobility based on the end user's preferences and context information. Our method determines the access network and the AP that can best satisfy the requirements of the end user for a particular context. We showed how autonomic management can be used for handover decisions.

For future work, we will optimize our algorithm to calculate APAVs and APSVs. We will find the optimized timeout value for periodic decisions. Finally, we will perform more tests and optimize by considering handover overhead and network performance.

This research was supported by the WCU program through National Research Foundation of Korea funded by the Ministry of Education, Science and Technology (R31-2010-000-10100-0) and by the IT R&D program of MKE/KEIT. (KI003594 , Novel Study on Highly Manageable Network and Service Architecture for New Generation)


Ricardo De O Schmidt
email: r.schmidt@utwente.nl

Aiko Pras
email: a.pras@utwente.nl

Estimating bandwidth requirements using flow-level measurements

Bandwidth provisioning is used to dimension links so that a desired performance is met within a network (e.g. QoS metrics). Practical approaches for the estimation of bandwidth needs rely on rough traffic measurements overlooking traffic fluctuations that may have a negative impact on the network performance. On the other hand, effective theoretical solutions for estimating bandwidth needs require traffic measurements at very small timescales, which limit their applicability due to, for example, transmission rates. This paper describes our research goal of proposing a reliable and scalable solution for estimating bandwidth requirements by means of flow-level traffic measurements, as well as our proposed approach to achieve it. This research, currently in its initial phase, is planned to result in a Ph.D. thesis at the end of four years.

Introduction and motivation

Link dimensioning plays an important role in network management, for example to support QoS provisioning. Usually, network operators make manual provisioning decisions based on empirical rules and on rough traffic measurements, like by using the Simple Network Management Protocol (SNMP) [START_REF] Schonwalder | Simple Network Management Protocol (SNMP) Context EngineID Discovery[END_REF], and the Multi Router Traffic Grapher (MRTG) [START_REF]MRTG: Multi Router Traffic Grapher[END_REF] for graphics, in timescales of 5 minutes. However, as proved in [START_REF] Meent | Traffic Measurements for Link Dimensioning, A Case of Study[END_REF], traffic measurements in large timescales (e.g. interval of minutes) completely overlook traffic peaks that are detected in smaller timescales (e.g. intervals of milliseconds). This leads to imprecise estimations of bandwidth needs and, consequently, it results in non-optimal network performance. Both underdimensioning and overdimensioning of links can impact negatively in the network performance. The former can result in traffic bottlenecks caused by undetected traffic peaks, and the latter in waste of link resources when allocating more bandwidth than necessary. Some of the solutions proposed so far for network dimensioning are application specific and, consequently, have reduced applicability scope. Solutions like [START_REF] Fraleigh | Provisioning Internet Backbone Networks to Support Latency Sensitive Applications[END_REF] and [START_REF] Papagiannaki | Provisioning IP Backbone Networks Based on Measurements[END_REF] are focused on delay sensitive applications (e.g., VoIP) in backbone links, where the delay is resulted from queuing. By means of flow-level measurements, [START_REF] Bonald | Dimensioning high speed IP access networks[END_REF] and [START_REF] Barakat | Modeling Internet backbone traffic at the flow level[END_REF] focus on, respectively, dimensioning of TCP-connections throughput rates and congestion control. A more general approach, i.e. applicable not only for backbone traffic, is proposed in [START_REF] Meent | Network Link Dimensioning -a measurement & modeling based approach[END_REF] and [START_REF] Pras | Dimensioning Network Links: A New Look at Equivalent Bandwidth[END_REF]. One of the main contributions of this work is the dimensioning formula [START_REF] Schonwalder | Simple Network Management Protocol (SNMP) Context EngineID Discovery[END_REF]. It is used for calculating the minimal bandwidth requirements C for a given link, in a time scale of length T and with an acceptable error rate of ε, by adding to the average traffic rate µ a safety margin, which strongly depends on the traffic variance ν (T). The assumption of this formula is that the measured traffic at timescale T is Gaussian.

C(T, ε) = µ + 1 T (-2 log ε) • ν(T ) (1)
This formula was extensively validated and proved to be a good solution for the problem of link dimensioning. However, the drawback of this approach is that it requires packet-level traffic measurements, which may drastically reduce its applicability in high-speed networks (e.g., Gigabit Ethernet). Considering traffic loads currently observed in the Internet, packet-level measurements could often suffer from scalability issues. In addition, it may demand very specific, powerful and expensive hardware/software for traffic monitoring.

Given the increasing employment of flow-level measurements on network management and operations, e.g. Cisco IOS routers with embedded NetFlow [10], we believe it to be a promising approach for our research problem. Considering current transmission rates, the use of flow-level traffic measurement tools (e.g., NetFlow and IPFIX [START_REF] Quittek | Requirements for IP Flow Information Export (IPFIX)[END_REF]) would allow us to come up with a scalable solution when working with aggregated traffic information. Therefore, this paper describes our research idea towards the proposal of a solution for reliably estimating bandwidth requirements without recurring to packet-level traffic measurements.

The remaining of this paper is organized as follows. Section 2 presents our goal and research questions, as well as the proposed approach. The planned process to validate our findings is described in Section 3. Finally, Section 4 concludes the paper.

Goal, research questions and approach

The goal of our research is to determine how to estimate bandwidth requirements by means of flow-level traffic measurements. In order to pursue this goal, we have defined the following research questions as the base of our research:

What is the state of the art in flow-based bandwidth provisioning? 2. Which relevant data extracted from flow records can be used to estimate bandwidth needs? 3. Which statistical models describe the traffic measured at the flow-level?

Research question 1 aims to raise information about main contributions and trends in the area of our research. To do so, we plan an exhaustive literature review. Interviews with network operators can also be considered as a possible way for obtaining information from real-world situations.

With research question 2, we want to identify what information (e.g., metrics) is relevant to our problem and how to extract it from the flow records. For instance, we aim to find out how to determine average traffic rate and variance from flow measurements in order to further apply these values in the dimensioning formula (1), as presented in section 1. It is important to remark that, as presented by [START_REF] Meent | Network Link Dimensioning -a measurement & modeling based approach[END_REF], determining traffic variance from packet-level measurements (with smaller timescales) is not a complex task, due to the high level of information granularity, which allows for a high accuracy. However, the same task can be considered as a challenge in flow-level measurements, where traffic information is summarized into flow records and measurements occur at coarser timescales. In order to answer research question 2, we will use the knowledge acquired from research question 1 and further analysis on flow-level measurements of real-traffic.

In research question 3, we aim at identifying which traffic models fit better the traffic measured at the flow-level. More specifically, we want to investigate whether the measured traffic is Gaussian or not, in order to determine the suitability of the dimensioning formula from [START_REF] Meent | Network Link Dimensioning -a measurement & modeling based approach[END_REF] for estimating bandwidth needs from flow-level traffic measurements. Once the traffic model(s) is identified, it will allow us to parameterize metrics such as average traffic rates and peaks (see [START_REF] Meent | Burstiness predictions based on rough network traffic measurements[END_REF]). We plan to use real-world network traces in order to identify such models.

Validation

In order to validate our final proposal, we intend to evaluate its correctness by analyzing real-world traffic, from different sources like GÉANT [START_REF] Géant | [END_REF] and SURFnet [14]. We plan to compare the results from our solution with results obtained from the dimensioning formula in [START_REF] Meent | Network Link Dimensioning -a measurement & modeling based approach[END_REF] with packet-level measurements. In addition, experimentations with real equipment and real-world traffic measurements will be done in the laboratory.

Final considerations

This paper discusses the possibility of estimating network bandwidth requirements by means of flow-level traffic measurements. We have stated our motivation and planned means for achieving our goal. The long-term research goal, as described in this paper, is to be achieved in the period of 4 years, as a Ph.D. research. The research reported in this paper is supported by the SURFnet [14] and the FP7 ICT UniverSelf [START_REF]UniverSelf: UniverSelf FP7 project number 257513[END_REF] (#257513).


Yuqian Song
email: yuqians@cs.tcd.ie

Owen Conlan
email: owen.conlan@cs.tcd.ie

A Novel Approach to Support the Visual Exploration and Management of Heterogeneous Home Area Network Devices and Services

Keywords: Home Area Network, HAN, network monitoring, domain ontology, semantic visualization, semantic annotation, network management

With the advance of home networking technologies, people now enjoy increased communications convenience whilst occasionally suffering annoyance from trivial issues. The automated application of appropriate expertise can support ordinary users in managing and diagnosing their own network, despite being unfamiliar with the basic technologies. This presents a significant opportunity to increase user satisfaction and reduce support costs. As a part of my PhD research, this paper proposes an expert knowledge derived approach to manage, diagnose and resolve network problems in real time through a user-friendly visual interface derived from semantically annotated heterogeneous raw network monitoring data. This approach has been implemented in an early prototype -Home Area Network Monitoring System (HANMS). A detailed description of the current stage of this research and future work is addressed in this paper.

Introduction

As ICT (Information and communications technology) complexity increases, inexperienced users are continuously challenged and perplexed with even simple management and maintenance tasks. In a Home Area Network (HAN) scenario, normal HAN users are often puzzled by trivial wireless network issues. For example: a laptop suddenly suffers a quality degradation of the network connection, perhaps caused by a neighbor initiating some WiFi activity in the same channel -causing interference, or a signal weakens when the antenna of the wireless device is obstructed, or the laptop user may upload a large file which causes network congestion. Such events seem unremarkable but will impact on the end-user's perception of the quality of the service provided by the wireless network connection. Moreover, most users will not have the technical expertise to diagnose these problems in real time. Conversely, if the user could easily diagnose and solve these (often simple) problems, this would avoid unnecessary contact with support staff and reduce the operating costs for both the network provider and HAN users. To address this challenge, a novel approach is proposed to bridge the gap between the hard-learned knowledge of domain experts and the cognitive competencies of normal users in a HAN context. This approach aims to achieve the following objectives: to semantically enrich and correlate raw network log data from heterogeneous HAN devices and services with domain knowledge; to detect and analyze anomalies through a real-time diagnosis loop monitoring the constantly evolving HAN devices and services; to appropriately present the highly abstracted, correlated and annotated monitoring data for normal HAN users in a user-friendly visual interface; and finally, to leverage the high-level annotated monitoring data to perform high-level (but groundable) management actions. Related research already addressing some of these objectives will be briefly reviewed in the Related Work section. The Solution Overview & Evaluation section presents an early prototype of the Home Area Network Monitoring System (HANMS), followed by preliminary evaluation results. Future work and conclusions are described in the last section.

Related Work

One problem in the rapidly evolving smart home network management area is how to deal with the vast number of dynamic network log datasets. Driven by Semantic Web technologies, Semantic Annotation can be applied to transform and enrich the data and information with formal semantic meanings. In Handschuh and Staab's book [START_REF] Handschuh | Annotation for the semantic web[END_REF], a broad range of technologies and methods is introduced for the explicit construction of semantic annotations, including approaches for data extraction, collaboration and translation into Semantic Web metadata. In particular, goal-oriented data extraction is valuable for network log data annotation, especially for monitoring and management purposes. By facing the challenges of evolution and heterogeneity of HAN technologies and varying user requirements in the complex HAN environment, Brennan et al. [START_REF] Brennan | Policy-based integration of multiprovider digital home services[END_REF] presented a policy-based federated service management architecture that addresses these concerns for digital home devices participating in end-to-end communications services, while Jennings et al. [START_REF] Jennings | Towards autonomic management of communications networks[END_REF] proposed an autonomic network management scheme, which allows a networked system to self-govern its behavior within the constraints of the human-specified goals. Another effective strategy is to perform network management by involving ontology-based modeling and reasoning. Hoag [START_REF] Hoag | Semantic Reasoning for Adaptive Management of Telecommunications Networks[END_REF] presented an approach to apply semantic reasoning techniques for network management and resource allocation to avoid overbuilding and improve quality.

Visualization is an effective strategy to align complex data by capitalizing on several human perception capabilities, and a novel interactive visualization system [START_REF] Chetty | Who' s Hogging The Bandwidth: The Consequences Of Revealing The Invisible In The Home[END_REF] was proposed as an approach to monitor the data collected from a home router and control bandwidth usage for family users. An interactive network management system, called Eden [START_REF] Yang | Eden: supporting home network management through interactive visual tools[END_REF], delivered a simple conceptual model to help users understand key aspects of the network by eliminating the barrier and cost of the network technical minutia. Our research is also based on Conlan et al.'s work [START_REF] Conlan | Towards Non-expert Users Monitoring Networks and Services through Semantically Enhanced Visualizations[END_REF] to express a semantically enriched visualization approch for non-expert users to monitor networks and services. Our work intends to combine the advantages of these research to propose a novel approach for HAN monitoing and management.

Fig. 1. HAN Monitoring and Management

In this figure, the data annotation process is implemented in a layered annotation engine to enhance large amounts of heterogeneous data with multi-level discrete semantic encodings according to the embedded network expertise to support the highlevel processing. In the current HANMS prototype, the raw wireless log data are gathered from diverse wireless devices in a HAN test bed; expert domain knowledge is modeled into domain ontologies; and the annotation is applied in a two-levelannotation process. Through the Event/Fault Analysis Engine, real-time semantically enhanced data is observed in an analysis and diagnosis loop according to correlated expertise, in HANMS, modeled in network event ontologies. Using HANMS visual widgets (Figure 2), detected events/faults, and network information, is automatically and intelligently presented and highlighted. Management requests will be expressed through high-level management actions and these actions will be decomposited into lower level actions [START_REF] Keeney | Towards the use of Policy Decomposition for Composite Service Management by Non-expert Endusers[END_REF] to directly affect or control HAN services and devices according to expert defined ontologies and rules. The management decomposition is not supported by HANMS at this stage.

Fig. 2. HANMS prototype screenshot

The current HANMS prototype has been deployed and initial evaluation was carried out with two target groups: normal HAN users and domain experts. The majority of feedback (more than 70%) was positive, especially in the usability and UI design whilst identified limitations will be addressed in the future development.

Future Work and Conclusions

The current HANMS prototype is still under active development. For high-level network management, multi-resource HAN monitoring merits further development. As a part of the FAME project, HANMS has the potential to integrate with a number of prototype systems, e.g. smart home composite UPnP services. By consuming the knowledge models of UPnP events and faults [START_REF] Brennan | Policy-based integration of multiprovider digital home services[END_REF], HANMS could present a more effective and efficient approach to diagnose the real time event/fault with high complexity. Based on this integration, high-level management goals can be decomposed into enforceable UPnP actions to control low-level devices and services. According to the up-to-date HANMS prototype deployment and evaluation results, this approach will assist ordinary end users to diagnose and resolve potential problems in a HAN, which presents a significant opportunity to reduce support costs and increase user satisfaction. This work will also show that it is possible to alleviate the difficulties of non-expert users in HAN scenarios by combining Semantic Web and Visualization technologies, while capturing and integrating domain experts' insights.

Acknowledgments

This work is partly funded by Science Foundation Ireland via grant 08/SRC/I1403 -Federated, Autonomic Management of End-to-End Communications Services (FAME).

Solution Overview and Evaluation

This section describes a novel approach for HAN monitoring and management (Figure 1) established in our prototype, HANMS [START_REF] Song | An Ontology-driven Approach to support Wireless Network Monitoring for Home Area Networks[END_REF].


Javier Baliosian
email: javierba@fing.edu.uy

Joan Serrat
email: serrat@tsc.upc.edu

Matias Richart

Juan Saavedra

Mariela Borba

Jose Luis Melus

Policy-based Pricing for Heterogeneous Wireless Access Networks

Our cities are already covered by a myriad of diverse wireless access networks. The most ubiquitous access networks are the well organized homogeneous and centralized operator-based cellular networks that sustain their business model on a captive client basis. However, a new billing paradigm is rising, where a client can choose to connect to the provider that best comply with his/her current requirements and context. Inside this paradigm, this paper presents a distributed, rule-based pricing strategy aimed to improve the quality of service and to increase the global income of a service provider. The performance and reliability of the rule-based decisions is supported by a Finite State Transducers-based inference machine specially designed to manage networking systems. We show, with simulations, that, using our strategy, the operators can make the new billing paradigm profitable while the clients benefit from the economic advantages of competition and of the quality given by a pricing-based network balance mechanism.

Introduction

Our cities are already covered by a myriad of diverse wireless access networks. The most ubiquitous are the well organized homogeneous and centralized operator-based cellular networks that base their business on a captive client model. However, a more heterogeneous and free network of WiFi, WiMAX and 3G access-providers has appeared and it is starting to challenge the dominant billing paradigm. In this new world, clients can choose the best or the cheapest provider for the time and place they are trying to connect from, setting the grounds of a future free market of Internet connectivityproviders. In this context, pricing will have a fundamental role. Using the right pricing strategy, an operator will try to obtain the highest possible revenue while the users will try to get a service that fits their requirements at the minimum possible price. As stated in [START_REF] Niyato | Competitive Pricing in Heterogeneous Wireless Access Networks: Issues and Approaches[END_REF] "From an economic point of view, pricing plays an important role in trading any resource or service. The most important objective of trading is to provide benefits to both the sellers and the buyers. Therefore, the price must be chosen so that the revenue of the sellers is maximized while the highest satisfaction is achieved by the buyers. There are two main factors influencing the price setting, namely, user demand and competition among service providers. Price and demand are functions of each other." Following these ideas, if demand is high the service provider can charge a high price in order to earn more. However, if demand is low, the price must be reduced to attract more mobile users. Competition between service providers also impacts on the price of the service. Typically, if the services are substitutable (even though different), users buy a service that provides the highest satisfaction at the lowest price. As explained by Courcoubetis and Weber in [START_REF] Courcoubetis | Pricing Communication Networks: Economics, Technology and Modelling[END_REF] the communication services that we are considering in this work can be considered simply as means for the transport of data with a given quality, characterized as a certain error rate, delay and jitter. Obviously, the network access providers will want to recover their investment charging a price for their services, but that is not the only reason for which pricing is important. The price of simple goods it is often determined by only one parameter such as the number of copies, their weight or the length of a lease. However, communication services are specified by several parameters such as peak rate, average bandwidth or loss rate. Moreover, multimedia service contracts are specified with additional parameters such as tolerance to bursts and adaptability to network changes. Since connectivity services can be specified in terms of so many variables, the number of different possible contracts is enormous and complicates the design of a reasonable and coherent pricing strategy. On the other hand, contracts are more than simple price agreements. For example, a contract may be an incentive for the user to produce traffic conforming to the agreed parameters. This, at time, will impact positively on the service quality and the price paid by the clients in general. All this motivates an effort to develop a pricing technique simple enough to be implemented by the operators, but, at the same time, sophisticated enough to compete successfully with other strategies and work as a scalable feedback mechanism to control how the network is used. A provider can reduce the price of a service during off-peak hours to incentive the use of idle resources or charge an extra price to a user that exceeds the agreed traffic. Additionally, pricing may be seen as an alternative to TCP for congestion control. Thus, in a similar manner to TCP and its signaling, a higher price may induce users to reduce the packet transmission rate or to stop it completely. However, to be useful as a congestion-control mechanism, a pricing technique must be very dynamic. It should be able to change the price of a particular service in real time and in a particular region of the network, for example in a particular access point that is suffering of congestion.

This paper presents a distributed, rule-based pricing system that implements exactly the same intuitive ideas in the shape of policy-rules to be enforced on the price charged by each provider. Those rules are aimed to improve the quality of service and to increase the global income of a service provider in that world in which users are free to choose every time they connect. The paper is organized as follows: in 2 we introduce the rationale of our solution and and overview of its architecture. In section 2.2 and 2.3 we present the set of rules governing our system and the kind of conflicts that arise using them. In 3 we present details of the system design and the manner in which policy conflicts are solved. Finally, in 4 we depict a simulation-based evaluation and we conclude and present some future work in 5.

A Policy-based Pricing Solution

The rationale behind the proposal of this paper is twofold: first, it tries to offer an autonomic, scalable pricing system that provides the operators with simple and businessunderstandable means to set prices. In the examples presented in this paper, we follow the principles of demand and competition mentioned above. Second, our proposal is aimed to demonstrate that the process of solving the conflicts between the pricing policies constitutes an optimization procedure that is capable of, for example, balance an access network using the price of the access service as an incentive-based tool to drive the users choices. The following sections describe the overall scenario and the design of the proposed system.

Overall System Architecture

We envisage scenarios where three types of actors coexist on a geographical area: users, providers and a regulator (see Figure 1). Users are persons in possession of some wireless-capable device that are willing to establish a connection to the Internet with some quality of service requirements and at the minimum possible cost. As we envision it, users are not attached to a provider by a contract. Instead, they pay for the provider's service on demand, by, for example, a credit card or pre-paid means, as it is common in current cellular 3G services. In our model, in front of equal or similar services, users will always choose the services of less expensive provider. The providers have an access network conformed for a set of access devices that we will call generically Access Points (AP). A provider's objective is to sell access services to users while maximizing their revenue. The third entity, the regulator, is a neutral entity, probably played by a governmental agency in a real setup, with the objective of enforcing the sharing of pricing information between the providers to allow pure market competition.

In our scenario, there are two different providers offering their connectivity services through a network of APs over the overlapping geographical regions. This means that at any point of this region, a user has network coverage from one or more APs of each provider. In the particular world we are envisioning, each AP has its own price for a connectivity service of a given bandwidth and duration. Those prices may vary from one AP to another even if they are operated by the same provider and from time to time. In this manner, an AP in a popular part of the city may have a higher price than another in a neighborhood with few users or the same AP can diminish the service price during the low-usage parts of the day. The mobile user's terminal can connect to any of those APs and run an agent that is able to communicate with an AP and get information about the current price for a certain service the AP provides (later we will discuss about the technical solutions for this issue). Once it has the price from different APs, the agent connects to the AP with the lowest price, with or without human assistance (another possible criteria will be discussed bellow in 5). The established connection will last for the contracted time or until the connection is lost, for example, because the user moved out of the AP's coverage region. For this paper, the manner in which the regulator collects and shares price information between APs is not relevant but it may be made with a standard publish-subscribe mechanism with some integrity and no-repudiation guaranties.

Governing Policies

Policies are intended to allow each AP to decide which is the most advantageous price for its own connectivity service, having into account its context, user's demand and potential competitors. These decisions are made by a Policy Decision Point [START_REF] Moore | Policy Core Information Model -Version 1 Specification[END_REF] installed in the same AP, independently from the others regardless whether they belong to the same or competing providers, following a set of policy-rules modeling the economic criteria of demand and competition mentioned in 1. It is worthy to mention that these sets of policies can be freely established by each provider according to best practices founded on past experience, forecasts based on economic models, etc and that by no means are imposed by the regulator. Even more, a provider could participate without making use of pricing policies, just fixing a flat rate for its services. Nevertheless, for the sake of illustrating the approach, and also to allow a quantitative evaluation we present hereafter specific sets of policies; one concerning demand and the other competition. The set of rules driving the decisions regarding demand is presented in Table 1. The rationale behind this set of rules is simple; the price of the service is increased, kept constant or decreased depending on the number of users served and its gradient of change. In this way, the price will be adapted to stimulate or inhibit service demand and its adaptation rate will track the evolution of such demand. In practice we accomplish it by classifying the number of users in three categories (few, mid and lots), the gradient of change in two (slow and fast) and also allowing two rates of price change (slow and fast). On the other hand, the set of rules facing the competition is presented in Table 2. Here the global objective is to accommodate the price to the evolution of the competitors to avoid users' migration. In particular we have classified the price of the competition in two categories (lower and higher) and considered two adaptation rates (slow and fast). In this context, a competitor is an individual AP and two APs are competitors between them when their coverage regions overlap and they belong to different providers.

Conflicting Policies

Conflicts, or logic contradictions, are an intrinsic phenomenon of the policy-based management. The process to solve those conflicts is the way that policy-based network management has so as to optimize the configuration of the services and devices of a communications network.

In the particular case of the this work, conflicts arise mainly because the diversity of objectives of our system. For example, lets consider the following simple -and quite intuitive-version of the rules presented above:

Rule 1: if few users then decrease price slow Rule 2: if competitor price higher then increase price fast Rule 1 models a pricing policy based on demand, and Rule 2 models a pricing policy based on competition. Although in a simple manner, they are the most patent case in which our previous set of rules may present a conflict: which action has the system to trigger if there are a small number of users connected to an AP but the price of the competitor is higher than the local price? The process that our system uses to decide in front of this kind of situations is described bellow in 3.1.

A Policy-based System Design

Our solution combines centralized, hierarchical and fully-distributed management structures to address different challenges with the most appropriate approach. Our basic de-Fig. 2. Architecture of a Node sign criteria is to maximize the distribution of tasks over the nodes as much as possible. Only the tasks that inherently require a centralized organization, such as global optimizations, or those tasks which perform better on a weakly-distributed structure, are carried out using hierarchical structures. Management tasks such as local optimizations follow a fully distributed approach. The system can be seen as mainly composed by independent units or nodes that work as peers deployed at each managed network device (a wireless access point). From a high level architectural point of view those nodes follow a classical policy-based architecture constituted by a Policy Decision Point (PDP), a Policy Enforcement Point (PEP) and an agent that monitors the state of the device and the behavior of the network. Policies follow the Event Condition Action (ECA) model, where the condition may be the occurrence of some event (e.g., an alarm or a service request) or certain network state, and the action is the desired response when the condition is true or just it occurs. The monitoring agent of any of the nodes of the network generates notifications, which are the source of events for the PDP of the same or any other node. All the communications between nodes, including control messages, notifications and request for actions are carried out by a content-routed asynchronous communication bus that organizes the nodes in a hierarchical overlay. The communications bus' design is inspired by existent notification services such as Siena [START_REF] Caporuscio | Design and evaluation of a support service for mobile, wireless publish/subscribe applications[END_REF]. Finally, a centralized management station is in charge of the edition, optimization and distribution of rules into the nodes. The reconfiguration actions decided by the PDP may also involve other network elements, in which case the node may be the manager of the activity and delegate responsibility for some actions to other nodes. As it will be described in the next section, policies have to be managed as finite state transducers (FST). The translation from policy rules expressed in natural language into the FSTs implementing those rules is currently made by programmer assisted algorithms presented in [START_REF] Baliosian | Finite State Transducers for Policy Evaluation and Conflict Resolution[END_REF]. Despite the fact of being a software assisted process, it is still a complex and work-intensive task.

The Logic behind the Pricing Management System. Finite State Transducers for Conflict Resolution

To better understand how the FST-based conflict-resolution process works, we will describe it with the help of the rules presented above in 2.3. As stated above, Rule 1 models a pricing policy based on demand, and Rule 2 models a pricing policy based on competition. The FST in Figure 3(a) models both rules at once. In the figure, the TF few users is depicted as fu, the TF competitor price higher as cph, action decrease price slow is depicted as dps and increase price fast as ipf.

For simplicity, we are considering the case in which a single notification arrives, then the PDP makes a decision based on it, and nothing else happens. (This makes the FST simpler because only has to accept strings made of a single symbol). Now, consider the case in which a notification arrives stating that the price of a competitor has changed. At that time the PDP evaluates the FST in Figure 3(a) as follows: starting in the initial state "0" it evaluates the TFs on the input label of each of the state's outgoing edges. This is, the PDP evaluates few users and competitor price higher. In the case that there are few users connected to the local AP and that the price of the competitor is lower or equal to the price charged by the local node, few users is a positive number and competitor price higher a negative number. Therefore, the PDP chooses the upper edge of the FST, which produces decrease price slow as an output, causing local EP to decrease the price slowly. However, in the case that the price of the competitor is higher than the local price, we have two edges going out of state "0" with positive TFs and not a straightforward choice to be made. To solve this problem, before being deployed to the AP's PDP, the FST is determinised and the FST in Figure 3(b) is computed using the algorithm mentioned before. Now, when the PDP evaluates the determinised transducer, at each state it has only one possible edge to follow. Let us assume that, when the PDP evaluates it, the TF few users takes a value of 0.3 and competitor price higher a value of 0.5. We are in the case in which few users is "tauter than" competitor price higher, expressed in Figure 3(b) as f u → λ cph, therefore, the transducer produces a single output, in this case the order to decrease the price charged by the local AP slowly (action decrease price slow).

Evaluation

The simulation platform presented hereafter was designed with the aim to evaluate the viability of the proposed solution and, above all, the viability of the new pricing paradigm. In this section we present the result of two sets of experiments in which we compare the behavior of our proposal against the most popular pricing strategy for WiFi and 3G access networks.

Evaluation Setup

In order to evaluate our solution we simulated an environment with two competing providers offering access services on the same geographical region and with identical networking infrastructure resources. However, one of the providers follows our pricing strategy and the other follows the prevailing fixed-rate pricing structure presented in 2.2. Although it will not happen in a real deployment, to ensure a fair comparison between competitors in our experiments, both providers have identical sets of APs installed on the same coordinates and with identical coverage areas. A large number of clients move along the area covered by both providers choosing to connect to the AP with the lower price for a randomly specified service. To perform the experiments in this section we developed a simple discrete events simulator based on the JavaSimulation library.

For each experiment, each provider has nine APs deployed in a 1200x1200m region. Those APs are distributed in a mesh at approximately 400m of each other (see Figure 4). As said before, APs are by pairs, one owned by the provider following our strategy and the other owned by the competitor provider following a fixed-rate strategy. The price charged by each AP is a random variable that follows a uniform distribution with values between 50 and 60 units. Each AP has an available bandwidth of 3.5 Mbps, and a homogeneous circular coverage area with a radius of 285m. For the experiments, there are 450 mobile users. Each user tries to hire a connectivity service with a random bandwidth of between 50 and 150 Kbps with any of the APs in its connectivity range. These users move inside the square area mentioned above following one of two mobility models. In the first set of experiments we assume an urban environment and use the Manhattan mobility model [START_REF] Etsi | Selection procedures for the choice of radio transmission technologies of the umts[END_REF] to capture the movements of the users. Following this model, each user moves along a mesh of parallel, perpendicular streets placed every 100m. For all the experiments using the Manhattan model, the speed of a terminal varies randomly with a pedestrian mean of 1m/s and a standard deviation of 0.2. At each intersection, a terminal decides whether to turn left, right or keep the direction with a probability of 0.2. The second set of experiments were made to study the balancing capabilities of our strategy assuming that the clients move following the Reference Point Group Mobility (RPGM) model [START_REF] Hong | A Group Mobility Model for Ad Hoc Wireless Networks[END_REF]. The RPGM models a set of clients moving in groups but with some individual freedom to move inside and between groups. Each group has a logic center (group leader) that determines the behavior of the rest of the group. At the beginning of the simulation each client is uniformly distributed in the vicinity of the leader, then, each client moves with random speed and direction. All the experiments run during a simulated time of 120 minutes. The metric used to compare the behavior of both providers is the total amount of money made by each provider (adding the money made by all its APs) within the simulation time.

Evaluation results

Figure 5 shows the price evolution for the 9 APs of the provider implementing our pricing strategy. In this experiment the users move following the Manhattan model. In the figure, it is possible to see how the prices change getting similar to the competitor price but slightly below. This makes the clients to prefer our provider. The higher number of clients connected to APs owned by our provider explains the bigger revenue despite charging lower prices for the same service. The Reference Point Group Mobility left regions of the geography without users and regions with high concentrations of them. This situation makes more visible the price dependence on the number of users trying to connect to an AP. In this experiment, APs 0 and 12 have no users in their coverage regions (see Figure 4), consequently, the price charged by those APs decreases until it reaches the minimum possible cost defined by the provider. The APs 2, 6, and 8 have a small number of users in range, therefore, they moderately reduce the price. Finally, those APs with most clients in their coverage area maintain their price close to the price charged by the competitor provider. This divergence between the prices charges by different APs of the same provider causes that some clients migrate from their closest APs to an AP with a lower price. In this manner, the pricing strategy works as a network balancing tool. We can see that the global earnings made by our provider are also higher than the earnings of the competitor (see Figure 8).

This experiment also shows how rule-based pricing can be used as a congestion control tool. As more users start connecting to a given AP, the price rises following for example Rule 15, naturally pushing some users out to other APs. Finally, an accessory, hard access-control prevents the connection of too many users to an AP in case the pricing mechanism is not enough. The stability of the connections is related with the users' movement and velocity. When a user that is connected to a given AP is informed of the existence of a lower price AP in its current position, it terminates its current connection and tries to connect to the AP with the lower price. However, a high percentage of users are connected along the simulation to any of the AP with the only exceptions of the intervals in which the users are switching from one AP to another.

Conclusions And Future Work

In this work we present a distributed, rule-based pricing system that improves the quality of service -by means of a smart access control and dynamic network balancewhile increasing the global income of a service provider. This pricing mechanism assumes a business model, not yet existing today, where users can freely select any service provider with radio coverage at their location. However, we believe that this work supports the thesis of the plausibility of such a model. The depicted simulations encourage the idea that operators can make the new billing paradigm profitable while the clients benefit from the economic advantages of competition and of the quality given by a pricing-based network balance mechanism. First, a solution must be designed to let the mobile nodes and the access point to communicate the price between the AP and the mobile node, even if there is no established connection between them. There are several possible solutions for this issue, for example, in the case of WiFi APs the price information can be transmitted as part of the AP beacon, we have made experiments that support this solution, however, using this method it is at least difficult to offer different prices to different clients but other solutions may be envisioned. Another issue to address is the stability of the solution. As can be seen in the graphs, even if the dynamic price of the rule-based APs converges to a region near an optimum, it keeps fluctuating above and below it. This issue can be alleviated with a proper study on the functions representing fuzzy concepts such as "decrease price slowly´´. This fine kind of tuning is a whole research issue that will be part of our future work. Finally, another future work issue is the inclusion of additional AP selection criteria such as signal strength, preferred provider or handover reduction.

Fig. 1 .

 1 Fig. 1. Scenario

  (a) TFFST Modeling two Conflicting Pricing Rules (b) TFFST Modeling two Pricing Rules with their Conflicts Solved

Fig. 3 .

 3 Fig. 3. Finite State Transducers for Conflict Resolution

Fig. 4 .

 4 Fig. 4. Position of APs and Users (RPGM Mobility)

Fig. 7 .

 7 Fig. 7. Revenue by Provider. (Manhattan Mobility)

Fig. 8 .

 8 Fig. 8. Revenue by Provider. (RPG Mobility)

Fig. 9 .

 9 Fig. 9. Distribution of BW usage per Provider. (RPGM Mobility)

Table 1 .

 1 Rule 1: if few users and users steady then decrease price slow Rule 2: if few users and users decreasing slow then decrease price slow Rule 3: if few users and users decreasing fast then decrease price fast Rule 4: if few users and users increasing slow then keep price Rule 5: if few users and users increasing fast then keep price Rule 6: if mid users and users steady then increase price slow Rule 7: if mid users and users decreasing slow then decrease price slow Rule 8: if mid users and users decreasing fast then decrease price fast Rule 9: if mid users and users increasing slow then keep price Rule 10: if mid users and users increasing fast then increase price slow Rule 11: if lots users and users steady then keep price Rule 12: if lots users and users decreasing slow then decrease price slow Rule 13: if lots users and users decreasing fast then decrease price fast Rule 14: if lots users and users increasing slow then increase price slow Rule 15: if lots users and users increasing fast then increase price fast Set of rules driving an access point behavior on demand bases Rule 16: if competitor price lower and competitor price decreasing slow then decrease price slow Rule 17: if competitor price lower and competitor price decreasing fast then decrease price fast Rule 18: if competitor price lower and competitor price steady then decrease price fast Rule 19: if competitor price lower and competitor price increasing slow then decrease price slow Rule 20: if competitor price lower and competitor price increasing fast then decrease price fast Rule 21: if competitor price higher and competitor price decreasing fast then decrease price slow Rule 22: if competitor price higher and competitor price steady then increase price slow Rule 23: if competitor price higher and competitor price increasing slow then increase price fast Rule 24: if competitor price higher and competitor price increasing fast then increase price fast

Table 2 .

 2 Set of rules driving an access point behavior on competitors' bases

Acknowledgments

This work has been done in the context of the MICINN project TEC2009-14598-C02-02.