Architecture for user preference-based dynamic service selection in grid infrastructure using mobile...

PROCEEDINGS OF THE 14th

ON WORLD WIDE WEB APPLICATIONS ANNUAL CONFERENCE

7-9 November 2012

Durban South Africa

Editors:

A. Koch P.A. van Brakel

Publisher:

Cape Peninsula University of Technology PO Box 652 Cape Town

8000

Proceedings published at http://www.zaw3.co.za

ISBN: 978-0-620-55590-6

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Proceedings of the 14th Annual Conference on World Wide Web Applications

Durban, 7-9 November 2012 (http://www.zaw3.co.za) ISBN: 978-0-620-55590-6

TO WHOM IT MAY CONCERN

The full papers were refereed by a double-blind reviewing process according to South Africa’s Department of Higher Education and Training (DHET) refereeing standards. Before accepting a paper, authors were to include the corrections as stated by the peer-reviewers. Of the 72 full papers received, 64 were accepted for the Proceedings (acceptance rate: 89%). Papers were reviewed according to the following criteria: Relevancy of the paper to Web-based applications Explanation of the research problem & investigative questions Quality of the literature analysis Appropriateness of the research method(s) Adequacy of the evidence (findings) presented in the paper Technical (e.g. language editing; reference style). The following reviewers took part in the process of evaluating the full papers of the 14th Annual Conference on World Wide Web Applications: Prof RA Botha Department of Business Informatics Nelson Mandela Metropolitan University Port Elizabeth Mr AA Buitendag Department of Business Informatics Tshwane University of Technology Pretoria Prof AJ Bytheway Faculty of Informatics and Design Cape Peninsula University of Technology Cape Town Mr A El-Sobky Consultant 22 Sebwih El-Masry Street Nasr City, Cairo Prof M Herselman Meraka Institute, CSIR Pretoria Mr EL Howe Institute of Development Management Swaziland

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Dr A Koch Department of Cooperative Education Faculty of Business Cape Peninsula University of Technology Cape Town Dr DI Raitt Editor: The Electronic Library (Emerald) London Mr PK Ramdeyal Department of Information and Communication Technology Mangosuthu University of Technology Durban Prof CW Rensleigh Department of Information and Knowledge Management University of Johannesburg Johannesburg Prof A Singh Business School University of KwaZulu-Natal Durban Prof JS van der Walt Department of Business Informatics Tshwane University of Technology Pretoria Prof D van Greunen School of ICT Nelson Mandela Metropolitan University Port Elizabeth Further enquiries: Prof PA van Brakel Conference Chair: Annual Conference on WWW Applications Cape Town +27 21 469 1015 (landline) +27 82 966 0789 (mobile)

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Architecture for user preference-based dynamic service selection in grid infrastructure using mobile devices for SMMEs

S. Manqele

[email protected]

N.Dlodlo [email protected]

Meraka Institute, CSIR Pretoria

South Africa

M. Adigun

[email protected]

S.S. Xulu [email protected]

University of Zululand

KwaDlangezwa South Africa

Abstract Introduction: Grid computing has emerged as an important new field, distinguished from conventional distributed computing by its focus on large-scale resource sharing, innovative applications and in some cases, high performance oriented. In this research a user preference is described as a user’s description of the service that the user intends to utilize to solve his/her problem, and service selection is defined as the process of matching the user preference with the available services in the Grid. Challenge: The usage of grid technology across different service provisioning environments has increased the challenges associated with service selection and discovery. Although having a rich set of terms that can be used to easily express requirements for the desired service, a more detailed and specific user interface would suffice in making it easy for the user to express their requirements using high level constructs. In order to tackle this problem we need mechanisms to represent user preferences and manipulate rich services description of available services in the Grid environment. Solution: This research proposes an architecture for user preference-based dynamic service selection in a grid infrastructure. In the architecture, using mobile devices, a grid user(SMME) makes a request for a service through a service requester. The search for the service is enabled or optimized through an algorithm. The algorithm in use is an optimization of collaborative filtering and content-based algorithms. The optimized algorithm is evaluated on the basis of response time, recall and precision. Conclusion: The approach used handles preferences as soft constraints and semantically understands a request from a grid user.The use of both collaborative filtering and content-based algorithm makes it easy for grid users to get relevant services anytime and anywhere that users request services through mobile devices (e.g. PDA, laptop, cellphone, etc.).

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Keywords: Grid computing, SMMEs, service provision, dynamic service selection, mobile device, web service architecture

1. Introduction

Grid computing has emerged as an important new field, distinguished from conventional distributed computing by its focus on large-scale resource sharing, innovative applications and in some cases, high performance oriented (Foster 2002). In this research a user preference is described as a user’s description of the service that the user intends to utilize for solving their problem, and service selection is defined as the process of matching the user preference with the available services in the grid. The usage of grid technology across different service provisioning environments has increased the challenges associated with service selection and discovery (Prabhakar T and Manikrao U 2006). Although having a rich set of terms that can be used to easily express requirements for the desired service, a more detailed and specific user interface would suffice in making it easy for the user to express their requirements using high level constructs. In order to tackle this problem, we need the mechanisms to represent user preferences and manipulate rich services description of the available services in grid environment (Prabhakar T and Manikrao U 2006). This research proposes an architecture for user preference-based dynamic service selection in a grid infrastructure for small and medium enterprises (SMME). In this architecture, a grid user makes a request for a service through a service requester using mobile devices. The search for the service is enabled or optimized through an algorithm. The algorithm in use is an optimization of collaborative filtering and content-based algorithms. The optimized algorithm is evaluated on the basis of response time, recall and precision. An approach used handles preferences as soft constraints and semantically understands a request from a grid user.The use of both collaborative filtering and content-based algorithm makes it easy for grid users to get relevant services anytime, anywhere as users request services through mobile devices (e.g. PDA, laptop, cellphone, etc.). SMMEs are small businesses normally found in rural areas. The purpose of Grid in SMMEs is economic growth, social cohesion, affordable and cost effective services, local and regional development, to improve SMME technologies, and to generate employment.

Section 2 is on the problem statement. The next four sections describe the concepts that are related to the architecture that is the web services architecture, Grid-based Utility Infrastructure for SMMEs enabling Technologies (GUISET) architecture, recommender systems and grid services selection approaches. Sections 7 and 8 describe dynamic service selection architecture and user-preference-based dynamic grid selection framework. The last section is the conclusion.

2. Statement of the problem Dynamic web/grid services selection is a fundamental problem. Traditionally web services description language (WSDL) focuses on the syntax of web services. Considerable research and industry effort has focused on the semantics of web services leading to standards such as Web Service Modelling Ontology (WSMO) and Web Ontology Language Service (OWL-S) (Lamparter et al. 2004). One of the key open challenges is performing dynamic service selection for highly configurable web services with dynamic user preferences (Lamparter et al. 2004). There is a lack of efficient methods for the representation and matching of configurable services. In order to be able to compare service attributes correctly, we need to describe them in a way that captures their semantics. Dynamic service selection implies that atomic services have to be dynamically

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selected and composed to be able to suit user’s requirements, which in most cases depend on the user context. In order to enable Grid-based Utility Infrastructure for SMME-enabling Technologies (GUISET) to have dynamic service selection capabilities this research work will attempt to answer the following research questions:

1. What efficient mechanism can we use for dynamic service selection using user preferences in a grid environment to support dynamic service composition?

2. Which preference based algorithm that integrates user preference will work best for selection of grid services?

The main goal of this research is to develop a scheme for dynamic service selection based on user preference for a Grid infrastructure for mobile devices. The objectives are as follows:

1. To survey existing user preference-based selection approaches and mechanisms. 2. To design an architecture for dynamic service selection in grid services environment.

3. Web service architecture Web services are software systems designed to support interoperable machine-to-machine interaction over a network. Web services provide standard means of interoperating different software applications, running on different platforms and/or frameworks (Baltopoulos 2005). A web service is described using some standards such as Extensible Markup Language (XML), Simple Object Access Protocol (SOAP), Web Services Description Language (WSDL), and the Universal Description, Discovery, and Integration (UDDI Figure 1) project. WSDL works as transport protocol, message formatter, and locator. WSDL is a specification that describes available web service to the client. Discovery of service is any action that gives the service requester access to the WSDL for a service. Service registry provides the service requester with the location of a WSDL description and a URL (uniform resource locator) pointing to the service itself. The messaging layer is based on SOAP. SOAP is an XML protocol that facilitates publishes, finds, binds, and invokes operation. Figure 1: Web Service Architecture (Gottschalk et al. 2008)

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While numerous efforts have focused on service composition in the web/grid environment, service selection among similar services from multiple providers has not been satisfactorily resolved. In particular, all service composition works done so far are based on a given selection of services under a well set environment ( Gottschalk et al. 2002). The ultimate goal in personalized service provisioning has to be the fulfilment of individual user needs/preferences expressed as complex task which can be further divided into sub-goals and subsequently matched to different services. Even though UDDI and WSDL are commonly used today to implement service catalogs they still essentially lack strong concepts for service personalization which are crucial for advanced usability (Balke and Wagner 2003)

4. GUISET architecture

The conceptualization of Grid-based Utility Infrastructure for SMME-enabling Technologies (GUISET) is based on the idea of providing affordable ICT technology for small, micro and medium-sized enterprise (SMME) (Adigun et al, 2006). An appropriate strategy to make affordable technologies available using the utility approach to service delivery has to be identified. Figure 2 shows GUISET architecture with three layers and the focus is in the middleware layer. The focus of this research work is to address the issues around service selection in a dynamic environment like the GUISET architecture (Adigun et al. 2006)

Figure 2:GUISET architecture (Adigun et al. 2006)

To this effect, this work develops the middleware component for dynamic service selection in such environments. There are quite a number of challenges that arise from the adoption of mobile devices as clients to the grid infrastructure and the on-demand computing

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paradigm, which do not exist in the classical web services and computational grid environments. In such cases, the computing environment will be highly dynamic, which may result in poor quality of service (QoS) and quality of experience (QoE). Quality of service in the grid environment is an important issue due to the distributed nature of the services. It is also highly possible that there may be no single service which can provide the functionality required. In this case a service has to be composed automatically using the existing atomic grid services. Typically in a grid environment the number of candidate services to a grid service composition can be too large. In this case it will be a very cumbersome exercise for the user to select the most suitable service from those discovered. This then necessitates automatic dynamic selection of such service-based on the user’s preferences. This will require the use of some preference-based service recommendation approaches. Little or no work has been done so far in the development of preference-based recommendation system for grid service selection, and hence our work adapts some approaches emanating from product recommendation approaches.

5. Recommender systems

Recommender systems are deployed in e-commerce settings to help customers find products according to their special preference (Manouselis et al. 2006). The greatest challenge is to correlate users reliably when they overlap on a few services. The main purpose of recommender systems is to pre-select information a user might be interested in (Drachster et al. 2007). They make a prediction of user’s needs or interests. They are deployed for service selection. Recommender systems are classified into utility based, knowledge based, model-based and memory-based approach. Utility-based approach models a user’s multi-attribute utility function and recommends items with the highest utility based on function (Burke 2000). Knowledge based approach provides advices to the users about services they might be interested to request (Shiu-li 2008).

Model-based approach uses the database to estimate or learn a model which will be used for prediction (Breese et al. 1998). This class of approach first builds a model from the given users and then uses the model for making predictions about the active user. After clustering the users ahead of time, the active user is placed in one of the clusters. Alternatively, one might build decision trees (Singh and Huhns 2005). Memory-based approach considers the rating of all users directly instead of via an intervening step of building a model (Singh and Huhns 2005). There are some limitations of applying recommender system approaches to service selection. The shortcoming is the fact that the purchase of a product or the selection of a service does not imply any preference on a given product or service. The registry or broker does not provide the service that it is recommending and may have little to say about its quality and other features. Service registries do not have any control of service interaction; whereas an e-commerce site would know that a product was shipped. Another shortcoming of recommendation systems considered in this study is that services would be invoked multiple times and registry would not even be aware of the repeat customers of a provider. Services should be selected based on user preference dynamically in pervasive grid environment. Figure 3 covers classification of preference based algorithm.

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Figure 3: Classification of recommender systems

6. Grid service selection approaches There are a number of grid service selection approaches today , as follows:

6.1 Boolean selection Boolean selection is a widely used approach in grid systems and relies on classified advertised language. In Boolean selection, the expression of the desired service is based on Boolean logic. In this case services are presented as those that satisfy the user expectation and those that do not. Requirements are Boolean expressions. UDDI is being used to enable QoS based selection. Services should satisfy user’s expectations (Franco and Cannas 2000).

6.2 Ontology-based matchmaker Ontology is a model for describing the world that consists of a set of types, properties and relationships types. It is a data model that represents a set of concepts within a domain and the relationships among those concepts. Ontology performs a semantic based selection relying on the defined ontologies and offers a ranking phase using the attribute. Ontology repository maintains .different domain specific ontologies to support semantic matching in order to extend the service discovery capability to go beyond the simple keyword based approach (Balke and Wagner 2003).

6.3 Matchmaking Matchmaking framework is based on context, semantic and registry selection. Context selection request is matched within the appropriate context. Semantic selection request is matched semantically. Registry selection allows the separate application and Grid services semantics and supports application developers and Grid services developers in registering application and services semantic separately (Prabhakar T and Manikrao U 2006).

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5.4 Preference based selection: We are moving from traditional applications to advanced technology. Recommender systems are used mostly in e-commerce applications, search engines, retrieval systems. Now we are introducing recommender systems to grid environments. Services selected based on recommender algorithms are based on user requirements or satisfaction called user preferences according to this study. In this approach users should be provided with the language and a tool to express their requirements and preferences and to assess the suitable solutions when submitting a job to a grid system. Such as a tool can be integrated in the grid scheduling process in order to improve the selection capabilities of current systems. User-preference based approaches helps users to express their needs, the service providers satisfactorily provide users requested service according to this study. At some point the user may require a service, maybe available services might not meet the head of the user to accomplish a certain task. Therefore user-preference based approaches helps users to express themselves and receive relevant provision of services. Users should be provided by the language and the tool to express their requirements and preferences and to assess suitable solution when submitting their jobs to the grid system. In this study we use collaborative filtering as one of the user-preference based approaches applicable in highly dynamic environment like the grid. Collaborative filtering says: those who agreed in the past tend to agree again in the future. It looks for a user who shares the same selection, pattern with the active user. This approach supports dynamic selection of services in the grid environment. User-preference collaborative filtering (UPCF) is applicable in hybrid of memory-based and model-base recommender system. 7. The architecture for dynamic service selection The design requirements for the Grid services architecture are:

• The framework should be able to query for and infer preferences from the user preference

• The system should support personalization in dynamic environments.

Figure 4: Dynamic service selection architecture

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This framework creates middleware component of GUISET architecture for dynamic service selection. It achieves a primary objective of the grid, that is, the provision of services in a simple and transparent way (transparent to the user). Middleware hides the heterogeneous nature of services i.e. (provides services as if they are from the same providers). There are various components found in the architecture and each component has its own vital function in building up the middleware component. This architecture compares algorithm that will work for Grid service selection. Further we derive a framework from this architecture to perform selection of services in Figure 5

8. User preference-based dynamic grid selection framework The proposed architecture help us to evaluate efficient mechanisms (algorithms) that we can use for dynamic service selection by using user preference in a grid environment to support dynamic service composition. We derived the framework from the architecture that also creates the middleware component of GUISET architecture for dynamic service selection. It provides a variety of services required by an application to function correctly. It also achieves a primary objective of Grid that is, the provision of services in a simple and transparent way (transparent to the user). There are various components found in our derived framework and each component has its own vital function in building up the middleware component Figure 5. The framework selects services and compares recommender algorithms to be used for service selection. The user profile captures input data about the user and the service query. The user profile is defined according to the context of the user i.e. location, time, location and service requested. Mobile devices such as PDA, laptop, cell phones are Grid platforms used to access Grid services. The highly dynamic environment supports mobile devices. Although mobile devices have restricted computational and storage facilities, they help make existing tools more available by offering the possibility of new applications that are not possible with traditional desktop setups. Mobility makes it possible for an increase of accessibility of services in dynamic environments like the Grid. The service requester submits the user query to the service registry using a preference based algorithm. Preference based algorithm components contain algorithms that dynamically select services according to the user profile description. An algorithm matches service queries with available services, using both functional (service features or properties) and non-functional properties (QoS parameters). In this component we compare the existing recommender algorithms. The algorithm matches a user request with a registered service description and provides a list of available services matching the user requirements. The list is passed to the recommender system. A user selects a service from the list. After selecting from the list the user provides a rating of this service using the given metrics. This rating shows the user’s satisfaction and stores it in repository to be used as input to the recommendation. The service registry allows the service provider to register information about the services they offer. It holds references to services locates them and invokes a service. The service repository holds services registered by service providers. The service provider needs to register their services using service description, contains service profiles, contains QoS parameters and specifies the location of WSDL.

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Figure 5: Dynamic service selection framework

9. Conclusion Based on the requirements for grid service selection identified in this work, it can be concluded that an approach for grid services selection is able to handle preferences as soft constraints and semantically understand a request from a client. To this, a hybrid approach based on content-based and collaborative filtering recommender algorithms satisfies the needs of the GUISET architecture. The resultant approach considers factor such as specialized service features and QoS parameters in the selection of grid services that best suits the request and user preferences.

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10. References

Adigun, M., Emuoyibofarhe, O. and Migiro, O., Challenges to Access and opportunity to use SMME enabling Technology in Africa. In South Africa. Available at: http://www.witfor2007.org/commision/building_the_infrastructure/GUISETWITFOR.pdf [Accessed October 10, 2009].

Balke, W.T. and Wagner, M., 2003. Towards Personalized Selection of Web Services. In WWW (Alternate Paper Tracks). Available at: http://dblp.uni-trier.de/db/conf/www/www2003at.html#BalkeW03.

Baltopoulos, L.G., 2005. Introduction to Web Services. Available at: http://www.cl.cam.ac.uk/~ib249/teaching/Lecture1.handout.pdf [Accessed October 15, 2012].

Breese, J.S., Heckerman, D. and Kadie, C., 1998. Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In Morgan Kaufmann, pp. 43–52.

Burke, R., 2000. Knowledge-based recommender systems. Available at: http://www.cs.odu.edu/~mukka/cs795sum10dm/Lecturenotes/Day6/burke-elis00.pdf [Accessed October 15, 2012].

Drachsler, H., Hummel, H. and Koper, R., 2007. Recommendations for learners are different: Applying memory-based recommender system techniques to lifelong learning. In Proceedings of the 1st Workshop on Social Information Retrieval for Technology-Enhanced Learning and Exchange.

Foster, I., 2002. What is the Grid? A Three Point Checklist. Available at: http://www-fp.mcs.anl.gov/~foster/Articles/WhatIsTheGrid.pdf.

Franco, L. and Cannas, S.A., 2000. Generalization and Selection of Examples in Feed Forward Neural Networks. NEURAL COMPUTATION, 12, pp.2405–2426.

Gottschalk, K. et al., 2002. Introduction to Web services architecture. IBM Systems Journal, 41(2), pp.170–177.

Huang, S.-L., 2008. Comparison of Utility-Based Recommendation Methods. In 12th Pacific Asia Conference on Information Systems (PACIS 2008). City University Hong Kong. Available at: http://www.pacis-net.org/file/2008/PACIS2008_Camera-Ready_Paper_021.pdf.

Lamparter, S., Ankolekar, A. and Studer, R., 2007. Preference-based selection of highly configurable web services. In In Proc. of the 16th Int. World Wide Web Conference (WWW’07. ACM Press, pp. 1013–1022.

Manikrao, U.S. and Prabhakar, T.V., 2005. Dynamic Selection of Web Services with Recommendation System. In In: Proceedings of the International Conference on Next Generation Web Services Practices (NWESP), IEEE Computer Society. Press, p. 117.

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Manouselis, N., Costopoulou, C. and Sideridis, E.B., 2006. Metadata for Web Portals: Developing an e-Markets ’ Directory. In International Conference on (HAICTA 2006)Volos. Greece.

Singh, M.P. and Hunhs, M.N., 2005. Service-oriented computing semantics, processes, Agents, England: John Wiley and Sons LTD.

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