P2P WEB-BASED TRAINING SYSTEM

USING MOBILE AGENT TECHNOLOGIES

Shinichi Motomura, Takao Kawamura, Ryosuke Nakatani, Kazunori Sugahara

Tottori University

4–101, Koyama-Minami, Tottori 680–8552, JAPAN

Keywords:

P2P, e-Learning, Mobile Agent, Distributed hash table.

Abstract:

In this paper, we present a novel framework for asynchronous Web-based training. The proposed system has

two distinguishing features. Firstly, it is based on P2P architecture for scalability and robustness. Secondly,

all contents in the system are not only data but also agents so that they can mark user’s answers, can tell the

correct answers, and can show some extra information without human instruction. We also present a prototype

implementation of the proposed system on Maglog. Maglog is a Prolog-based framework for building mobile

multi-agent systems we have developed. The agent migrates using HTTP as transfer protocol and XML as

encoding format itself. The user interface program of the proposed system is built on Squeak. Performance

simulations demonstrate the effectiveness of the proposed system.

1 INTRODUCTION

The term e-Learning covers a wide set of applications

and processes, such as Web-based training (hereafter

we abbreviate as WBT), computer-based training, vir-

tual classrooms, and digital collaboration. We are

concerned with asynchronous WBT that allows the

learner to complete the WBT on his own time and

schedule, without live interaction with the instructor.

Although a large number of studies have been made

on asynchronous WBT(Helic et al., 2003; Homma

and Aoki, 2003), all of them are based on the

client/server model. The client/server systems gen-

erally lack scalability and robustness. In the recent

years, P2P research has grown exponentially. In this

paper, we present a novel framework for asynchro-

nous WBT. The proposed system has two distinguish-

ing features. Firstly, it is based on P2P architecture

and every user’s computer plays the role of a client

and a server.

Namely, while a user uses the proposed e-Learning

system, his/her computer (hereafter we refer to such a

computer as a node) is a part of the system. It receives

some number of contents from another node when it

joins the system and has responsibility to send appro-

priate contents to requesting nodes. Secondly, each

content in the system is not only data but also an agent

so that it can mark user’s answers, tell the correct an-

swers, and show some extra information without hu-

man instruction. The agent migrates using HTTP as

transfer protocol and XML as encoding format itself.

In this paper,we present a prototype of the proposed

system on Maglog that is a Prolog-based framework

for building mobile multi-agent systems we have de-

veloped.

2 PROPOSED SYSTEM

2.1 Overview

As mentioned in the previous section, we focus on

asynchronous WBT, that is to say, a user can connect

to the proposed e-Learning system anytime and any-

where he/she wants. Once connection is established,

the user can obtain exercises one after another through

specifying categories of the required exercises. User’s

answers for each exercise are marked as correct or

incorrect right away. Extra information may be pro-

vided for each answer, which can be viewed when the

correct answer is shown.

While a user uses the proposed e-Learning system,

his/her computer is a part of the system. Namely, it

receives some number of categories and exercises in

them from another node when it joins the system and

202

Motomura S., Kawamura T., Nakatani R. and Sugahara K. (2005).

P2P WEB-BASED TRAINING SYSTEM USING MOBILE AGENT TECHNOLOGIES.

In Proceedings of the First International Conference on Web Information Systems and Technologies, pages 202-205

DOI: 10.5220/0001228602020205

 SciTePress

has responsibility to send appropriate exercises to re-

questing nodes.

The important point to note is that the categories a

node has are independent of the categories in which

the node’s user are interested as shown in Figure 1.

Figure 1 illustrates that user A’s request is forwarded

at ﬁrst to the neighbor node, next forwarded to the

node which has the requested category.

Figure 1: Proposed e-Learning system.

2.2 P2P Aspect

When the proposed system begins, one initial node

has all categories in the system. When another node

joins the system, it is received some number of cate-

gories from the initial node. The categories are dis-

tributed among all nodes in the system according as

nodes join the system or leave the system.

We would like to emphasize that in existing P2P-

based ﬁle sharing systems such as Napster(Napster,

1999) and Gnutella(Gnutella, 2000) each shared ﬁle

is owned by a particular node. Accordingly, ﬁles

are originally distributed among all nodes. On the

other hand, the categories in the proposed system

are originally concentrated. Consequently, when a

new node joins the system, not only location infor-

mation of a category but the category itself must be

handed to the new node. Considering that, the P2P

network of the proposed system can be constructed as

a CAN(Ratnasamy et al., 2001).

A CAN has a virtual coordinate space that is used

to store (key, value) pairs. To store a pair (K

, V

key K

is deterministically mapped onto a point P in

the coordinate space using a uniform hash function.

The corresponding (key, value) pair is then stored at

the node that owns the zone within which the point

P lies. In the proposed system, we let each category

be a key and let a set of exercises belonging to the

category be the corresponding value.

2.3 Mobile Agent Aspect

Generally, in addition to service to show an exercise, a

WBT server provides services to mark the user’s an-

swers, tell the correct answers, and show some ex-

tra information about the exercise. Therefore, for the

proposed system which can be considered a distrib-

uted WBT system, it is not enough that only exercises

are distributed among all nodes. Functions to provide

the above services also must be distributed among all

nodes. We adopt mobile agent technology to achieve

this goal. Namely, an exercise is not only data but

also an agent so that it can mark user’s answers, tell

the correct answers, and show some extra information

about the exercise.

In addition, mobile agent technology is applied to

realize the migration of categories, that is, each cate-

gory is also an agent in the proposed system.

3 DESIGN AND

IMPLEMENTATION

We have implemented a prototype of the proposed

system on Maglog that is a Prolog-based framework

for building mobile multi-agent systems we have de-

veloped(Kawamura et al., 2003).

Node Agent There is one node agent on each node.

It manages the zone information of a CAN and for-

wards messages to the category agents in the node.

Category Agent Each category agent stands for a

unit of a particular subject. It manages exercise

agents in itself and sends them to the requesting

node.

Exercise Agent Each exercise agent has a question

and functions to mark user’s answers, tell the cor-

rect answers, and show some extra information

about the exercise. These data are formatted in

HTML.

Interface Agent There is one interface agent on each

node. It is an interface between the user interface

program and other agents.

Agents communicate with other agents through

‘ﬁeld’s provided by Maglog framework. A ﬁeld is

kind of a preemptive queue. Roughly speaking, the

above-mentioned four kinds of agents execute a mes-

sage dispatch loop. Each message to an agent is

queued into the ﬁeld owned by the agent. The user in-

terface program also communicates with the interface

agent through a ﬁeld via XML-RPC(Winer, 1998).

As mentioned above, the user interface program of

the proposed system has been developed through ex-

tending Scamper which is a simple web browser runs

in Squeak(Ingalls et al., 1997).

P2P WEB-BASED TRAINING SYSTEM USING MOBILE AGENT TECHNOLOGIES

203

Figures 2 and 3 are screen-shots of the user inter-

face program. By clicking the left button of a mouse

on the category, a user can select it. After selection of

the category, a user can obtain an exercise belonging

to the category by clicking the left button of a mouse

on one of buttons in the button pane. After a while

an appropriate exercise agent comes from some node

and the user can try the question. The user can re-

quire to mark his/her answer anytime by clicking the

submit button. Figure 2 shows an example result of

marking. Figure 3 shows the correct answers and ex-

tra information about the exercise that are shown by

clicking the answer button.

Figure 2: User’s answers are marked as correct or incorrect

by clicking the submit button.

Figure 3: Correct answers and extra information are shown

by clicking the answer button on the screen shown as Figure

4 PERFORMANCE SIMULATION

This section presents performance simulations ob-

tained from a prototype implementation of the pro-

posed system described in the previous section.

The experimental environment consists of 8 PCs

with Intel Pentium4 2.4GHz processor and 512MB

of RAM, and all the PCs are running on GNU/Linux

(kernel version is 2.2.26) operating system.

We measured the searching latency in the exper-

imental environment under the conditions shown in

Table 1. All nodes send searching requests at the

same time and exercises to be searched are selected

randomly. We compared distributed and concentrated

systems where these terms are deﬁned as follows:

Distributed System Each node has one category.

Concentrated System One node has all categories

and the rest nodes have no category.

It must be noted that the distributed system represents

the proposed system in which all categories have ideal

distribution. The concentrated system is equivalent to

an ordinary WBT system.

Table 1: Experimental Conditions.

Number of Nodes 8

Number of Categories

Number of Exercises/Category

Searching Frequency [times/sec]

Simulations are carried out with the time interval

of 600 seconds. Each Simulation is repeated 10 times

and the average of those is reported in Figure 4. Nat-

urally, the higher searching frequency is, the larger

searching latency is. Figure 4 shows that search-

ing latency grows rapid in the concentrated system,

while it grows slowly in the distributed system. In

other words, the result suggests that the proposed e-

Learning system has higher scalability than ordinary

concentrated WBT systems have.

5000

10000

15000

20000

25000

0 0.1 0.2 0.3 0.4 0.5 0.6

Average Searching Latency [msec]

Searching Frequency [times/sec]

Concentrated System

Distributed System

Figure 4: Comparison of concentrated and distributed sys-

tems in searching latency.

5 RELATED WORKS

A great deal of effort has been made on agent-based

systems(Wong et al., 1997; Lange and Oshima, 1998;

Tarau, 1999; Satoh, 2000). However, these tech-

nologies provide support for agent collaboration and

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communication but lack support for P2P technology.

Therefore, there are few agent-based P2P applica-

tions. PeerDB(Ng et al., 2003) is one of them, how-

ever agent technology is only used to assist query

processing while in the proposed e-Learning system

it is used not only for interactiveness but also for mi-

gration of the functionality of the system.

Edutella is P2P network for exchanging infor-

mation about learning objects(Nejdl et al., 2002).

Edutella is based on RDF(Resource Description

Framework), which is a framework for representing

information in the Web. Consequently, Edutella does

not intend to receive user’s response. In contrast, that

is one of main goal of the proposed system and it is

achieved through agent technology.

6 CONCLUSION

Since existing asynchronous WBT systems are based

on the client/server model, they have problems of

scalability and robustness. The proposed e-Learning

system solves these problems in decentralized man-

ner through both P2P technology and mobile agent

technology. The agent migrates using HTTP as trans-

fer protocol and XML as encoding format itself. The

user interface program of the proposed system is built

on Squeak so that it obtains much interactiveness

and ﬂexibility. Performance simulations suggest that

the proposed e-Learning system has higher scalability

than ordinary concentrated WBT systems have. More

work needs to be done in the evaluation of the robust-

ness of the proposed system. In addition, the expan-

sion to provide popular functions of ordinary concen-

trated WBT systems for communication between the

instructor and the learners and among the learners by

means of email and BBS (Bulletin Board System), in

decentralized manner, is left for future work.

REFERENCES

Gnutella (2000). http://welcome.to/gnutella/.

Helic, D., Krottmaier, H., Maurer, H., and Scerbakov, N.

(2003). Implementing project-based learning in wbt

systems. In World Conference on E-Learning in Cor-

porate, Government, Healthcare, and Higher Educa-

tion, pages 2189–2196.

Homma, H. and Aoki, Y. (2003). Creation of wbt server

on digital signal processing. In Proceedings of 4th

International Conference on Information Technology

Based Higher Education and Training. Marrakech,

Morocco.

Ingalls, D., Kaehler, T., Maloney, J., Wallace, S., and Kay,

A. (1997). Back to the future: The story of squeak, a

practical smalltalk written in itself. In Proceedings of

ACM Conference on Object-Oriented Programming,

Systems, Languages, and Applications, pages 318–

326.

Kawamura, T., Kinoshita, S., Sugahara, K., and Kuwatani,

T. (2003). A logic-based framework for mobile multi-

agent systems. In Proceedings of International Con-

ference on Integration of Knowledge Intensive Multi-

Agent Systems, pages 754–759. Boston, Massa-

chusetts, USA.

Lange, D. B. and Oshima, M. (1998). Programming and

Deploying Java Mobile Agents with Aglets. Addison-

Wesley.

Napster (1999). http://www.napster.com/.

Nejdl, W., Wolf, B., Qu, C., Decker, S., Sintek, M., Naeve,

A., Nilsson, M., Palm

er, M., and Risch, T. (2002).

Edutella: A p2p networking infrastructure based on

rdf. In Proceedings of the Eleventh International Con-

ference on World Wide Web, pages 604–615. ACM

Press.

Ng, W. S., Ooi, B. C., Tan, K.-L., and Zhou, A. (2003).

Peerdb: A p2p-based system for distributed data shar-

ing. In Dayal, U., Ramamritham, K., and Vijayara-

man, T. M., editors, Proceedings of the 19th Interna-

tional Conference on Data Engineering, pages 633–

644. IEEE Computer Society.

Ratnasamy, S., Francis, P., Handley, M., Karp, R., and

Shenker, S. (2001). A scalable content-addressable

network. In Proceedings of the 2001 conference on

Applications, technologies, architectures, and proto-

cols for computer communications, pages 161–172.

ACM Press.

Satoh, I. (2000). Mobilespaces: A framework for building

adaptive distributed applications using a hierarchical

mobile agent system. In Proceedings of IEEE Interna-

tional Conference on Distributed Computing Systems,

pages 161–168. IEEE Press.

Tarau, P. (1999). Inference and computation mobility with

jinni. In Apt, K., Marek, V., and Truszczynski, M.,

editors, The Logic Programming Paradigm: a 25 Year

Perspective, pages 33–48. Springer.

Winer, D. (1998). Xml-rpc speciﬁcation.

http://xmlrcp.com/spec.

Wong, D., Paciorek, N., Walsh, T., and Dicelie, J. (1997).

Concordia: An infrastructure for collaborating mo-

bile agents. In Proceedings of the First International

Workshop on Mobile Agents, volume 1219, pages 86–

97. Springer-Verlag.

P2P WEB-BASED TRAINING SYSTEM USING MOBILE AGENT TECHNOLOGIES

205