towards e-learning grids using grid computing in electronic learning

Towards E-Learning Grids:Using Grid Computing in Electronic Learning

Victor PankratiusAIFB Institute

University of KarlsruheD-76128 Karlsruhe, Germany

[email protected]

Gottfried VossenDept. of Information Systems

University of MünsterD-48149 Münster, Germany

[email protected]

Abstract

E-learning has been a topic of increasing interest in re-cent years, due mainly to the fact that increased schedulingflexibility as well as tool support can now be offered at awidely affordable level. As a result, many e-learning plat-forms and systems have been developed and commercial-ized; these are based on client-server, peer-to-peer, or, morerecently, Web service architectures, with a major drawbackbeing their limitations in scalability, availability, and dis-tribution of computing power as well as storage capabili-ties. This paper tries to remedy this situation by proposingthe use of grid computing in the context of e-learning. Inparticular, it is shown what advantages a utilization of gridcomputing may have to offer and which applications couldbenefit from it. Moreover, an architecture for ane-learninggrid is outlined, and the notion of a grid learning object isintroduced.

1 Introduction

Electronic learning (“e-learning”) has been a topic of in-creasing interest in recent years [1], due mainly to the factthat increased scheduling flexibility as well as tool supportcan now be offered at a widely affordable level, both withrespect to technical prerequisites and pricing. At the sametime, learning content can be made available even in remoteplaces and without the need to travel to the site where con-tent is delivered. As a result, many e-learning platforms andsystems have been developed and commercialized; these arebased on client-server, on peer-to-peer, or, more recently,on Web service architectures [33], with a major drawbackbeing their limitations in scalability, availability, and dis-tribution of computing power as well as storage capabili-ties. Thus, e-learning is currently deployed mostly in ar-eas where high requirements in any of these aspects are not

mission-critical, which excludes its exploitation, for exam-ple, in most natural sciences or medical areas. This papertries to open a new door for electronic learning, by propos-ing the use ofgrid computingin the context of e-learning.In particular, it is shown what advantages a utilization ofgrid computing may have to offer and which applicationscould benefit from it. Moreover, an architecture for ane-learning grid is outlined, and the notion of agrid learningobjectis introduced as a first step towards the realization ofthis novel concept.

As a typical example where currently e-learning systemsreach their limits, consider a medical school where anatomystudents examine the human body and prepare for practicalexercises. Up to now, it is vastly impossible to compute,say, photo-realistic visualizations of a complex body modelin real-time and display the computation result on a remotescreen. With the advanced functionality of an e-learninggrid, students could be provided with the possibility to grab,deform, and cut model elements (e.g., organs) with the clickof a mouse. Basically as before, the e-learning system couldsupport the learner by giving advice on how to cut or givefeedback for the actions, but beyond that virtual reality atlocal machine would become possible and improve the un-derstanding of the subject considerably.

The organization of the paper is as follows: In Section 2we collect several fundamentals that are considered prereq-uisites for our work; these stem from the areas of e-learningas well as grid computing. In Section 3 we begin to com-bine the two, and we outline an architecture that shows insome level of detail how a learning management system canbe interfaced with grid middleware in such a way that ane-learning grid capable of handling new types of learningcontent results; the main ingredients upon which activitiesinside this grid are based is the grid learning object, a notionthat is introduced as well. In Section 4, we indicate howan implementation of a grid application for some photo-realistic e-learning visualization could be obtained, thereby

This paper was published in: Proc. IEEE Workshop on Knowledge Grid and Grid Intelligence

(in conjunction with 2003 IEEE/WIC International Conference on Web Intelligence/Intelligent Agent Technology),

Saint Mary's University, Halifax, Nova Scotia, Canada, pages: 4-15, Oct 2003

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showing the feasibility of our approach. Finally, we sum-marize our work and draw some conclusions in Section 5.We mention that this paper is based on [27], in which furtherdetails and explanations can be found.

2 Fundamentals

In this section we introduce the fundamentals relevant toour work, which falls into two categories: e-learning andgrid computing. We consider each area in turn.

2.1 E-Learning Fundamentals

E-learning has been a topic of increasing interest for anumber of years now, and a consolidation w.r.t. several fun-damental notions has meanwhile been achieved. A generalagreement seems to exist regarding roles played by peoplein a learning environment as well as regarding the core func-tionality of modern e-learning platforms; we refer the readerto [1] for a detail account. The main players in these sys-tems are thelearnersand theauthors; others include train-ers and administrators. Authors (which may be teachers orinstructional designers) create content, which is stored un-der the control of alearning management system(LMS) andtypically in a database [31]. Existing content can be up-dated, and it can also be exchanged with other systems. Alearning management system as shown in Figure 1 is underthe control of an administrator, and it interacts with a run-time environment which is addressed by learners, who inturn may be coached by a trainer. Importantly, these threecomponents of an e-learning system can be logically andphysically distributed, i.e., installed on distinct machines,and provided by different vendors or content suppliers. Inorder to make such a distribution feasible, standards (e.g.,IMS and SCORM) try to ensure plug-and-play compatibil-ity [22].

E-learning systems often do not address just a specialkind of learner, but may rather be implemented in such away that a customization of features and appearance to aparticular learner’s needs is supported. Learners vary sig-nificantly in their prerequisites, their abilities, their goalsfor approaching a learning system, their pace of learning,their way of learning, and the time (and money) they areable to spend on learning. Thus, the target group of learn-ers is typically very heterogeneous; a system is ideally ableto provide and present content for all (or at least severalof) these groups, in order to be suitable, for example, for astudent who wants to learn about database concepts or fora company employee who wants to become familiar withcompany-internal processes and their execution. To fulfillthe needs of a flexible system as sketched, a learning plat-form has to meet a number of requirements, including theintegration of a variety of materials, the potential deviation

from predetermined sequences of actions [10], personaliza-tion and adaptation, and the verifiability of work and ac-complishments [32].

Content consumed by learners and created by authors iscommonly handled, stored, and exchanged in units oflearn-ing objects(LOs). Basically, LOs are units of study, exer-cise, or practice that can be consumed in a single session,and they represent reusable granules that can be authoredindependently of the delivery medium and be accessed dy-namically, e.g., over the Web [31]. Learning objects canbe stored in a database and are typically broken down intoa collection of attributes [31]. In a similar way, other in-formation relevant to a learning system (e.g., learner per-sonal data, learner profiles, course maps, LO sequencingor presentation information, general user data, etc.) can bemapped to common database structures. This does not onlyrender interoperability feasible, but also allows for processor even workflow support inside an e-learning system. In-deed, as has been shown, for example, in [32] e-learningconsists of a multiplicity of complex activities such as con-tent authoring or learner tracking and administration whichinteract with resources (including people such as learnersand authors), with one another (some activities trigger oth-ers), and with the outside world (such as existing softwaresystems) in a predefined way. If these activities are modeledas processes or workflows that operate on and manipulatelearner and learning objects, and if they are then attributedto and associated with the various components of a learningplatform, a workflow management system can be employedto control these activities. Thus, it becomes possible, forexample, to track the work and performance of a learner au-tomatically, or to deliver content or process feedback. Thisidea can be taken to higher levels as well; for example, onecan think of a college degree program that is fully super-vised by an electronic system.

If a process view or even workflow management is ac-cepted as fundamental modeling and enactment paradigm,it is a straightforward task to turn this kind of learning, atleast for certain situations and learner groups, into a collec-tion of Web servicesthat handle content and course offer-ings as well as other LMS processes, as illustrated in Figure3. We briefly introduce the Web service paradigm next.

2.2 Web Services

In essence, Web services are independent software com-ponents that use the Internet as a communication and com-position infrastructure. They abstract from the view of spe-cific computers and provide a service-oriented view by us-ing a standardized stack of protocols. To specify the oper-ations supported by a Web service, theWeb Services De-scription Language(WSDL) is used [9]. TheSimple Ob-ject Access Protocol(SOAP) is used to exchange structured




People

AuthoringSystem

Run-TimeSystem

Learning Management System (LMS)

Content Storage & Management

Authors

Administrators

Learners

Import/Export Interaction

Trainers

LearningContentCreation

People

AuthoringSystem

Run-TimeSystem





Authors

Administrators

Learners

Import/Export Interaction

Trainers

LearningContentCreation

Figure 1. Generic View of a Learning Management System.

data over the Web by building upon the HTTP protocol [7].To discover new services on the Web, or to publish exist-ing ones, theUniversal Description Discovery and Inte-gration Protocol(UDDI) is employed [5]. More complexWeb services can be composed out of existing services us-ing BPEL4WS[2].

In Figure 2, the typical steps of an invocation of a Webservice are shown. In a first step, suppose that a client needsto find a Web service which provides a specific functional-ity. This is done by contacting a UDDI registry (step 1),which returns the name of a server (service provider) wherean appropriate Web service is hosted (step 2). Since theclient still does not know how to invoke the desired service,a WSDL description is requested which contains the nameand the parameters of the operation(s) of the service (steps3 and 4). The client is now able to invoke the service us-ing the SOAP protocol, which essentially puts the data inan envelope and sends it over the Web by using HTTP. Theservice provider receives the request and executes the de-sired operation(s) on behalf of that client. The results arefinally sent back to the client by using SOAP over HTTPagain (step 6).

The aforementioned view of making e-learning offeringsavailable as Web services has recently been developed in[33]. As described there, the various functionalities of an e-learning system can be decomposed and be made availableas Web services individually. The decomposition is basedon an appropriate modeling of the processes, objects, andtheir interactions as they occur in such a platform [32]; fi-

nally, the results need to be mapped to distinct services (andcorresponding providers) as desired. E-learning functionsresult from adequate service compositions.

2.3 Basics of Grid Computing

Thegrid computingparadigm [13, 8] essentially aggre-gates the view on existing hardware and software resources.The term “grid” alludes to an electrical power grid andemphasizes the perception that access to computational re-sources should be as easy as the common access to a powergrid. In addition, a grid user should not have to care wherethe computational power he or she is currently using comesfrom, just like in a power grid where a user does not care bywhich plant the electricity is produced which is currentlyused.

A grid unifies many different resources like computerswith CPUs and storage, remotely controlled instruments(e.g., electron microscopes, radio telescopes), and visu-alization environments by using uniform interfaces. Fur-thermore, it is possible to distribute computations and dataacross all computers in a grid. This is done in a transparentway so that users of the grid are not aware of it. Thus, avail-able resources can be used more efficiently and the aggre-gation of idle CPU cycles can even lead to supercomputingcapabilities.

Grid computing has its origins in wide-area distributedcomputing, in particular in meta-computing, where geo-graphically distributed computers are unified in a way that




UDDI Registry

Web Server A Client

1. Where is a Web service with operation X? (UDDI)

2. Server A (UDDI) 6. Result (SOAP over HTTP)

5. Call operation X (SOAP over HTTP)

4. Description (WSDL)

3. Which operations? How to invoke?

Figure 2. Invocation Steps of a Web Service.

UDDI

Learning Object 1 Meta- Data



E-Learning Course

Meta- Data

Content Presentation 1

Content Presentation 2

User Tracking

Content Creation

Learner/Client

Internet

Learning Content

Web services

...

Figure 3. E-learning as a Web Service.

they can be perceived as one big powerful computer [12].Grid computing extends this view to a large-scale “flexi-ble, secure, coordinated resource sharing among dynamiccollections of individuals, institutions, and resources” [15].Modern grids also focus on scalability and adaptability andtherefore adopt Web technologies and standards such as theExtensible Markup Language (XML) or Web services. Dur-ing the evolution of grid computing, two basic types of gridshave merged: Computational grids and data grids. Compu-tational grids mainly focus on the distribution of computa-

Grid

Fabric

Connectivity

Resource

Collective

Application

Resource Level

User Level

Core Grid Middleware

Figure 4. Layers in a Grid Middleware.

tion among computers in a grid, while data grids are tailoredtowards data intensive applications which handle petabyte-sized data in a grid [11]. Although these two types of gridshave evolved, some authors argue that a convergence be-tween computational grids and data grids can be observed[3, 23].

Grids are typically implemented as a form of middle-ware which provides all grid-related services and can alsouse the Internet as a communication infrastructure. In [15]




a general protocol architecture for grids is proposed whichis depicted in Figure 4. As can be seen, the middlewarecontains five layers. The lowestfabric layer implementsuniform interfaces for access to resources, such as comput-ers, storage media, or instruments in a grid and guaranteesinteroperability. The usage restrictions of a resource withinthe grid can be specified in security policy files. The fab-ric layer should be made available locally on each resourceto enable communication with the grid. The next higherconnectivity layerdefines communication and security pro-tocols for the grid. By using cryptographic authenticationalgorithms a “single-sign-on” can be implemented, so that auser has to authenticate only once in the grid and use any re-sources later on. The security protocols cooperate with thelocal security protocols of each resource and do not replacethem. Next, theresource layerimplements access to sin-gle resources and monitors their status. This functionalityis required in thecollective layer, which coordinates globalinteractions among collections of resources. In this layerbrokers and schedulers distribute computations or data onthe grid. Finally, theapplication layercontains grid appli-cations which build upon the underlying layers of the coregrid middleware. This layer can change depending on userprograms, while the underlying layers always provide thesame functionality. The hourglass-shaped outline of Figure4 suggests that the number of protocols and services shouldbe minimized in the core grid middleware in order to max-imize interoperability. The wide bottom and of the hour-glass implies that the fabric layer should support as manyresource types as possible. Similarly, many applicationsshould be available at the top.

Grid middleware along the lines just described has al-ready been implemented in various projects, includingGlobus [14], Legion [19], or Condor-G [17], to name just afew. TheGlobus ToolkitVersion 3 with itsOpen Grid Ser-vices Architecture(OGSA) [16] currently has the biggestinfluence on the development of grids. Its philosophy isto provide a bag of services and specifications which canbe combined individually to form a grid middleware. Thecomponent model of Globus Version 3 is based on grid ser-vices which are actually Web services with specific exten-sions (e.g., interfaces) for use in grids. Grid services can beimplemented in nearly any programming language and canuse the Internet as an underlying communication infrastruc-ture. In addition, grid services can also create instances ofthemselves. The idea behind OGSA is to construct each ofthe grid middleware layers described above by using appro-priate grid services.

Since Web services are being employed in the contextof grid computing already, and since certain e-learning ap-plications also render the use of Web services suitably, it isnear at hand to consider combining the two, which will bethe subject of the next section.

3 E-Learning Grids

Complex applications which are computationally inten-sive and handle large data sets have been systematically ig-nored in the context of e-learning up to now, due mainly totechnical feasibility problems and prohibitively high costs.As will be demonstrated in the remainder of this paper, gridcomputing can close this gap and enable new types of e-learning applications, such as photo-realistic visualizationsor complex real-time simulations. Computations and datacould be distributed on a grid as soon as desktop computersof learners cannot handle them anymore. This particularlypertains to university environments where the hardware in-frastructure already exists, since there are often hundredsof networked computers, ranging from PCs to supercom-puters, which most of the time are not working to capac-ity or even run idle. We therefore propose the creation ofe-learning gridsin which grid computing functionality isintegrated into e-learning systems.

As we have mentioned in the Introduction, thereare many conceivable applications fore-learning grids.Medicine students could use photo-realistic visualizationsof a complex model of the human body to prepare for prac-tical exercises. Such visualizations, computed in real-time,could improve the understanding of the three-dimensionallocations of bones, muscles, or organs. Students should beable to rotate and zoom into the model and get additional in-formation by clicking on each element of the model. Withmore advanced functionality such as virtual surgery, stu-dents could be provided with the possibility to grab, deform,and cut model elements (e.g., organs) with the click of amouse. In biology courses the ability of grids to integrateheterogenous resources could be used to integrate an elec-tron microscope into the grid. We mention that the technicalfeasibility of this approach has already been demonstratedin the TeleScience project [30]. However, this project couldbe widely extended to integrate the controls and output ofthe electron microscope into a learning environment so thatstudents can be assigned tasks or read subject-related textswhile operating the microscope. Similarly, in engineeringcourses complex simulations, e.g., in a wind channel, canbe made accessible for each student by using grids.

We next outline an architecture fore-learning grids. Todemonstrate the technical feasibility, the architecture will bekept as simple as possible. It contains a Learning Manage-ment System (LMS) as well as grid middleware, which areboth based on Web services and grid services, respectively(cf. Figure 5). In this figure, grid and Web services are de-picted as rectangles containing a name as well as the mostimportant operations. Note that grid services (with greyname fields) can easily be distinguished from Web services.The LMS interacts transparently with the grid middlewareso that a learner is not aware of the grid. Furthermore, the




architecture is designed in such a way that a learner onlyneeds a Java-enabled Internet browser to use both the LMSand the grid. We explain the architecture shown in Figure 5as well as most of the operations listed in this figure in moredetail in the following subsections.; we begin with the up-per half (Core Grid Middleware) and then turn to the lowerone (LMS).

3.1 Core Grid Middleware

The grid middleware of ane-learning grid implementsthe various layers shown above in Figure 4. Thefabric layeris implemented as a Java applet, which provides uniforminterfaces to all resources in the grid. For the time being,we assume for simplicity that there are only computers andno specialized instruments like electron microscopes in thegrid. We will explain possible extensions later. Further-more, locally available policy files specify usage restrictionsfor computers in the grid (e.g., maximal CPU load, usagehours, etc.); finally, meta-data describe each resource type.The fabric layer applet has to be started on each computerthat participates in sharing computational capacity and stor-age space in the grid. This can be done while a user accessesa Web page with his or her Web browser to authenticate inthe grid.

The connectivity layerconsists of agrid login ser-vice. This service needs to have access to a database inwhich user information and access rights are stored to-gether with a hash value of the password. The servicecan create new users with anewGridUser operationand delete users with correspondingdelGridUser op-eration. When a grid user wants to enter the grid, an op-erationGrid_login is called, which checks the user’slogin name and password against the values stored in thedatabase. If the login was successful, the computer is regis-tered withregisterClient in aClientsRegistry ,the latter of which contains a list of all resources cur-rently available in the grid. Furthermore, abroker is as-signed which can handle requests to distribute computationor data across other computers in the grid. When a clientlogs out, theClientsRegistry is updated through aderegisterClient operation. The login service usesan authentication mechanism based, for example, on Ker-beros v. 5 [24]. It uses a symmetric cryptographic algorithmand requires that each entity in the grid (i.e., grid service orcomputer) must have a specific key which must be known tothe grid login service. For all computers of a grid, the hash-value of the user password could be used as a key, while gridservices have a key generated and registered at the login ser-vice by the respective developers. The idea of the authen-tication algorithm, whose details are beyond the scope ofthis paper, is that a computer first requests a ticket-grantingticket (TGT). If a TGT is received, the authentication was

successful and further session tickets can be requested forthe communication with any other grid service. Before acomputer can access the operation of a grid service, a validsession ticket must be presented, which is checked by therespective service. This procedure is executed every timean operation of a grid service is accessed, so that an addi-tional graphical representation is omitted in Fig. 5.

The resource layercontains aninformation servicewhich is aware of the status and type of all resources inthe grid. By accessing the clients registry it firstly deter-mines which computers are available. It then requests fromeach computer some status information (e.g., CPU load, un-used storage space) with agetStatus operation, the re-source type withgetResType and the usage restrictionswith getPolicyFile . The list with the collected infor-mation can be accessed withshowAllStatus and is usedto schedule computations or distribute data in the collectivelayer.

In thecollective layerthere is abroker, replica manage-mentand replica selection service. For simplicity, we as-sume that there is only one copy of each service. The bro-ker implements a grid scheduling algorithm [21] and is re-sponsible for distributing computations and data across thegrid. The broker has to register in aBrokerRegistry ,so that the grid login service and grid applications in the ap-plication layer can find it. For the distribution of data it usesthe replica management and replica selection service, whichimplement the functionality that is typical of data grids. Weassume here that the data to be replicated is read-only. Thereplica management service can create replicas of data oncomputers with the operationcopyData . When data isreplicated, an entry is added to theReplicaRegistrywith the exact information which parts of data were repli-cated on which machines. When replicated data is deletedwith deleteData the corresponding entry in the registryhas to be erased withde-registerReplica . With theoperationfindReplica of the replica selection serviceexisting replicas can be found when data is accessed.

All assumptions made so far can be relaxed, which leadsto a loss of simplicity of the architecture. The fabric layerapplet could be extended to support instruments, like elec-tron microscopes. In this case, the information and bro-ker service would have to be adapted since instruments can-not execute computations or store data. Furthermore, therecould be more than one service of each type. For exam-ple, several grid login services could be responsible for dis-joint authentication domains. Clients could look up in aLoginRegistry which login service is responsible forthe domain they are located in. A problem arises whenclients need to access resources of different authenticationdomains, which requires cooperation between grid loginservices. It is also possible to have information, broker, andreplica services for each authentication domain to increase




LMS_login


edit_LOB publish_LOB

Authoring

bookLOB execLOB searchforLOB

Course Mgmt.

showAllPoints showPerformaceGraph

Progress Monitor composes asessment

results of all LOBs

implements the functionality of a

discussion board for learners

showContributions addContribution

Discussion

connectToChatRoom sendMsg getMsg disconnect

Chat

showBookedCourses showBill

Accounting

supports synchronous communication between

learners

manages accounting- related information

edit search add delete

Ontology

DB with access rights for the LMS LMS_login

LMS_logout newLMSUser delLMSUser generateTGT generateSessTicket

LMS Login

UDDI Content Registry

Learning Content (internal or external)

supports semantic search

TGT

Ticket Req.

Session Ticket Learner

learning object

metadata assessment

learning object

metadata assessment

grid-enabled learning object

metadata assessment grid application layer

user interface

additional services (internal or external)

Core Grid Middleware

which resource is available?

status information

Request (computation, store data)

Reply (results, acknowledgement)

Clients Registry

which brokers are available?

Grid_login Grid_logout registerClient deregister Client generateTGT generateSessTicket assignBroker newGridUser delGridUser

Grid Login

getStatus getResType getPolicyFile showAllStatus

Information

knows status and types of all resources

Ressource with JAVA-Client, local PolicyFile,

Metadata

scheduleGridTasks

Broker

distributes computation and data on the grid

Broker Registry

register

copyData deleteData registerReplica de-registerReplica

Replica-Mgmt.

findReplica

Replica-Selection register/de-register

client

Grid_login

Replica Registry

DB with user information and access rights for the

grid

Fabric Applet

Broker

TGT

Resource

is a Session Ticket

TicketReq.

Grid_login Grid_logout

newGridUser delGridUser

E-Learning PC

Figure 5. Architecture of an E-Learning Grid.




the efficiency in large grids. Similarly to the problems en-countered at the login services, a cooperation between allinformation, broker, and replica services of each domaincould be necessary.

3.2 Learning Management System (LMS)

An LMS generally coordinates all learning-related activ-ities. We assume that the entire LMS functionality includ-ing the learning contents are implemented as Web services.A learner who typically uses a PC for a learning sessioninteracts directly only with the LMS and is not aware of agrid. The LMS offers both content which makes use of thegrid as well as content that does not need grid functionality.The integration between LMS and grid will be described inthe next subsection. All Web services of the LMS are ac-cessed via Web pages, so that the learner only needs a Webbrowser to utilize the LMS.

In a first step the learner has to authenticate in the LMS,which is done by anLMS login service. This service is sim-ilar to thegrid login service, i.e., it draws on a database withaccess rights and uses the same authentication mechanismfor the LMS. When the learner is logged in and authenti-cated, he or she can access a Web page for course manage-ment, the functionality of which is implemented in acoursemanagement service. The learner can look for suitablecourses with asearchLOB operation, which searches forlearning objects in aContentRegistry . ThebookLOBoperation is called to enroll for a course. A class can be at-tended by calling theexecLOB operation.

An ontology servicesupports the semantic search forcourses. It basically contains an ontology defining seman-tic relationships between Web services that provide learningobjects. Thus, for a search term like "programming" it pos-sible to obtain results like “C++,” “Java,” or “Prolog.” Theontology service provides operations to add, delete, edit, orsearch for entries in the ontology. Next, theauthoring ser-viceprovides an environment to create, edit, and publish e-learning content in theContentRegistry , so that theycan be found by the LMS. In addition, entries can be addedto the ontology for the semantic retrieval of content.

The Web services which providee-learning contentcon-sist of three fundamental components. The first part is alearning object, which is typically a lesson (e.g., in HTMLor XML format) or a course consisting of several learn-ing objects. The assessment part defines online-tests sothat students or teachers can check wether a lesson is well-understood and the material mastered. The last part containsmeta-data for search engines that describes the content in astandardized way. The functionality of the grid is used withgrid learning objects, which also integrate a grid applica-tion layer and a user interface and will be described in thenext subsection.

The LMS also comprises other services. Indiscussionboardsor chat roomslearners can interact with instructorsor other learners and ask questions. Aprogress monitorcomposes the assessment results from all lessons into a gen-eral overview; this can also be used to create certificates.An accounting servicemanages all processes which are re-lated to financial aspects. It shows, for example, all bookedcourses or the bill that has to be paid by an individual. Fi-nally, it should be mentioned that the functionality of theLMS can be extended by other Web services, which can ei-ther be provided internally (i.e., in a local network) or exter-nally from other suppliers over the Web. The flexibility ofWeb services also allows to distribute services of the LMSon different machines in a local network or over the Web.

3.3 Integration of Grid Middleware and LMS

After having discussed the functionalities of grid mid-dleware and LMS in the previous subsection, resp., we willexplain their integration next.

Thee-learning PCis used by learners to access the LMS.At the same time, such a PC can also be used as a resourcefor the grid. This has been modeled by the “is a” relation-ship in Figure 5 which also illustrates that at the same timenot every resource of the grid needs to have access to theLMS.

The LMS login servicemakes it possible for thee-learning PC to become a resource in the grid. When thelearner authenticates himself on the Web page that is con-nected to the login service of the LMS, the fabric layer ap-plet of the grid can be transferred asmobile codeand bestarted locally on thee-learning PC. This enables commu-nication with the grid. TheLMS login servicetransparentlycalls theGrid_login operation of thegrid login servicewith the data and the PC of the user as parameters. Thiscompletes the authentication process of thee-learning PCin the grid. If a user of the LMS is not registered at the gridlogin service, the LMS login service could be given the au-thority to create new users in the grid login service, basedon a trust relationship between the two services. These stepskeep the login procedure of the grid completely in the back-ground, so that the learner is not aware of it.

3.4 Grid Learning Objects (GLOBs)

We propose the notion of agrid learning objects(GLOB) for using the grid in e-learning applications. AGLOB extends the functionality of a “traditional” learningobject (in the sense of [4, 31]) by adding grid functionalityconsisting of a specificgrid application layer(see Figure 4)and auser interface. The structure of a GLOB is depictedin Figure 6 and is designed in such a way that it can containboth conventional e-learning content and content that uses




Grid-enabled Learning Object (GLOB)

Overview

Metadata

Summary

Reusable Information Object (RIO)

Reusable Information Object (RIO)

Content

Practice Items

Assessment Items

Grid Functionality

- Interface for user access to grid application - Java applet

- Application layer implements access to the grid - calls other grid services

User Interface

Grid Application Layer (Grid Service)

Web Service Wrapper

Figure 6. Structure of a Grid Learning Object (GLOB).

grid functionality. Basically, a GLOB is wrapped by a Webservice which makes it possible to easily integrate it intothe LMS (see above). In addition, the Web service providesoperations to access individual elements of the GLOB, totransform content (e.g., from XML to HTML), or to gener-ate online tests.

The design of a GLOB is based on [4] and consists ofseveral parts: Anoverviewof the lesson,metadata, whichis used to find GLOBs, severalreusable information objects(RIOs), and asummary. A RIO contains acontentpart withthe actual content of a unit of study. The content can eitherbe stored in a format such as XML, HTML or, as alreadymentioned in Section 2, in a database.Practice itemscanbe used to generate online exercises for the learners.Assess-ment itemsare used to generate online tests for final exams.

An RIO may additionally contain grid functionalitywhich is implemented as a grid service of thegrid appli-cation layerand accessed via auser interface. The grid ap-plication layer implements grid applications for e-learningand uses the underlying layers of the core grid middleware.While the layers of the core grid middleware remain sta-ble, the application layer may change depending on the ap-plication. To be executed, the grid service of the appli-cation layer first has to be extracted from an RIO by us-ing a Web service operation. It then has to be started ona computer that provides the hosting environment of the

Globus Toolkit v. 3, which also supports the creation ofinstances of grid services. We will call such a computera grid application server. During an initialization proce-dure of the grid service, a grid broker has to be found viathe BrokerRegistry , so that it can be assigned taskssuch as the distribution of computations or data on the grid.In addition, the grid service of the application layer is re-sponsible for the composition of the results returned by thebroker.

Theuser interfacecan be implemented as a Java appletthat transforms user input (e.g., mouse clicks) into tasks forthe grid service in the application layer (e.g., a request torecalculate a 3D model). If the learning content is avail-able in HTML format, the interface applet can be placedanywhere inside the content. When a user accesses sucha lesson, the applet is automatically loaded onto his locale-learning PC. In this way no other administrative actionshave to be taken, and a learner is not aware that he or she isusing the grid. If the learning content is available in otherformats (e.g., XML), Web service operations can be pro-vided to transform it into HTML and embed the interfaceapplet. Furthermore, Web service operations can be used tointegrate the user interface applet into online tests generatedfrom practice and assessment items.




User Interface (Java)

I B M

Grid

3D-Model

Instance 1

Instance 2

Instance 3

Instance n

"rotate clockwise 30

degrees around X-axis "

result: (JPEG-file)

result 1 result 2 result 3 result n

Visualization Grid Service

Broker

Figure 7. Layers of a Visualization Application for an E-Learning Grid.

4 Towards an Implementation of an Applica-tion Layer for Visualization

To show the feasibility of the concepts developed in theprevious section, and to illustrate the interaction betweengrid middleware, grid application layer and user interface,we will now outline a possible implementation of a grid ap-plication for photo-realistic visualization that can be usedfor e-learning courses in medicine. To this end, we assumethat learners are to be provided with an interactive compo-nent visualizing in a photo-realistic way complex modelsof the human body with elements like bones, muscles, ororgans. By clicking with the mouse, learners can rotate orzoom into the model or get explanatory information on cer-tain elements. Furthermore, learners can suppress the ren-dering of different layers of the model (e.g. skin, bloodvessels, muscles, etc.).

The relevant layers for an implementation of this sce-nario are shown in Figure 7. The user interface is imple-

mented as a Java applet and runs inside an Internet browserof a learner. The visualization component is implementedas a grid service, runs on a grid application server and repre-sents the grid application layer. The broker provides accessto the grid, which consists of many networked resources.The user interface captures mouse clicks and movementsand transforms them into commands for the visualizationservice (e.g., rotate model clockwise 30◦ around X-axis anddisplay it again). After performing the necessary calcula-tions, the visualization service returns an image file (e.g., inJPEG format) that can be displayed by the user interface.

The visualization service implements a computer graph-ics algorithm (e.g., ray tracing [18]) to render model datain a photo-realistic way. It uses the commands of the userinterface, a model file and the coordinates of the upper leftand lower right corners of a rendering window to computea graphics file as output. The model data is found in afile which may contain polygon and texture descriptions orfinite-element models of the human body. Before the com-putation of the output file begins, the area inside the render-




ing window is divided into several disjoint regions. For eachregion, an instance of the visualization service is created.This is basically a copy of the visualization service wherethe parameters of the rendering window are adjusted to therespective region. An instance has to calculate the color forevery pixel of its region depending on parameters like viewangle, texture, lighting, etc. The calculations for each pixelrepresent a task that is submitted to the grid broker. Thebroker distributes the computations on the grid and returnsthe results (i.e. color of a pixel) back to the instance whichsubmitted the task. When the calculations of all regions arefinished, the results are returned to the visualization servicewhich composes them to the final result. Although this ap-plication is computationally intensive, it can be observedthat network traffic is low.

For the implementation of the visualization service,available libraries likevtk [29] or VisAd [20], which of-fer a wide range of graphics algorithms, could be used. Inthe 3D model file different categories such as data for skin,muscles or blood vessels need to be distinguished. To pre-serve interoperability, the format could be based on XML(see also X3D [34]). This would render the use of recentquery languages such as XQuery [6] possible, which canbe used to select only those model categories for renderingwhich have not been suppressed by the user. In addition,XML offers the possibility to easily add explanatory textsassociated to model categories or elements. If a learner re-quests more information by clicking on a certain group ofpolygons (e.g., a bone), the user interface applet and the vi-sualization service can identify the model element and ex-tract the textual information from the model file by usinga query. Since a model file may become huge, it could bestored on a server and identified with a unique URL. Theadvantage is that model data does not have to be duplicatedfor each instance that is created by the visualization service.Instead, the visualization service only passes the URL ref-erence of the model file.

Finally, it should be mentioned that anatomically correctmodels can be obtained by using the computed tomogra-phy technique [25, 28]. The functionality of the visualiza-tion service can even be extended to support virtual surg-eries, which would be in line with recent developments inthe intersection of tele-medicine and Web services. To givelearners to possibility to grab, deform, or cut model ele-ments, a simulation component and physical models have tobe added. These models specify the physical properties ofmodel elements, e.g., how skin or organs can be deformedwhen touched, permissible positions, etc. More details forthe implementation of virtual surgeries can be found in [25]as well as in [26].

5 Conclusions

In this paper, we have argued that e-learning and learningmanagement systems on the one hand and grid computingon the other, which have been considered and developedseparately in the past, can fruitfully be brought together, inparticular for applications or learning scenarios where ei-ther high computational power is needed or the tool sets onwhich learning should be done are too expensive to be givenout to each and every learner. Beyond making a case for itsfeasibility and more importantly, we have outlined in detailan architecture for an e-learning grid which integrates coregrid middleware and LMS functionality appropriately. Fi-nally, we have indicated how an e-learning grid could berealized on the basis of suitably designed grid learning ob-jects.

Clearly, what has been described in this paper is intendedmainly as an attempt to draw up a research agenda for an ex-ploitation of grid computing in e-learning, and many detailsremain to be filled in. Yet it appears feasible to pursue thearea, as there is considerable hope for being able to extendthe achievements of electronic learning beyond the limits ofindividual computers. Future issues to be studied include,for example, transactional guarantees for service executionsover a grid such as atomicity, or recovery protocols that helprestore an operational state after a grid failure (a problemnot to be neglected, as learned from the recent northeasternUS power grid failure).

Acknowledgements.Most of this work was done while thefirst author was with the Department of Information Sys-tems, University of Münster in Germany and the secondauthor was with the Department of Management Systems,University of Waikato in Hamilton, New Zealand; we thankboth institutions for their support and hospitality.

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